From 05c9effca005f9d087cd37a71460c06cc268aa20 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 14 Aug 2023 10:54:58 +0000 Subject: [PATCH 01/63] Bump pypa/gh-action-pypi-publish from 1.8.8 to 1.8.10 Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.8.8 to 1.8.10. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/f8c70e705ffc13c3b4d1221169b84f12a75d6ca8...b7f401de30cb6434a1e19f805ff006643653240e) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Former-commit-id: c45a5a3881cd50924861d916fbd5195149392fb3 --- .github/workflows/python-publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 4734d02f..545e3432 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -26,7 +26,7 @@ jobs: - name: Build package run: python -m build - name: Publish package - uses: pypa/gh-action-pypi-publish@f8c70e705ffc13c3b4d1221169b84f12a75d6ca8 + uses: pypa/gh-action-pypi-publish@b7f401de30cb6434a1e19f805ff006643653240e with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} \ No newline at end of file From a06da772e99325de1a92de25943e006047ecc80c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 9 Oct 2023 10:54:58 +0000 Subject: [PATCH 02/63] Bump actions/first-interaction from 1.1.1 to 1.2.0 Bumps [actions/first-interaction](https://github.com/actions/first-interaction) from 1.1.1 to 1.2.0. - [Release notes](https://github.com/actions/first-interaction/releases) - [Commits](https://github.com/actions/first-interaction/compare/v1.1.1...v1.2.0) --- updated-dependencies: - dependency-name: actions/first-interaction dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Former-commit-id: 35a1735a31211d42e0c95929447fa930f93b7900 --- .github/workflows/welcome.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/welcome.yml b/.github/workflows/welcome.yml index a993236c..eadc0b68 100644 --- a/.github/workflows/welcome.yml +++ b/.github/workflows/welcome.yml @@ -11,7 +11,7 @@ jobs: name: ๐Ÿ‘‹ Welcome runs-on: ubuntu-latest steps: - - uses: actions/first-interaction@v1.1.1 + - uses: actions/first-interaction@v1.2.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} issue-message: "Hello there, thank you for opening an Issue ! ๐Ÿ™๐Ÿป The team was notified and they will get back to you asap." From 3d96e1605bdb66dabb99592f6e7671de31d5f39a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 9 Oct 2023 10:55:06 +0000 Subject: [PATCH 03/63] Bump actions/setup-python from 2 to 4 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 2 to 4. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v2...v4) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Former-commit-id: ef1f6603a015094f9096c395687990525d0b9bd1 --- .github/workflows/code_quality_control.yml | 2 +- .github/workflows/cos_integration.yml | 2 +- .github/workflows/docs_test.yml | 2 +- .github/workflows/lints.yml | 2 +- .github/workflows/pr_request_checks.yml | 2 +- .github/workflows/testing.yml | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/code_quality_control.yml b/.github/workflows/code_quality_control.yml index 4b94b454..261a4fdc 100644 --- a/.github/workflows/code_quality_control.yml +++ b/.github/workflows/code_quality_control.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v3 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/cos_integration.yml b/.github/workflows/cos_integration.yml index abae70b2..4a20c9dd 100644 --- a/.github/workflows/cos_integration.yml +++ b/.github/workflows/cos_integration.yml @@ -13,7 +13,7 @@ jobs: uses: actions/checkout@v2 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/docs_test.yml b/.github/workflows/docs_test.yml index c7b1ce6e..b9d67583 100644 --- a/.github/workflows/docs_test.yml +++ b/.github/workflows/docs_test.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v3 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/lints.yml b/.github/workflows/lints.yml index b61e471c..dcce52c2 100644 --- a/.github/workflows/lints.yml +++ b/.github/workflows/lints.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v2 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/pr_request_checks.yml b/.github/workflows/pr_request_checks.yml index dccdb9e2..046e5bae 100644 --- a/.github/workflows/pr_request_checks.yml +++ b/.github/workflows/pr_request_checks.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v2 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index d6a40768..080bcbb6 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v2 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: 3.x From 9bc0dc14bc4a5b26dcec8de4db4d656d3fa51e34 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 9 Oct 2023 10:55:13 +0000 Subject: [PATCH 04/63] Bump actions/checkout from 2 to 4 Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 4. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v2...v4) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Former-commit-id: 70aad75fc69faeecea206b57a1b03aa597a0da79 --- .github/workflows/code_quality_control.yml | 2 +- .github/workflows/cos_integration.yml | 2 +- .github/workflows/docs.yml | 2 +- .github/workflows/docs_test.yml | 2 +- .github/workflows/lints.yml | 2 +- .github/workflows/pr_request_checks.yml | 2 +- .github/workflows/testing.yml | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/code_quality_control.yml b/.github/workflows/code_quality_control.yml index 4b94b454..89a1f48a 100644 --- a/.github/workflows/code_quality_control.yml +++ b/.github/workflows/code_quality_control.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v3 diff --git a/.github/workflows/cos_integration.yml b/.github/workflows/cos_integration.yml index abae70b2..6eed30ad 100644 --- a/.github/workflows/cos_integration.yml +++ b/.github/workflows/cos_integration.yml @@ -10,7 +10,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v2 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index a7e64a06..0f89cb4c 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -10,7 +10,7 @@ jobs: deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: 3.x diff --git a/.github/workflows/docs_test.yml b/.github/workflows/docs_test.yml index c7b1ce6e..cef7db71 100644 --- a/.github/workflows/docs_test.yml +++ b/.github/workflows/docs_test.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v3 diff --git a/.github/workflows/lints.yml b/.github/workflows/lints.yml index b61e471c..f4f4b726 100644 --- a/.github/workflows/lints.yml +++ b/.github/workflows/lints.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v3 diff --git a/.github/workflows/pr_request_checks.yml b/.github/workflows/pr_request_checks.yml index dccdb9e2..4f1e990d 100644 --- a/.github/workflows/pr_request_checks.yml +++ b/.github/workflows/pr_request_checks.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v2 diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index d6a40768..0a5cab4b 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v3 From d7effca3a6bb0234cd78289bc2896e7f7f2af600 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 11:10:49 -0400 Subject: [PATCH 05/63] HuggingfaceLLM, jina embeds Former-commit-id: 8dfb1d33d00f51c1c9389c5dd93fc14cc20a03cd --- .github/workflows/cos_integration.yml | 2 +- CONTRIBUTING.md | 4 +- flow.py | 8 +- simple_agent.py | 1 + swarms/agents/stream_response.py | 6 - swarms/embeddings/__init__.py | 2 - swarms/embeddings/embed.py | 10 - swarms/memory/chroma.py | 2 +- .../base.py => models/embeddings_base.py} | 0 swarms/models/huggingface.py | 68 +++-- swarms/models/jina_embeds.py | 214 ++++++++++++++ .../openai.py => models/openai_embeddings.py} | 2 +- swarms/{embeddings => models}/pegasus.py | 0 swarms/{embeddings => models}/simple_ada.py | 3 +- swarms/models/yarn_mistral.py | 265 ++++++++++++++++++ swarms/structs/flow.py | 11 +- swarms/structs/sequential_workflow.py | 20 ++ tests/embeddings/pegasus.py | 2 +- 18 files changed, 571 insertions(+), 49 deletions(-) delete mode 100644 swarms/agents/stream_response.py delete mode 100644 swarms/embeddings/__init__.py delete mode 100644 swarms/embeddings/embed.py rename swarms/{embeddings/base.py => models/embeddings_base.py} (100%) create mode 100644 swarms/models/jina_embeds.py rename swarms/{embeddings/openai.py => models/openai_embeddings.py} (99%) rename swarms/{embeddings => models}/pegasus.py (100%) rename swarms/{embeddings => models}/simple_ada.py (99%) create mode 100644 swarms/models/yarn_mistral.py create mode 100644 swarms/structs/sequential_workflow.py diff --git a/.github/workflows/cos_integration.yml b/.github/workflows/cos_integration.yml index 7cdb41e9..0f3fc605 100644 --- a/.github/workflows/cos_integration.yml +++ b/.github/workflows/cos_integration.yml @@ -39,4 +39,4 @@ jobs: run: sphinx-build -b linkcheck docs build/docs - name: Run performance tests - run: pytest tests/performance \ No newline at end of file + run: find ./tests -name '*.py' -exec pytest {} \; \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e00478f1..bd9090de 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -97,8 +97,8 @@ To run the documentation, install the project requirements with `poetry install You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/). ## ๐Ÿงช tests - -[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests. +- Run all the tests in the tests folder +`find ./tests -name '*.py' -exec pytest {} \;` ## ๐Ÿ“„ license diff --git a/flow.py b/flow.py index fd7a02b2..d2c21ba8 100644 --- a/flow.py +++ b/flow.py @@ -11,7 +11,11 @@ llm = OpenAIChat( ) # Initialize the flow -flow = Flow(llm=llm, max_loops=5, dashboard=True,) +flow = Flow( + llm=llm, + max_loops=5, + dashboard=True, +) flow = Flow( llm=llm, @@ -28,4 +32,4 @@ flow = Flow( out = flow.run("Generate a 10,000 word blog on health and wellness.") -print(out) \ No newline at end of file +print(out) diff --git a/simple_agent.py b/simple_agent.py index 9ec9aaf6..515b83bc 100644 --- a/simple_agent.py +++ b/simple_agent.py @@ -19,6 +19,7 @@ flow = Flow( agent = SimpleAgent( name="Optimus Prime", flow=flow, + # Memory ) out = agent.run("Generate a 10,000 word blog on health and wellness.") diff --git a/swarms/agents/stream_response.py b/swarms/agents/stream_response.py deleted file mode 100644 index ecd29ff0..00000000 --- a/swarms/agents/stream_response.py +++ /dev/null @@ -1,6 +0,0 @@ -def stream(response): - """ - Yield the response token by token (word by word) from llm - """ - for token in response.split(): - yield token diff --git a/swarms/embeddings/__init__.py b/swarms/embeddings/__init__.py deleted file mode 100644 index 2c6c13b7..00000000 --- a/swarms/embeddings/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# from swarms.embeddings.pegasus import PegasusEmbedding -from swarms.embeddings.simple_ada import get_ada_embeddings diff --git a/swarms/embeddings/embed.py b/swarms/embeddings/embed.py deleted file mode 100644 index ce50e0cf..00000000 --- a/swarms/embeddings/embed.py +++ /dev/null @@ -1,10 +0,0 @@ -# This file contains the function that embeds the input into a vector -from chromadb import EmbeddingFunction - - -def openai_embed(self, input, api_key, model_name): - openai = EmbeddingFunction.OpenAIEmbeddingFunction( - api_key=api_key, model_name=model_name - ) - embedding = openai(input) - return embedding diff --git a/swarms/memory/chroma.py b/swarms/memory/chroma.py index dc0399ef..422d0a67 100644 --- a/swarms/memory/chroma.py +++ b/swarms/memory/chroma.py @@ -17,7 +17,7 @@ from typing import ( import numpy as np from swarms.structs.document import Document -from swarms.embeddings.base import Embeddings +from swarms.models.embeddings_base import Embeddings from langchain.schema.vectorstore import VectorStore from langchain.utils import xor_args from langchain.vectorstores.utils import maximal_marginal_relevance diff --git a/swarms/embeddings/base.py b/swarms/models/embeddings_base.py similarity index 100% rename from swarms/embeddings/base.py rename to swarms/models/embeddings_base.py diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index 5b12bc76..f07edad3 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -3,6 +3,7 @@ import logging import torch from torch.nn.parallel import DistributedDataParallel as DDP from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +from termcolor import colored class HuggingfaceLLM: @@ -20,13 +21,13 @@ class HuggingfaceLLM: # Usage ``` - from finetuning_suite import Inference + from swarms.models import HuggingfaceLLM model_id = "gpt2-small" - inference = Inference(model_id=model_id) + inference = HuggingfaceLLM(model_id=model_id) - prompt_text = "Once upon a time" - generated_text = inference(prompt_text) + task = "Once upon a time" + generated_text = inference(task) print(generated_text) ``` """ @@ -42,6 +43,8 @@ class HuggingfaceLLM: # logger=None, distributed=False, decoding=False, + *args, + **kwargs, ): self.logger = logging.getLogger(__name__) self.device = ( @@ -53,7 +56,6 @@ class HuggingfaceLLM: self.distributed = distributed self.decoding = decoding self.model, self.tokenizer = None, None - # self.log = Logging() if self.distributed: assert ( @@ -104,12 +106,12 @@ class HuggingfaceLLM: self.logger.error(f"Failed to load the model or the tokenizer: {error}") raise - def run(self, prompt_text: str): + def run(self, task: str): """ Generate a response based on the prompt text. Args: - - prompt_text (str): Text to prompt the model. + - task (str): Text to prompt the model. - max_length (int): Maximum length of the response. Returns: @@ -119,10 +121,10 @@ class HuggingfaceLLM: max_length = self.max_length + self.print_dashboard(task) + try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) # self.log.start() @@ -181,12 +183,12 @@ class HuggingfaceLLM: # Wrapping synchronous calls with async return self.run(task, *args, **kwargs) - def __call__(self, prompt_text: str): + def __call__(self, task: str): """ Generate a response based on the prompt text. Args: - - prompt_text (str): Text to prompt the model. + - task (str): Text to prompt the model. - max_length (int): Maximum length of the response. Returns: @@ -194,12 +196,12 @@ class HuggingfaceLLM: """ self.load_model() - max_length = self.max_ + max_length = self.max_length + + self.print_dashboard(task) try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) # self.log.start() @@ -258,3 +260,37 @@ class HuggingfaceLLM: return {"allocated": allocated, "reserved": reserved} else: return {"error": "GPU not available"} + + def print_dashboard(self, task: str): + """Print dashboard""" + + dashboard = print( + colored( + f""" + HuggingfaceLLM Dashboard + -------------------------------------------- + Model Name: {self.model_id} + Tokenizer: {self.tokenizer} + Model MaxLength: {self.max_length} + Model Device: {self.device} + Model Quantization: {self.quantize} + Model Quantization Config: {self.quantization_config} + Model Verbose: {self.verbose} + Model Distributed: {self.distributed} + Model Decoding: {self.decoding} + + ---------------------------------------- + Metadata: + Task Memory Consumption: {self.memory_consumption()} + GPU Available: {self.gpu_available()} + ---------------------------------------- + + Task Environment: + Task: {task} + + """, + "red", + ) + ) + + print(dashboard) diff --git a/swarms/models/jina_embeds.py b/swarms/models/jina_embeds.py new file mode 100644 index 00000000..a72b8a9e --- /dev/null +++ b/swarms/models/jina_embeds.py @@ -0,0 +1,214 @@ +import logging + +import torch +from numpy.linalg import norm +from torch.nn.parallel import DistributedDataParallel as DDP +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig + + +def cos_sim(a, b): + return a @ b.T / (norm(a) * norm(b)) + + +class JinaEmbeddings: + """ + A class for running inference on a given model. + + Attributes: + model_id (str): The ID of the model. + device (str): The device to run the model on (either 'cuda' or 'cpu'). + max_length (int): The maximum length of the output sequence. + quantize (bool, optional): Whether to use quantization. Defaults to False. + quantization_config (dict, optional): The configuration for quantization. + verbose (bool, optional): Whether to print verbose logs. Defaults to False. + logger (logging.Logger, optional): The logger to use. Defaults to a basic logger. + + # Usage + ``` + from swarms.models import JinaEmbeddings + + model = JinaEmbeddings() + + embeddings = model("Encode this text") + + print(embeddings) + + + ``` + """ + + def __init__( + self, + model_id: str, + device: str = None, + max_length: int = 500, + quantize: bool = False, + quantization_config: dict = None, + verbose=False, + # logger=None, + distributed=False, + decoding=False, + cos_sim: bool = False, + *args, + **kwargs, + ): + self.logger = logging.getLogger(__name__) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) + self.model_id = model_id + self.max_length = max_length + self.verbose = verbose + self.distributed = distributed + self.decoding = decoding + self.model, self.tokenizer = None, None + # self.log = Logging() + self.cos_sim = cos_sim + + if self.distributed: + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" + + bnb_config = None + if quantize: + if not quantization_config: + quantization_config = { + "load_in_4bit": True, + "bnb_4bit_use_double_quant": True, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_compute_dtype": torch.bfloat16, + } + bnb_config = BitsAndBytesConfig(**quantization_config) + + try: + self.model = AutoModelForCausalLM.from_pretrained( + self.model_id, quantization_config=bnb_config, trust_remote_code=True + ) + + self.model # .to(self.device) + except Exception as e: + self.logger.error(f"Failed to load the model or the tokenizer: {e}") + raise + + def load_model(self): + """Load the model""" + if not self.model or not self.tokenizer: + try: + self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) + + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) + + self.model = AutoModelForCausalLM.from_pretrained( + self.model_id, + quantization_config=bnb_config, + trust_remote_code=True, + ).to(self.device) + + if self.distributed: + self.model = DDP(self.model) + except Exception as error: + self.logger.error(f"Failed to load the model or the tokenizer: {error}") + raise + + def run(self, task: str): + """ + Generate a response based on the prompt text. + + Args: + - task (str): Text to prompt the model. + - max_length (int): Maximum length of the response. + + Returns: + - Generated text (str). + """ + self.load_model() + + max_length = self.max_length + + try: + embeddings = self.model.encode([task], max_length=max_length) + + if self.cos_sim: + print(cos_sim(embeddings[0], embeddings[1])) + else: + return embeddings[0] + except Exception as e: + self.logger.error(f"Failed to generate the text: {e}") + raise + + async def run_async(self, task: str, *args, **kwargs) -> str: + """ + Run the model asynchronously + + Args: + task (str): Task to run. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Examples: + >>> mpt_instance = MPT('mosaicml/mpt-7b-storywriter', "EleutherAI/gpt-neox-20b", max_tokens=150) + >>> mpt_instance("generate", "Once upon a time in a land far, far away...") + 'Once upon a time in a land far, far away...' + >>> mpt_instance.batch_generate(["In the deep jungles,", "At the heart of the city,"], temperature=0.7) + ['In the deep jungles,', + 'At the heart of the city,'] + >>> mpt_instance.freeze_model() + >>> mpt_instance.unfreeze_model() + + """ + # Wrapping synchronous calls with async + return self.run(task, *args, **kwargs) + + def __call__(self, task: str): + """ + Generate a response based on the prompt text. + + Args: + - task (str): Text to prompt the model. + - max_length (int): Maximum length of the response. + + Returns: + - Generated text (str). + """ + self.load_model() + + max_length = self.max_length + + try: + embeddings = self.model.encode([task], max_length=max_length) + + if self.cos_sim: + print(cos_sim(embeddings[0], embeddings[1])) + else: + return embeddings[0] + except Exception as e: + self.logger.error(f"Failed to generate the text: {e}") + raise + + async def __call_async__(self, task: str, *args, **kwargs) -> str: + """Call the model asynchronously""" "" + return await self.run_async(task, *args, **kwargs) + + def save_model(self, path: str): + """Save the model to a given path""" + self.model.save_pretrained(path) + self.tokenizer.save_pretrained(path) + + def gpu_available(self) -> bool: + """Check if GPU is available""" + return torch.cuda.is_available() + + def memory_consumption(self) -> dict: + """Get the memory consumption of the GPU""" + if self.gpu_available(): + torch.cuda.synchronize() + allocated = torch.cuda.memory_allocated() + reserved = torch.cuda.memory_reserved() + return {"allocated": allocated, "reserved": reserved} + else: + return {"error": "GPU not available"} diff --git a/swarms/embeddings/openai.py b/swarms/models/openai_embeddings.py similarity index 99% rename from swarms/embeddings/openai.py rename to swarms/models/openai_embeddings.py index 230dade9..0aa3473d 100644 --- a/swarms/embeddings/openai.py +++ b/swarms/models/openai_embeddings.py @@ -25,7 +25,7 @@ from tenacity import ( stop_after_attempt, wait_exponential, ) -from swarms.embeddings.base import Embeddings +from swarms.models.embeddings_base import Embeddings def get_from_dict_or_env( diff --git a/swarms/embeddings/pegasus.py b/swarms/models/pegasus.py similarity index 100% rename from swarms/embeddings/pegasus.py rename to swarms/models/pegasus.py diff --git a/swarms/embeddings/simple_ada.py b/swarms/models/simple_ada.py similarity index 99% rename from swarms/embeddings/simple_ada.py rename to swarms/models/simple_ada.py index ba0b4cf7..7eb923b4 100644 --- a/swarms/embeddings/simple_ada.py +++ b/swarms/models/simple_ada.py @@ -1,10 +1,9 @@ import openai from dotenv import load_dotenv +from os import getenv load_dotenv() -from os import getenv - def get_ada_embeddings(text: str, model: str = "text-embedding-ada-002"): """ diff --git a/swarms/models/yarn_mistral.py b/swarms/models/yarn_mistral.py new file mode 100644 index 00000000..ebe107a2 --- /dev/null +++ b/swarms/models/yarn_mistral.py @@ -0,0 +1,265 @@ +import logging + +import torch +from torch.nn.parallel import DistributedDataParallel as DDP +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig + + +class YarnMistral128: + """ + A class for running inference on a given model. + + Attributes: + model_id (str): The ID of the model. + device (str): The device to run the model on (either 'cuda' or 'cpu'). + max_length (int): The maximum length of the output sequence. + quantize (bool, optional): Whether to use quantization. Defaults to False. + quantization_config (dict, optional): The configuration for quantization. + verbose (bool, optional): Whether to print verbose logs. Defaults to False. + logger (logging.Logger, optional): The logger to use. Defaults to a basic logger. + + # Usage + ``` + from finetuning_suite import Inference + + model_id = "gpt2-small" + inference = Inference(model_id=model_id) + + prompt_text = "Once upon a time" + generated_text = inference(prompt_text) + print(generated_text) + ``` + """ + + def __init__( + self, + model_id: str = "NousResearch/Yarn-Mistral-7b-128k", + device: str = None, + max_length: int = 500, + quantize: bool = False, + quantization_config: dict = None, + verbose=False, + # logger=None, + distributed=False, + decoding=False, + ): + self.logger = logging.getLogger(__name__) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) + self.model_id = model_id + self.max_length = max_length + self.verbose = verbose + self.distributed = distributed + self.decoding = decoding + self.model, self.tokenizer = None, None + # self.log = Logging() + + if self.distributed: + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" + + bnb_config = None + if quantize: + if not quantization_config: + quantization_config = { + "load_in_4bit": True, + "bnb_4bit_use_double_quant": True, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_compute_dtype": torch.bfloat16, + } + bnb_config = BitsAndBytesConfig(**quantization_config) + + try: + self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) + self.model = AutoModelForCausalLM.from_pretrained( + self.model_id, + quantization_config=bnb_config, + use_flash_attention_2=True, + torch_dtype=torch.bfloat16, + device_map="auto", + trust_remote_code=True, + ) + + self.model # .to(self.device) + except Exception as e: + self.logger.error(f"Failed to load the model or the tokenizer: {e}") + raise + + def load_model(self): + """Load the model""" + if not self.model or not self.tokenizer: + try: + self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) + + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) + + self.model = AutoModelForCausalLM.from_pretrained( + self.model_id, quantization_config=bnb_config + ).to(self.device) + + if self.distributed: + self.model = DDP(self.model) + except Exception as error: + self.logger.error(f"Failed to load the model or the tokenizer: {error}") + raise + + def run(self, prompt_text: str): + """ + Generate a response based on the prompt text. + + Args: + - prompt_text (str): Text to prompt the model. + - max_length (int): Maximum length of the response. + + Returns: + - Generated text (str). + """ + self.load_model() + + max_length = self.max_length + + try: + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) + + # self.log.start() + + if self.decoding: + with torch.no_grad(): + for _ in range(max_length): + output_sequence = [] + + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) + output_tokens = outputs[0][-1] + output_sequence.append(output_tokens.item()) + + # print token in real-time + print( + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), + end="", + flush=True, + ) + inputs = outputs + else: + with torch.no_grad(): + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) + + del inputs + return self.tokenizer.decode(outputs[0], skip_special_tokens=True) + except Exception as e: + self.logger.error(f"Failed to generate the text: {e}") + raise + + async def run_async(self, task: str, *args, **kwargs) -> str: + """ + Run the model asynchronously + + Args: + task (str): Task to run. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Examples: + >>> mpt_instance = MPT('mosaicml/mpt-7b-storywriter', "EleutherAI/gpt-neox-20b", max_tokens=150) + >>> mpt_instance("generate", "Once upon a time in a land far, far away...") + 'Once upon a time in a land far, far away...' + >>> mpt_instance.batch_generate(["In the deep jungles,", "At the heart of the city,"], temperature=0.7) + ['In the deep jungles,', + 'At the heart of the city,'] + >>> mpt_instance.freeze_model() + >>> mpt_instance.unfreeze_model() + + """ + # Wrapping synchronous calls with async + return self.run(task, *args, **kwargs) + + def __call__(self, prompt_text: str): + """ + Generate a response based on the prompt text. + + Args: + - prompt_text (str): Text to prompt the model. + - max_length (int): Maximum length of the response. + + Returns: + - Generated text (str). + """ + self.load_model() + + max_length = self.max_ + + try: + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) + + # self.log.start() + + if self.decoding: + with torch.no_grad(): + for _ in range(max_length): + output_sequence = [] + + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) + output_tokens = outputs[0][-1] + output_sequence.append(output_tokens.item()) + + # print token in real-time + print( + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), + end="", + flush=True, + ) + inputs = outputs + else: + with torch.no_grad(): + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) + + del inputs + + return self.tokenizer.decode(outputs[0], skip_special_tokens=True) + except Exception as e: + self.logger.error(f"Failed to generate the text: {e}") + raise + + async def __call_async__(self, task: str, *args, **kwargs) -> str: + """Call the model asynchronously""" "" + return await self.run_async(task, *args, **kwargs) + + def save_model(self, path: str): + """Save the model to a given path""" + self.model.save_pretrained(path) + self.tokenizer.save_pretrained(path) + + def gpu_available(self) -> bool: + """Check if GPU is available""" + return torch.cuda.is_available() + + def memory_consumption(self) -> dict: + """Get the memory consumption of the GPU""" + if self.gpu_available(): + torch.cuda.synchronize() + allocated = torch.cuda.memory_allocated() + reserved = torch.cuda.memory_reserved() + return {"allocated": allocated, "reserved": reserved} + else: + return {"error": "GPU not available"} diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 8d7a09ed..8601b8dd 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -1,9 +1,9 @@ """ TODO: -- Add a retry mechanism -- Add prompt injection letting the agent know it's in a flow, Flow prompt -- Dynamic temperature handling - +- Add tools +- Add open interpreter style conversation +- Add configurable save and restore so the user can restore from previus flows +- Add memory vector database retrieval """ import json @@ -252,7 +252,8 @@ class Flow: History: {response} - """, **kwargs + """, + **kwargs, ) # print(f"Next query: {response}") # break diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py new file mode 100644 index 00000000..2df95c07 --- /dev/null +++ b/swarms/structs/sequential_workflow.py @@ -0,0 +1,20 @@ +""" +Sequential Workflow + +from swarms.models import OpenAIChat, Mistral +from swarms.structs import SequentialWorkflow + + +llm = OpenAIChat(openai_api_key="") +mistral = Mistral() + +# Max loops will run over the sequential pipeline twice +workflow = SequentialWorkflow(max_loops=2) + +workflow.add("What's the weather in miami", llm) + +workflow.add("Create a report on these metrics", mistral) + +workflow.run() + +""" diff --git a/tests/embeddings/pegasus.py b/tests/embeddings/pegasus.py index d1e901dc..e9632eae 100644 --- a/tests/embeddings/pegasus.py +++ b/tests/embeddings/pegasus.py @@ -1,6 +1,6 @@ import pytest from unittest.mock import patch -from swarms.embeddings.pegasus import PegasusEmbedding +from swarms.models.pegasus import PegasusEmbedding def test_init(): From 4cfe966883e02e0b3fbae996b2d4e675e4955a53 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 11:24:13 -0400 Subject: [PATCH 06/63] hugginface Former-commit-id: fea0eeebc9a83ca53651843dd587f4a2563689fd --- swarms/models/distilled_whisperx.py | 3 + swarms/models/huggingface.py | 20 ++ swarms/models/petals.py | 2 +- tests/models/ada.py | 68 +++++++ tests/models/huggingface.py | 286 ++++++++++++++++++++++------ 5 files changed, 325 insertions(+), 54 deletions(-) create mode 100644 swarms/models/distilled_whisperx.py create mode 100644 tests/models/ada.py diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py new file mode 100644 index 00000000..2eb2788d --- /dev/null +++ b/swarms/models/distilled_whisperx.py @@ -0,0 +1,3 @@ +""" + +""" \ No newline at end of file diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index f07edad3..437d9144 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -294,3 +294,23 @@ class HuggingfaceLLM: ) print(dashboard) + + def set_device(self, device): + """ + Changes the device used for inference. + + Parameters + ---------- + device : str + The new device to use for inference. + """ + self.device = device + self.model.to(self.device) + + def set_max_length(self, max_length): + """Set max_length""" + self.max_length = max_length + + def clear_chat_history(self): + """Clear chat history""" + self.chat_history = [] diff --git a/swarms/models/petals.py b/swarms/models/petals.py index cc90cb62..189c2477 100644 --- a/swarms/models/petals.py +++ b/swarms/models/petals.py @@ -35,7 +35,7 @@ class Petals: "max_length": self.max_length, } - def generate(self, prompt): + def __call__(self, prompt): """Generate text using the Petals API.""" params = self._default_params() inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"] diff --git a/tests/models/ada.py b/tests/models/ada.py new file mode 100644 index 00000000..786b162d --- /dev/null +++ b/tests/models/ada.py @@ -0,0 +1,68 @@ +# test_embeddings.py + +import pytest +import openai +from unittest.mock import patch +from swarms.models.simple_ada import get_ada_embeddings # Adjust this import path to your project structure +from os import getenv +from dotenv import load_dotenv + +load_dotenv() + +# Fixture for test texts +@pytest.fixture +def test_texts(): + return [ + "Hello World", + "This is a test string with newline\ncharacters", + "A quick brown fox jumps over the lazy dog", + ] + +# Basic Test +def test_get_ada_embeddings_basic(test_texts): + with patch('openai.Embedding.create') as mock_create: + # Mocking the OpenAI API call + mock_create.return_value = { + "data": [ + {"embedding": [0.1, 0.2, 0.3]} + ] + } + + for text in test_texts: + embedding = get_ada_embeddings(text) + assert embedding == [0.1, 0.2, 0.3], "Embedding does not match expected output" + mock_create.assert_called_with(input=[text.replace("\n", " ")], model="text-embedding-ada-002") + +# Parameterized Test +@pytest.mark.parametrize( + "text, model, expected_call_model", + [ + ("Hello World", "text-embedding-ada-002", "text-embedding-ada-002"), + ("Hello World", "text-embedding-ada-001", "text-embedding-ada-001"), + ], +) +def test_get_ada_embeddings_models(text, model, expected_call_model): + with patch('openai.Embedding.create') as mock_create: + mock_create.return_value = { + "data": [ + {"embedding": [0.1, 0.2, 0.3]} + ] + } + + _ = get_ada_embeddings(text, model=model) + mock_create.assert_called_with(input=[text], model=expected_call_model) + +# Exception Test +def test_get_ada_embeddings_exception(): + with patch('openai.Embedding.create') as mock_create: + mock_create.side_effect = openai.error.OpenAIError("Test error") + with pytest.raises(openai.error.OpenAIError): + get_ada_embeddings("Some text") + +# Tests for environment variable loading +def test_env_var_loading(monkeypatch): + monkeypatch.setenv("OPENAI_API_KEY", "testkey123") + with patch('openai.Embedding.create'): + assert getenv("OPENAI_API_KEY") == "testkey123", "Environment variable for API key is not set correctly" + +# ... more tests to cover other aspects such as different input types, large inputs, invalid inputs, etc. diff --git a/tests/models/huggingface.py b/tests/models/huggingface.py index 46c7fa12..1bb44bed 100644 --- a/tests/models/huggingface.py +++ b/tests/models/huggingface.py @@ -1,58 +1,238 @@ -import pytest import torch -from unittest.mock import Mock, patch -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig -from swarms.models.huggingface import HuggingfaceLLM +import pytest +from unittest.mock import patch, MagicMock +from swarms.models.huggingface import HuggingfaceLLM # Replace with the actual import path +# Fixture for the class instance @pytest.fixture -def huggingface_llm(): - # Create an instance of HuggingfaceLLM for testing. +def llm_instance(): model_id = "gpt2-small" - return HuggingfaceLLM(model_id=model_id) - - -def test_initialization(huggingface_llm): - # Test the initialization of the HuggingfaceLLM class. - assert huggingface_llm.model_id == "gpt2-small" - assert huggingface_llm.device in ["cpu", "cuda"] - assert huggingface_llm.max_length == 20 - assert huggingface_llm.verbose == False - assert huggingface_llm.distributed == False - assert huggingface_llm.decoding == False - assert huggingface_llm.model is None - assert huggingface_llm.tokenizer is None - - -def test_load_model(huggingface_llm): - # Test loading the model. - huggingface_llm.load_model() - assert isinstance(huggingface_llm.model, AutoModelForCausalLM) - assert isinstance(huggingface_llm.tokenizer, AutoTokenizer) - - -def test_run(huggingface_llm): - # Test the run method of HuggingfaceLLM. - prompt_text = "Once upon a time" - generated_text = huggingface_llm.run(prompt_text) - assert isinstance(generated_text, str) - assert len(generated_text) > 0 - - -def test_call_method(huggingface_llm): - # Test the __call__ method of HuggingfaceLLM. - prompt_text = "Once upon a time" - generated_text = huggingface_llm(prompt_text) - assert isinstance(generated_text, str) - assert len(generated_text) > 0 - - -def test_load_model_failure(): - # Test loading model failure. - with patch( - "your_module.AutoModelForCausalLM.from_pretrained", - side_effect=Exception("Model load failed"), - ): - with pytest.raises(Exception): - huggingface_llm = HuggingfaceLLM(model_id="gpt2-small") - huggingface_llm.load_model() + instance = HuggingfaceLLM(model_id=model_id) + return instance + + +# Test for instantiation and attributes +def test_llm_initialization(llm_instance): + assert llm_instance.model_id == "gpt2-small" + assert llm_instance.max_length == 500 + # ... add more assertions for all default attributes + + +# Parameterized test for setting devices +@pytest.mark.parametrize("device", ["cpu", "cuda"]) +def test_llm_set_device(llm_instance, device): + llm_instance.set_device(device) + assert llm_instance.device == device + + +# Test exception during initialization with a bad model_id +def test_llm_bad_model_initialization(): + with pytest.raises(Exception): + HuggingfaceLLM(model_id="unknown-model") + + +# Mocking the tokenizer and model to test run method +@patch("swarms.models.huggingface.AutoTokenizer.from_pretrained") +@patch("swarms.models.huggingface.AutoModelForCausalLM.from_pretrained") +def test_llm_run(mock_model, mock_tokenizer, llm_instance): + mock_model.return_value.generate.return_value = "mocked output" + mock_tokenizer.return_value.encode.return_value = "mocked input" + result = llm_instance.run("test task") + assert result == "mocked output" + + +# Async test (requires pytest-asyncio plugin) +@pytest.mark.asyncio +async def test_llm_run_async(llm_instance): + result = await llm_instance.run_async("test task") + assert isinstance(result, str) + + +# Test for checking GPU availability +def test_llm_gpu_availability(llm_instance): + # Assuming the test is running on a machine where the GPU availability is known + expected_result = torch.cuda.is_available() + assert llm_instance.gpu_available() == expected_result + + +# Test for memory consumption reporting +def test_llm_memory_consumption(llm_instance): + # Mocking torch.cuda functions for consistent results + with patch("torch.cuda.memory_allocated", return_value=1024): + with patch("torch.cuda.memory_reserved", return_value=2048): + memory = llm_instance.memory_consumption() + assert memory == {"allocated": 1024, "reserved": 2048} + + +# Test different initialization parameters +@pytest.mark.parametrize("model_id, max_length", [ + ("gpt2-small", 100), + ("gpt2-medium", 200), + ("gpt2-large", None) # None to check default behavior +]) +def test_llm_initialization_params(model_id, max_length): + if max_length: + instance = HuggingfaceLLM(model_id=model_id, max_length=max_length) + assert instance.max_length == max_length + else: + instance = HuggingfaceLLM(model_id=model_id) + assert instance.max_length == 500 # Assuming 500 is the default max_length + + +# Test for setting an invalid device +def test_llm_set_invalid_device(llm_instance): + with pytest.raises(ValueError): + llm_instance.set_device("quantum_processor") + + +# Test for model download progress bar +@patch("swarms.models.huggingface.HuggingfaceLLM._download_model") +def test_llm_model_download_progress(mock_download, llm_instance): + llm_instance.download_model_with_progress() + mock_download.assert_called_once() + + +# Mocking external API call to test run method without network +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_run_without_network(mock_run, llm_instance): + mock_run.return_value = "mocked output" + result = llm_instance.run("test task without network") + assert result == "mocked output" + + +# Test handling of empty input for the run method +def test_llm_run_empty_input(llm_instance): + with pytest.raises(ValueError): + llm_instance.run("") + + +# Test the generation with a provided seed for reproducibility +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_run_with_seed(mock_run, llm_instance): + seed = 42 + llm_instance.set_seed(seed) + # Assuming set_seed method affects the randomness in the model + # You would typically ensure that setting the seed gives reproducible results + mock_run.return_value = "mocked deterministic output" + result = llm_instance.run("test task", seed=seed) + assert result == "mocked deterministic output" + + +# Test the output length is as expected +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_run_output_length(mock_run, llm_instance): + input_text = "test task" + llm_instance.max_length = 50 # set a max_length for the output + mock_run.return_value = "mocked output" * 10 # some long text + result = llm_instance.run(input_text) + assert len(result.split()) <= llm_instance.max_length + + +# Test the tokenizer handling special tokens correctly +@patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer.encode") +@patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer.decode") +def test_llm_tokenizer_special_tokens(mock_decode, mock_encode, llm_instance): + mock_encode.return_value = "encoded input with special tokens" + mock_decode.return_value = "decoded output with special tokens" + result = llm_instance.run("test task with special tokens") + mock_encode.assert_called_once() + mock_decode.assert_called_once() + assert "special tokens" in result + + +# Test for correct handling of timeouts +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_timeout_handling(mock_run, llm_instance): + mock_run.side_effect = TimeoutError + with pytest.raises(TimeoutError): + llm_instance.run("test task with timeout") + + +# Test for response time within a threshold (performance test) +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_response_time(mock_run, llm_instance): + import time + mock_run.return_value = "mocked output" + start_time = time.time() + llm_instance.run("test task for response time") + end_time = time.time() + assert end_time - start_time < 1 # Assuming the response should be faster than 1 second + + +# Test the logging of a warning for long inputs +@patch("swarms.models.huggingface.logging.warning") +def test_llm_long_input_warning(mock_warning, llm_instance): + long_input = "x" * 10000 # input longer than the typical limit + llm_instance.run(long_input) + mock_warning.assert_called_once() + + +# Test for run method behavior when model raises an exception +@patch("swarms.models.huggingface.HuggingfaceLLM._model.generate", side_effect=RuntimeError) +def test_llm_run_model_exception(mock_generate, llm_instance): + with pytest.raises(RuntimeError): + llm_instance.run("test task when model fails") + + +# Test the behavior when GPU is forced but not available +@patch("torch.cuda.is_available", return_value=False) +def test_llm_force_gpu_when_unavailable(mock_is_available, llm_instance): + with pytest.raises(EnvironmentError): + llm_instance.set_device("cuda") # Attempt to set CUDA when it's not available + + +# Test for proper cleanup after model use (releasing resources) +@patch("swarms.models.huggingface.HuggingfaceLLM._model") +@patch("swarms.models.huggingface.HuggingfaceLLM._tokenizer") +def test_llm_cleanup(mock_model, mock_tokenizer, llm_instance): + llm_instance.cleanup() + # Assuming cleanup method is meant to free resources + mock_model.delete.assert_called_once() + mock_tokenizer.delete.assert_called_once() + + +# Test updating the configuration after instantiation +def test_llm_update_configuration(llm_instance): + new_config = {"temperature": 0.7} + llm_instance.update_configuration(new_config) + assert llm_instance.configuration["temperature"] == 0.7 + + +# Test if the model is re-downloaded when changing the model_id +@patch("swarms.models.huggingface.HuggingfaceLLM._download_model") +def test_llm_change_model_id(mock_download, llm_instance): + new_model_id = "gpt2-xl" + llm_instance.model_id = new_model_id + mock_download.assert_called_with(new_model_id) + + +# Test model's ability to handle multilingual input +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_multilingual_input(mock_run, llm_instance): + mock_run.return_value = "mocked multilingual output" + multilingual_input = "Bonjour, ceci est un test multilingue." + result = llm_instance.run(multilingual_input) + assert isinstance(result, str) # Simple check to ensure output is string type + +# Test caching mechanism to prevent re-running the same inputs +@patch("swarms.models.huggingface.HuggingfaceLLM.run") +def test_llm_caching_mechanism(mock_run, llm_instance): + input_text = "test caching mechanism" + mock_run.return_value = "cached output" + # Run the input twice + first_run_result = llm_instance.run(input_text) + second_run_result = llm_instance.run(input_text) + mock_run.assert_called_once() # Should only be called once due to caching + assert first_run_result == second_run_result + + +# Ensure that model re-downloads when force_download flag is set +@patch("swarms.models.huggingface.HuggingfaceLLM._download_model") +def test_llm_force_download(mock_download, llm_instance): + llm_instance.download_model_with_progress(force_download=True) + mock_download.assert_called_once_with(llm_instance.model_id, force=True) + + +# These tests are provided as examples. In real-world scenarios, you will need to adapt these tests to the actual logic of your `HuggingfaceLLM` class. +# For instance, "mock_model.delete.assert_called_once()" and similar lines are based on hypothetical methods and behaviors that you need to replace with actual implementations. From fb3e158ee89fb8180df31ae49173ccb1e97b7dfe Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 15:44:06 -0400 Subject: [PATCH 07/63] tests Former-commit-id: 81d02a610366f0305428d2b300c4b059f34b3875 --- swarms/models/bioclip.py | 1 - tests/models/huggingface.py | 10 +++++++--- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/swarms/models/bioclip.py b/swarms/models/bioclip.py index 937634e3..318de290 100644 --- a/swarms/models/bioclip.py +++ b/swarms/models/bioclip.py @@ -42,7 +42,6 @@ Please refer to the corresponding paper, "Large-Scale Domain-Specific Pretrainin """ import open_clip -import glob import torch from PIL import Image import matplotlib.pyplot as plt diff --git a/tests/models/huggingface.py b/tests/models/huggingface.py index 1bb44bed..847ced06 100644 --- a/tests/models/huggingface.py +++ b/tests/models/huggingface.py @@ -1,7 +1,11 @@ -import torch +from unittest.mock import MagicMock, patch + import pytest -from unittest.mock import patch, MagicMock -from swarms.models.huggingface import HuggingfaceLLM # Replace with the actual import path +import torch + +from swarms.models.huggingface import ( + HuggingfaceLLM, # Replace with the actual import path +) # Fixture for the class instance From f04092ad07231f35894d378eb7ebd16307ae8927 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 15:54:11 -0400 Subject: [PATCH 08/63] flow Former-commit-id: 1162271fc628b5ddea343af480a3952fb0afd565 --- README.md | 5 ++--- swarms/structs/flow.py | 6 +++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index bea090d3..68d7ba05 100644 --- a/README.md +++ b/README.md @@ -118,14 +118,13 @@ agent.run("Create a video of a swarm of fish") --- ## Documentation - - For documentation, go here, [swarms.apac.ai](https://swarms.apac.ai) ## Contribute -We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](CONTRIBUTING.md). +We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) -# License +# License MIT diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 8601b8dd..40e00ca1 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -13,7 +13,7 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Generator from termcolor import colored import inspect import random -from swarms.tools.tool import BaseTool +# from swarms.tools.tool import BaseTool # Constants @@ -103,7 +103,7 @@ class Flow: retry_interval: int = 1, interactive: bool = False, dashboard: bool = False, - tools: List[BaseTool] = None, + # tools: List[BaseTool] = None, dynamic_temperature: bool = False, **kwargs: Any, ): @@ -121,7 +121,7 @@ class Flow: self.interactive = interactive self.dashboard = dashboard self.dynamic_temperature = dynamic_temperature - self.tools = tools + # self.tools = tools def provide_feedback(self, feedback: str) -> None: """Allow users to provide feedback on the responses.""" From 2b642fe1663bfe4dd39739b36b318a857ffc02bf Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 15:55:41 -0400 Subject: [PATCH 09/63] no stream Former-commit-id: 1b0cb87c737aa9e748f0132dd09ff9a714b4faf0 --- swarms/agents/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index 0026cdc3..f622f3f8 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -1,7 +1,7 @@ from swarms.agents.omni_modal_agent import OmniModalAgent from swarms.agents.hf_agents import HFAgent from swarms.agents.message import Message -from swarms.agents.stream_response import stream +# from swarms.agents.stream_response import stream from swarms.agents.base import AbstractAgent from swarms.agents.registry import Registry from swarms.agents.idea_to_image_agent import Idea2Image @@ -14,7 +14,6 @@ __all__ = [ "OmniModalAgent", "HFAgent", "Message", - "stream", "AbstractAgent", "Registry", "Idea2Image", From fe05f157f01311df616b75dea1901b42e47eeaa2 Mon Sep 17 00:00:00 2001 From: evelynmitchell Date: Fri, 3 Nov 2023 16:13:49 -0600 Subject: [PATCH 10/63] added labeler.yml Former-commit-id: f190d848a4cbe5a69946c5487ebfa772acf199ce --- .github/labeler.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .github/labeler.yml diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 00000000..72ccc40a --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,12 @@ +# this is a config file for the github action labeler + +# Add 'label1' to any changes within 'example' folder or any subfolders +example_change: +- example/** + +# Add 'label2' to any file changes within 'example2' folder +example2_change: example2/* + +# Add label3 to any change to .txt files within the entire repository. Quotation marks are required for the leading asterisk +text_files: +- '**/*.txt' \ No newline at end of file From 3970bf4649888424e2a14a6b63d7b6c23a7ef75b Mon Sep 17 00:00:00 2001 From: evelynmitchell Date: Fri, 3 Nov 2023 16:18:37 -0600 Subject: [PATCH 11/63] expanded permissions to allow welcome action run Former-commit-id: 4fd2eab87ee0ac3c9e4970188dcc54c814ac8282 --- .github/workflows/welcome.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/welcome.yml b/.github/workflows/welcome.yml index eadc0b68..25edc27c 100644 --- a/.github/workflows/welcome.yml +++ b/.github/workflows/welcome.yml @@ -9,6 +9,7 @@ on: jobs: build: name: ๐Ÿ‘‹ Welcome + permissions: write-all runs-on: ubuntu-latest steps: - uses: actions/first-interaction@v1.2.0 From 0598b64df6793f9e88701c0dba640f727aa76900 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 18:37:02 -0400 Subject: [PATCH 12/63] flow Former-commit-id: f53236a0708e3380abbd956d11a3aba7ad1769b8 --- flow.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/flow.py b/flow.py index d2c21ba8..1eb46ee6 100644 --- a/flow.py +++ b/flow.py @@ -10,16 +10,10 @@ llm = OpenAIChat( max_tokens=3000, ) -# Initialize the flow +## Initialize the workflow flow = Flow( llm=llm, - max_loops=5, - dashboard=True, -) - -flow = Flow( - llm=llm, - max_loops=5, + max_loops=1, dashboard=True, # stopping_condition=None, # You can define a stopping condition as needed. # loop_interval=1, From 9437cbcdfe8bf03efb553666be0e853fc11e1d5c Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 3 Nov 2023 19:57:19 -0400 Subject: [PATCH 13/63] flow example, save and load state Former-commit-id: 7d888c6a71fe8e4600458cbc03d5e649705ea30d --- flow.py | 9 +- flow_state.json | 14 ++ swarms/agents/__init__.py | 1 + swarms/models/distilled_whisperx.py | 2 +- swarms/models/huggingface.py | 2 +- swarms/structs/flow.py | 233 ++++++++++++++++++++------ swarms/structs/sequential_workflow.py | 79 +++++++++ tests/models/ada.py | 46 ++--- tests/models/huggingface.py | 23 ++- 9 files changed, 329 insertions(+), 80 deletions(-) create mode 100644 flow_state.json diff --git a/flow.py b/flow.py index 1eb46ee6..ed402a92 100644 --- a/flow.py +++ b/flow.py @@ -23,7 +23,12 @@ flow = Flow( # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. ) - +# out = flow.load_state("flow_state.json") +# temp = flow.dynamic_temperature() +# filter = flow.add_response_filter("Trump") out = flow.run("Generate a 10,000 word blog on health and wellness.") - +# out = flow.validate_response(out) +# out = flow.analyze_feedback(out) +# out = flow.print_history_and_memory() +# out = flow.save_state("flow_state.json") print(out) diff --git a/flow_state.json b/flow_state.json new file mode 100644 index 00000000..8ed134a0 --- /dev/null +++ b/flow_state.json @@ -0,0 +1,14 @@ +{ + "memory": [ + [ + "Human: Generate a 10,000 word blog on health and wellness." + ] + ], + "llm_params": {}, + "loop_interval": 1, + "retry_attempts": 3, + "retry_interval": 1, + "interactive": false, + "dashboard": true, + "dynamic_temperature": false +} \ No newline at end of file diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index f622f3f8..597c8c76 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -1,6 +1,7 @@ from swarms.agents.omni_modal_agent import OmniModalAgent from swarms.agents.hf_agents import HFAgent from swarms.agents.message import Message + # from swarms.agents.stream_response import stream from swarms.agents.base import AbstractAgent from swarms.agents.registry import Registry diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py index 2eb2788d..8062daa4 100644 --- a/swarms/models/distilled_whisperx.py +++ b/swarms/models/distilled_whisperx.py @@ -1,3 +1,3 @@ """ -""" \ No newline at end of file +""" diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index 437d9144..d18b1b9d 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -294,7 +294,7 @@ class HuggingfaceLLM: ) print(dashboard) - + def set_device(self, device): """ Changes the device used for inference. diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 40e00ca1..1d46678c 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -2,18 +2,16 @@ TODO: - Add tools - Add open interpreter style conversation -- Add configurable save and restore so the user can restore from previus flows - Add memory vector database retrieval """ import json import logging import time -from typing import Any, Callable, Dict, List, Optional, Tuple, Generator +from typing import Any, Callable, Dict, List, Optional, Tuple from termcolor import colored import inspect import random -# from swarms.tools.tool import BaseTool # Constants @@ -36,7 +34,6 @@ When you have finished the task, and you feel as if you are done: output a speci This will enable you to leave the flow loop. """ - # Custome stopping condition def stop_when_repeats(response: str) -> bool: # Stop if the word stop appears in the response @@ -209,7 +206,7 @@ class Flow: print(dashboard) - def run(self, task: str, **kwargs): + def run(self, task: str, save: bool = True, **kwargs): """ Run the autonomous agent loop @@ -223,7 +220,16 @@ class Flow: 4. If stopping condition is not met, generate a response 5. Repeat until stopping condition is met or max_loops is reached + Example: + >>> out = flow.run("Generate a 10,000 word blog on health and wellness.") + """ + # Start with a new history or continue from the last saved state + if not self.memory or not self.memory[-1]: + history = [f"Human: {task}"] + else: + history = self.memory[-1] + response = task history = [f"Human: {task}"] @@ -231,9 +237,12 @@ class Flow: if self.dashboard: self.print_dashboard(task) - for i in range(self.max_loops): + # Start or continue the loop process + for i in range(len(history), self.max_loops): print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) print("\n") + response = history[-1].split(": ", 1)[-1] # Get the last response + if self._check_stopping_condition(response) or parse_done_token(response): break @@ -245,15 +254,8 @@ class Flow: while attempt < self.retry_attempts: try: response = self.llm( - f""" - SYSTEM_PROMPT: - {FLOW_SYSTEM_PROMPT} - - - History: {response} - - """, - **kwargs, + self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) + ** kwargs, ) # print(f"Next query: {response}") # break @@ -274,6 +276,10 @@ class Flow: history.append(response) time.sleep(self.loop_interval) self.memory.append(history) + + if save: + self.save("flow_history.json") + return response # , history def _run(self, **kwargs: Any) -> str: @@ -283,32 +289,31 @@ class Flow: logging.info(f"Message history: {history}") return response - def bulk_run(self, inputs: List[Dict[str, Any]]) -> List[str]: - """Generate responses for multiple input sets.""" - return [self.run(**input_data) for input_data in inputs] - - def run_dynamically(self, task: str, max_loops: Optional[int] = None): + def agent_history_prompt( + self, + system_prompt: str = FLOW_SYSTEM_PROMPT, + history=None, + ): """ - Run the autonomous agent loop dynamically based on the - - # Usage Example + Generate the agent history prompt - # Initialize the Flow - flow = Flow(llm=lambda x: x, max_loops=5) - - # Run dynamically based on token and optional max loops - response = flow.run_dynamically("Generate a report ", max_loops=3) - print(response) + Args: + system_prompt (str): The system prompt + history (List[str]): The history of the conversation - response = flow.run_dynamically("Generate a report ") - print(response) + Returns: + str: The agent history prompt + """ + agent_history_prompt = f""" + SYSTEM_PROMPT: {system_prompt} + History: {history} """ - if "" in task: - self.stopping_condition = parse_done_token - self.max_loops = max_loops or float("inf") - response = self.run(task) - return response + return agent_history_prompt + + def bulk_run(self, inputs: List[Dict[str, Any]]) -> List[str]: + """Generate responses for multiple input sets.""" + return [self.run(**input_data) for input_data in inputs] @staticmethod def from_llm_and_template(llm: Any, template: str) -> "Flow": @@ -339,6 +344,60 @@ class Flow: return False return True + def print_history_and_memory(self): + """ + Prints the entire history and memory of the flow. + Each message is colored and formatted for better readability. + """ + print(colored("Flow History and Memory", "cyan", attrs=["bold"])) + print(colored("========================", "cyan", attrs=["bold"])) + for loop_index, history in enumerate(self.memory, start=1): + print(colored(f"\nLoop {loop_index}:", "yellow", attrs=["bold"])) + for message in history: + speaker, _, message_text = message.partition(": ") + if "Human" in speaker: + print(colored(f"{speaker}:", "green") + f" {message_text}") + else: + print(colored(f"{speaker}:", "blue") + f" {message_text}") + print(colored("------------------------", "cyan")) + print(colored("End of Flow History", "cyan", attrs=["bold"])) + + def step(self, task: str, **kwargs): + """ + + Executes a single step in the flow interaction, generating a response + from the language model based on the given input text. + + Args: + input_text (str): The input text to prompt the language model with. + + Returns: + str: The language model's generated response. + + Raises: + Exception: If an error occurs during response generation. + + """ + try: + # Generate the response using lm + response = self.llm(task, **kwargs) + + # Update the flow's history with the new interaction + if self.interactive: + self.memory.append(f"AI: {response}") + self.memory.append(f"Human: {task}") + else: + self.memory.append(f"AI: {response}") + + return response + except Exception as error: + logging.error(f"Error generating response: {error}") + raise + + def graceful_shutdown(self): + """Gracefully shutdown the system saving the state""" + return self.save_state("flow_state.json") + def run_with_timeout(self, task: str, timeout: int = 60) -> str: """Run the loop but stop if it takes longer than the timeout""" start_time = time.time() @@ -455,23 +514,97 @@ class Flow: print() return response - def streamed_token_generation(self, prompt: str) -> Generator[str, None, None]: + def get_llm_params(self): + """ + Extracts and returns the parameters of the llm object for serialization. + It assumes that the llm object has an __init__ method with parameters that can be used to recreate it. """ - Generate tokens in real-time for a given prompt. + if not hasattr(self.llm, "__init__"): + return None - This method simulates the real-time generation of each token. - For simplicity, we treat each character of the input as a token - and yield them with a slight delay. In a real-world scenario, - this would involve using the LLM's internal methods to generate - the response token by token. + init_signature = inspect.signature(self.llm.__init__) + params = init_signature.parameters + llm_params = {} + + for name, param in params.items(): + if name == "self": + continue + if hasattr(self.llm, name): + value = getattr(self.llm, name) + if isinstance( + value, (str, int, float, bool, list, dict, tuple, type(None)) + ): + llm_params[name] = value + else: + llm_params[name] = str( + value + ) # For non-serializable objects, save their string representation. + + return llm_params + + def save_state(self, file_path: str) -> None: + """ + Saves the current state of the flow to a JSON file, including the llm parameters. Args: - prompt (str): The input prompt for which the tokens should be generated. + file_path (str): The path to the JSON file where the state will be saved. - Yields: - str: The next token (character) from the generated response. + Example: + >>> flow.save_state('saved_flow.json') """ - tokens = list(prompt) - for token in tokens: - time.sleep(0.1) - yield token + state = { + "memory": self.memory, + # "llm_params": self.get_llm_params(), + "loop_interval": self.loop_interval, + "retry_attempts": self.retry_attempts, + "retry_interval": self.retry_interval, + "interactive": self.interactive, + "dashboard": self.dashboard, + "dynamic_temperature": self.dynamic_temperature, + } + + with open(file_path, "w") as f: + json.dump(state, f, indent=4) + + saved = colored("Saved flow state to", "green") + print(f"{saved} {file_path}") + + def load_state(self, file_path: str): + """ + Loads the state of the flow from a json file and restores the configuration and memory. + + + Example: + >>> flow = Flow(llm=llm_instance, max_loops=5) + >>> flow.load_state('saved_flow.json') + >>> flow.run("Continue with the task") + + """ + with open(file_path, "r") as f: + state = json.load(f) + + # Assuming 'llm_class' is a class reference to the language + # llm_params = state.get("llm_params", {}) + # self.llm = self.llm(**llm_params) + + # Restore other saved attributes + self.memory = state.get("memory", []) + self.max_loops = state.get("max_loops", 5) + self.loop_interval = state.get("loop_interval", 1) + self.retry_attempts = state.get("retry_attempts", 3) + self.retry_interval = state.get("retry_interval", 1) + self.interactive = state.get("interactive", False) + + print(f"Flow state loaded from {file_path}") + + def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1): + """Retry wrapper for LLM calls.""" + attempt = 0 + while attempt < retries: + try: + return function() + except Exception as error: + logging.error(f"Error generating response: {error}") + attempt += 1 + time.sleep(retry_delay) + raise Exception("All retry attempts failed") diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index 2df95c07..f27f3989 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -18,3 +18,82 @@ workflow.add("Create a report on these metrics", mistral) workflow.run() """ +from dataclasses import dataclass, field +from typing import List, Any, Dict, Callable, Union +from swarms.models import OpenAIChat +from swarms.structs import Flow + + +# Define a generic Task that can handle different types of callable objects +@dataclass +class Task: + description: str + model: Union[Callable, Flow] + args: List[Any] = field(default_factory=list) + kwargs: Dict[str, Any] = field(default_factory=dict) + result: Any = None + + def execute(self): + if isinstance(self.model, Flow): + self.result = self.model.run(*self.args, **self.kwargs) + else: + self.result = self.model(*self.args, **self.kwargs) + + +# SequentialWorkflow class definition using dataclasses +@dataclass +class SequentialWorkflow: + tasks: List[Task] = field(default_factory=list) + max_loops: int = 1 + + def add( + self, description: str, model: Union[Callable, Flow], *args, **kwargs + ) -> None: + self.tasks.append( + Task(description=description, model=model, args=list(args), kwargs=kwargs) + ) + + def run(self) -> None: + for _ in range(self.max_loops): + for task in self.tasks: + # Check if the current task can be executed + if task.result is None: + task.execute() + # Pass the result as an argument to the next task if it exists + next_task_index = self.tasks.index(task) + 1 + if next_task_index < len(self.tasks): + next_task = self.tasks[next_task_index] + next_task.args.insert(0, task.result) + + +# Example usage +api_key = "" # Your actual API key here + +# Initialize the language model +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize the Flow with the language model +flow1 = Flow(llm=llm, max_loops=5, dashboard=True) + +# Create another Flow for a different task +flow2 = Flow(llm=llm, max_loops=5, dashboard=True) + +# Create the workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + +# Suppose the next task takes the output of the first task as input +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") diff --git a/tests/models/ada.py b/tests/models/ada.py index 786b162d..08f1a687 100644 --- a/tests/models/ada.py +++ b/tests/models/ada.py @@ -3,12 +3,15 @@ import pytest import openai from unittest.mock import patch -from swarms.models.simple_ada import get_ada_embeddings # Adjust this import path to your project structure +from swarms.models.simple_ada import ( + get_ada_embeddings, +) # Adjust this import path to your project structure from os import getenv from dotenv import load_dotenv load_dotenv() + # Fixture for test texts @pytest.fixture def test_texts(): @@ -18,20 +21,24 @@ def test_texts(): "A quick brown fox jumps over the lazy dog", ] + # Basic Test def test_get_ada_embeddings_basic(test_texts): - with patch('openai.Embedding.create') as mock_create: + with patch("openai.Embedding.create") as mock_create: # Mocking the OpenAI API call - mock_create.return_value = { - "data": [ - {"embedding": [0.1, 0.2, 0.3]} - ] - } - + mock_create.return_value = {"data": [{"embedding": [0.1, 0.2, 0.3]}]} + for text in test_texts: embedding = get_ada_embeddings(text) - assert embedding == [0.1, 0.2, 0.3], "Embedding does not match expected output" - mock_create.assert_called_with(input=[text.replace("\n", " ")], model="text-embedding-ada-002") + assert embedding == [ + 0.1, + 0.2, + 0.3, + ], "Embedding does not match expected output" + mock_create.assert_called_with( + input=[text.replace("\n", " ")], model="text-embedding-ada-002" + ) + # Parameterized Test @pytest.mark.parametrize( @@ -42,27 +49,28 @@ def test_get_ada_embeddings_basic(test_texts): ], ) def test_get_ada_embeddings_models(text, model, expected_call_model): - with patch('openai.Embedding.create') as mock_create: - mock_create.return_value = { - "data": [ - {"embedding": [0.1, 0.2, 0.3]} - ] - } + with patch("openai.Embedding.create") as mock_create: + mock_create.return_value = {"data": [{"embedding": [0.1, 0.2, 0.3]}]} _ = get_ada_embeddings(text, model=model) mock_create.assert_called_with(input=[text], model=expected_call_model) + # Exception Test def test_get_ada_embeddings_exception(): - with patch('openai.Embedding.create') as mock_create: + with patch("openai.Embedding.create") as mock_create: mock_create.side_effect = openai.error.OpenAIError("Test error") with pytest.raises(openai.error.OpenAIError): get_ada_embeddings("Some text") + # Tests for environment variable loading def test_env_var_loading(monkeypatch): monkeypatch.setenv("OPENAI_API_KEY", "testkey123") - with patch('openai.Embedding.create'): - assert getenv("OPENAI_API_KEY") == "testkey123", "Environment variable for API key is not set correctly" + with patch("openai.Embedding.create"): + assert ( + getenv("OPENAI_API_KEY") == "testkey123" + ), "Environment variable for API key is not set correctly" + # ... more tests to cover other aspects such as different input types, large inputs, invalid inputs, etc. diff --git a/tests/models/huggingface.py b/tests/models/huggingface.py index 847ced06..71fefa67 100644 --- a/tests/models/huggingface.py +++ b/tests/models/huggingface.py @@ -70,11 +70,14 @@ def test_llm_memory_consumption(llm_instance): # Test different initialization parameters -@pytest.mark.parametrize("model_id, max_length", [ - ("gpt2-small", 100), - ("gpt2-medium", 200), - ("gpt2-large", None) # None to check default behavior -]) +@pytest.mark.parametrize( + "model_id, max_length", + [ + ("gpt2-small", 100), + ("gpt2-medium", 200), + ("gpt2-large", None), # None to check default behavior + ], +) def test_llm_initialization_params(model_id, max_length): if max_length: instance = HuggingfaceLLM(model_id=model_id, max_length=max_length) @@ -157,11 +160,14 @@ def test_llm_timeout_handling(mock_run, llm_instance): @patch("swarms.models.huggingface.HuggingfaceLLM.run") def test_llm_response_time(mock_run, llm_instance): import time + mock_run.return_value = "mocked output" start_time = time.time() llm_instance.run("test task for response time") end_time = time.time() - assert end_time - start_time < 1 # Assuming the response should be faster than 1 second + assert ( + end_time - start_time < 1 + ) # Assuming the response should be faster than 1 second # Test the logging of a warning for long inputs @@ -173,7 +179,9 @@ def test_llm_long_input_warning(mock_warning, llm_instance): # Test for run method behavior when model raises an exception -@patch("swarms.models.huggingface.HuggingfaceLLM._model.generate", side_effect=RuntimeError) +@patch( + "swarms.models.huggingface.HuggingfaceLLM._model.generate", side_effect=RuntimeError +) def test_llm_run_model_exception(mock_generate, llm_instance): with pytest.raises(RuntimeError): llm_instance.run("test task when model fails") @@ -219,6 +227,7 @@ def test_llm_multilingual_input(mock_run, llm_instance): result = llm_instance.run(multilingual_input) assert isinstance(result, str) # Simple check to ensure output is string type + # Test caching mechanism to prevent re-running the same inputs @patch("swarms.models.huggingface.HuggingfaceLLM.run") def test_llm_caching_mechanism(mock_run, llm_instance): From c7648510ffe510a39164971aa8c6009fdc3269e8 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 15:58:32 -0400 Subject: [PATCH 14/63] groupchat Former-commit-id: 154f50cc25eba4fc55866f5f7ae715acb3417f85 --- groupchat.py | 136 ++++++++------------------- swarms/structs/flow.py | 106 ++++++++++++++++++++- swarms/swarms/groupchat.py | 185 ++++++++++++++++++++----------------- 3 files changed, 241 insertions(+), 186 deletions(-) diff --git a/groupchat.py b/groupchat.py index 6694d71f..a97fbdd4 100644 --- a/groupchat.py +++ b/groupchat.py @@ -1,109 +1,49 @@ -# from swarms.structs import Flow -# from swarms.models import OpenAIChat -# from swarms.swarms.groupchat import GroupChat -# from swarms.agents import SimpleAgent +from swarms import OpenAI, Flow +from swarms.swarms.groupchat import GroupChatManager, GroupChat -# api_key = "" -# llm = OpenAIChat( -# openai_api_key=api_key, -# ) +api_key = "" -# agent1 = SimpleAgent("Captain Price", Flow(llm=llm, max_loops=4)) -# agent2 = SimpleAgent("John Mactavis", Flow(llm=llm, max_loops=4)) - -# # Create a groupchat with the 2 agents -# chat = GroupChat([agent1, agent2]) - -# # Assign duties to the agents -# chat.assign_duty(agent1.name, "Buy the groceries") -# chat.assign_duty(agent2.name, "Clean the house") - -# # Initate a chat -# response = chat.run("Captain Price", "Hello, how are you John?") -# print(response) - - -from swarms.models import OpenAIChat -from swarms.structs import Flow -import random - -api_key = "" # Your API Key here - - -class GroupChat: - """ - GroupChat class that facilitates agent-to-agent communication using multiple instances of the Flow class. - """ - - def __init__(self, agents: list): - self.agents = {f"agent_{i}": agent for i, agent in enumerate(agents)} - self.message_log = [] - - def add_agent(self, agent: Flow): - agent_id = f"agent_{len(self.agents)}" - self.agents[agent_id] = agent - - def remove_agent(self, agent_id: str): - if agent_id in self.agents: - del self.agents[agent_id] - - def send_message(self, sender_id: str, recipient_id: str, message: str): - if sender_id not in self.agents or recipient_id not in self.agents: - raise ValueError("Invalid sender or recipient ID.") - formatted_message = f"{sender_id} to {recipient_id}: {message}" - self.message_log.append(formatted_message) - recipient_agent = self.agents[recipient_id] - recipient_agent.run(message) - - def broadcast_message(self, sender_id: str, message: str): - for agent_id, agent in self.agents.items(): - if agent_id != sender_id: - self.send_message(sender_id, agent_id, message) - - def get_message_log(self): - return self.message_log - - -class EnhancedGroupChatV2(GroupChat): - def __init__(self, agents: list): - super().__init__(agents) - - def multi_round_conversation(self, rounds: int = 5): - """ - Initiate a multi-round conversation between agents. - - Args: - rounds (int): The number of rounds of conversation. - """ - for _ in range(rounds): - # Randomly select a sender and recipient agent for the conversation - sender_id = random.choice(list(self.agents.keys())) - recipient_id = random.choice(list(self.agents.keys())) - while recipient_id == sender_id: # Ensure the recipient is not the sender - recipient_id = random.choice(list(self.agents.keys())) - - # Generate a message (for simplicity, a generic message is used) - message = f"Hello {recipient_id}, how are you today?" - self.send_message(sender_id, recipient_id, message) - - -# Sample usage with EnhancedGroupChatV2 -# Initialize the language model -llm = OpenAIChat( +llm = OpenAI( openai_api_key=api_key, temperature=0.5, max_tokens=3000, ) -# Initialize two Flow agents -agent1 = Flow(llm=llm, max_loops=5, dashboard=True) -agent2 = Flow(llm=llm, max_loops=5, dashboard=True) +# Initialize the flow +flow1 = Flow( + llm=llm, + max_loops=1, + system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE", + name='silly', + dashboard=True, +) +flow2 = Flow( + llm=llm, + max_loops=1, + system_message="YOU ARE VERY SMART AND ANSWER RIDDLES", + name='detective', + dashboard=True, +) +flow3 = Flow( + llm=llm, + max_loops=1, + system_message="YOU MAKE RIDDLES", + name='riddler', + dashboard=True, +) +manager = Flow( + llm=llm, + max_loops=1, + system_message="YOU ARE A GROUP CHAT MANAGER", + name='manager', + dashboard=True, +) -# Create an enhanced group chat with the two agents -enhanced_group_chat_v2 = EnhancedGroupChatV2(agents=[agent1, agent2]) -# Simulate multi-round agent to agent communication -enhanced_group_chat_v2.multi_round_conversation(rounds=5) +# Example usage: +agents = [flow1, flow2, flow3] -enhanced_group_chat_v2.get_message_log() # Get the conversation log +group_chat = GroupChat(agents=agents, messages=[], max_round=10) +chat_manager = GroupChatManager(groupchat=group_chat, selector = manager) +chat_history = chat_manager("Write me a riddle") \ No newline at end of file diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 1d46678c..afbcf536 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -34,6 +34,7 @@ When you have finished the task, and you feel as if you are done: output a speci This will enable you to leave the flow loop. """ + # Custome stopping condition def stop_when_repeats(response: str) -> bool: # Stop if the word stop appears in the response @@ -100,6 +101,8 @@ class Flow: retry_interval: int = 1, interactive: bool = False, dashboard: bool = False, + name: str = "Flow agent", + system_message: str = FLOW_SYSTEM_PROMPT, # tools: List[BaseTool] = None, dynamic_temperature: bool = False, **kwargs: Any, @@ -119,6 +122,8 @@ class Flow: self.dashboard = dashboard self.dynamic_temperature = dynamic_temperature # self.tools = tools + self.system_message = system_message + self.name = name def provide_feedback(self, feedback: str) -> None: """Allow users to provide feedback on the responses.""" @@ -131,11 +136,6 @@ class Flow: return self.stopping_condition(response) return False - def __call__(self, prompt, **kwargs) -> str: - """Invoke the flow by providing a template and its variables.""" - response = self.llm(prompt, **kwargs) - return response - def dynamic_temperature(self): """ 1. Check the self.llm object for the temperature @@ -282,6 +282,82 @@ class Flow: return response # , history + def __call__(self, task: str, save: bool = True, **kwargs): + """ + Run the autonomous agent loop + + Args: + task (str): The initial task to run + + Flow: + 1. Generate a response + 2. Check stopping condition + 3. If stopping condition is met, stop + 4. If stopping condition is not met, generate a response + 5. Repeat until stopping condition is met or max_loops is reached + + Example: + >>> out = flow.run("Generate a 10,000 word blog on health and wellness.") + + """ + # Start with a new history or continue from the last saved state + if not self.memory or not self.memory[-1]: + history = [f"Human: {task}"] + else: + history = self.memory[-1] + + response = task + history = [f"Human: {task}"] + + # If dashboard = True then print the dashboard + if self.dashboard: + self.print_dashboard(task) + + # Start or continue the loop process + for i in range(len(history), self.max_loops): + print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) + print("\n") + response = history[-1].split(": ", 1)[-1] # Get the last response + + if self._check_stopping_condition(response) or parse_done_token(response): + break + + # Adjust temperature, comment if no work + if self.dynamic_temperature: + self.dynamic_temperature() + + attempt = 0 + while attempt < self.retry_attempts: + try: + response = self.llm( + self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) + ** kwargs, + ) + # print(f"Next query: {response}") + # break + if self.interactive: + print(f"AI: {response}") + history.append(f"AI: {response}") + response = input("You: ") + history.append(f"Human: {response}") + else: + print(f"AI: {response}") + history.append(f"AI: {response}") + print(response) + break + except Exception as e: + logging.error(f"Error generating response: {e}") + attempt += 1 + time.sleep(self.retry_interval) + history.append(response) + time.sleep(self.loop_interval) + self.memory.append(history) + + if save: + self.save_state("flow_history.json") + + return response # , history + def _run(self, **kwargs: Any) -> str: """Generate a result using the provided keyword args.""" task = self.format_prompt(**kwargs) @@ -304,6 +380,7 @@ class Flow: Returns: str: The agent history prompt """ + system_prompt = system_prompt or self.system_message agent_history_prompt = f""" SYSTEM_PROMPT: {system_prompt} @@ -608,3 +685,22 @@ class Flow: attempt += 1 time.sleep(retry_delay) raise Exception("All retry attempts failed") + + def generate_reply(self, history: str, **kwargs) -> str: + """ + Generate a response based on initial or task + """ + prompt = f""" + + SYSTEM_PROMPT: {self.system_message} + + History: {history} + + Your response: + """ + response = self.llm(prompt, **kwargs) + return {"role": self.name, "content": response} + + def update_system_message(self, system_message: str): + """Upddate the system message""" + self.system_message = system_message diff --git a/swarms/swarms/groupchat.py b/swarms/swarms/groupchat.py index 6f5f43b6..6bbe0898 100644 --- a/swarms/swarms/groupchat.py +++ b/swarms/swarms/groupchat.py @@ -1,89 +1,108 @@ -from swarms.agents import SimpleAgent -from termcolor import colored +import logging +from dataclasses import dataclass +from typing import Dict, List +from swarms.structs.flow import Flow +logger = logging.getLogger(__name__) + + +@dataclass class GroupChat: - """ - Groupchat - - Args: - agents (list): List of agents - dashboard (bool): Whether to print a dashboard or not - - Example: - >>> from swarms.structs import Flow - >>> from swarms.models import OpenAIChat - >>> from swarms.swarms.groupchat import GroupChat - >>> from swarms.agents import SimpleAgent - >>> api_key = "" - >>> llm = OpenAIChat() - >>> agent1 = SimpleAgent("Captain Price", Flow(llm=llm, max_loops=4)) - >>> agent2 = SimpleAgent("John Mactavis", Flow(llm=llm, max_loops=4)) - >>> chat = GroupChat([agent1, agent2]) - >>> chat.assign_duty(agent1.name, "Buy the groceries") - >>> chat.assign_duty(agent2.name, "Clean the house") - >>> response = chat.run("Captain Price", "Hello, how are you John?") - >>> print(response) - - - - """ - - def __init__(self, agents, dashboard: bool = False): - # Ensure that all provided agents are instances of simpleagents - if not all(isinstance(agent, SimpleAgent) for agent in agents): - raise ValueError("All agents must be instances of SimpleAgent") - self.agents = {agent.name: agent for agent in agents} - - # Dictionary to store duties for each agent - self.duties = {} - - # Dictionary to store roles for each agent - self.roles = {} - - self.dashboard = dashboard - - def assign_duty(self, agent_name, duty): - """Assigns duty to the agent""" - if agent_name not in self.agents: - raise ValueError(f"No agent named {agent_name} found.") - - def assign_role(self, agent_name, role): - """Assigns a role to the specified agent""" - if agent_name not in self.agents: - raise ValueError(f"No agent named {agent_name} found") - - self.roles[agent_name] = role - - def run(self, sender_name: str, message: str): - """Runs the groupchat""" - if self.dashboard: - metrics = print( - colored( - f""" - - Groupchat Configuration: - ------------------------ - - Agents: {self.agents} - Message: {message} - Sender: {sender_name} - """, - "red", - ) + """A group chat class that contains a list of agents and the maximum number of rounds.""" + + agents: List[Flow] + messages: List[Dict] + max_round: int = 10 + admin_name: str = "Admin" # the name of the admin agent + + @property + def agent_names(self) -> List[str]: + """Return the names of the agents in the group chat.""" + return [agent.name for agent in self.agents] + + def reset(self): + """Reset the group chat.""" + self.messages.clear() + + def agent_by_name(self, name: str) -> Flow: + """Find an agent whose name is contained within the given 'name' string.""" + for agent in self.agents: + if agent.name in name: + return agent + raise ValueError(f"No agent found with a name contained in '{name}'.") + + def next_agent(self, agent: Flow) -> Flow: + """Return the next agent in the list.""" + return self.agents[(self.agent_names.index(agent.name) + 1) % len(self.agents)] + + def select_speaker_msg(self): + """Return the message for selecting the next speaker.""" + return f""" + You are in a role play game. The following roles are available: + {self._participant_roles()}. + + Read the following conversation. + Then select the next role from {self.agent_names} to play. Only return the role. + """ + + def select_speaker(self, last_speaker: Flow, selector: Flow): + """Select the next speaker.""" + selector.update_system_message(self.select_speaker_msg()) + + # Warn if GroupChat is underpopulated, without established changing behavior + n_agents = len(self.agent_names) + if n_agents < 3: + logger.warning( + f"GroupChat is underpopulated with {n_agents} agents. Direct communication would be more efficient." ) - print(metrics) - - responses = {} - for agent_name, agent in self.agents.items(): - if agent_name != sender_name: - if agent_name in self.duties: - message += f"Your duty is {self.duties[agent_name]}" - if agent_name in self.roles: - message += ( - f"You are the {self.roles[agent_name]} in this conversation" - ) + name = selector.generate_reply( + self.format_history( + self.messages + + [ + { + "role": "system", + "content": f"Read the above conversation. Then select the next most suitable role from {self.agent_names} to play. Only return the role.", + } + ] + ) + ) + try: + return self.agent_by_name(name["content"]) + except ValueError: + return self.next_agent(last_speaker) + + def _participant_roles(self): + return "\n".join( + [f"{agent.name}: {agent.system_message}" for agent in self.agents] + ) + + def format_history(self, messages: List[Dict]) -> str: + formatted_messages = [] + for message in messages: + formatted_message = f"'{message['role']}:{message['content']}" + formatted_messages.append(formatted_message) + return "\n".join(formatted_messages) + + +class GroupChatManager: + def __init__(self, groupchat: GroupChat, selector: Flow): + self.groupchat = groupchat + self.selector = selector + + def __call__(self, task: str): + self.groupchat.messages.append({"role": self.selector.name, "content": task}) + for i in range(self.groupchat.max_round): + speaker = self.groupchat.select_speaker( + last_speaker=self.selector, selector=self.selector + ) + reply = speaker.generate_reply( + self.groupchat.format_history(self.groupchat.messages) + ) + self.groupchat.messages.append(reply) + print(reply) + if i == self.groupchat.max_round - 1: + break - responses[agent_name] = agent.run(message) - return responses + return reply From 89dffeb46c8ceb4801eb472254a5aa17e2189cb6 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 16:20:51 -0400 Subject: [PATCH 15/63] flow -> example.py Former-commit-id: 2f31a6349419f122ad36e47516e185ca19bbdc6d --- example.py | 35 +++++++++++++++++++++++------------ flow.py | 34 ---------------------------------- 2 files changed, 23 insertions(+), 46 deletions(-) delete mode 100644 flow.py diff --git a/example.py b/example.py index e9dfac18..eb750eb7 100644 --- a/example.py +++ b/example.py @@ -1,24 +1,35 @@ from swarms.models import OpenAIChat -from swarms import Worker -from swarms.prompts import PRODUCT_AGENT_PROMPT +from swarms.structs import Flow api_key = "" +# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( + # model_name="gpt-4" openai_api_key=api_key, temperature=0.5, + #max_tokens=100, ) -node = Worker( +## Initialize the workflow +flow = Flow( llm=llm, - ai_name="Optimus Prime", - openai_api_key=api_key, - ai_role=PRODUCT_AGENT_PROMPT, - external_tools=None, - human_in_the_loop=False, - temperature=0.5, + max_loops=1, + dashboard=True, + # stopping_condition=None, # You can define a stopping condition as needed. + # loop_interval=1, + # retry_attempts=3, + # retry_interval=1, + # interactive=False, # Set to 'True' for interactive mode. + # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. ) -task = "Locate 5 trending topics on healthy living, locate a website like NYTimes, and then generate an image of people doing those topics." -response = node.run(task) -print(response) +# out = flow.load_state("flow_state.json") +# temp = flow.dynamic_temperature() +# filter = flow.add_response_filter("Trump") +out = flow.run("Generate a 10,000 word blog on health and wellness.") +# out = flow.validate_response(out) +# out = flow.analyze_feedback(out) +# out = flow.print_history_and_memory() +# out = flow.save_state("flow_state.json") +print(out) diff --git a/flow.py b/flow.py deleted file mode 100644 index ed402a92..00000000 --- a/flow.py +++ /dev/null @@ -1,34 +0,0 @@ -from swarms.models import OpenAIChat -from swarms.structs import Flow - -api_key = "" - -# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC -llm = OpenAIChat( - openai_api_key=api_key, - temperature=0.5, - max_tokens=3000, -) - -## Initialize the workflow -flow = Flow( - llm=llm, - max_loops=1, - dashboard=True, - # stopping_condition=None, # You can define a stopping condition as needed. - # loop_interval=1, - # retry_attempts=3, - # retry_interval=1, - # interactive=False, # Set to 'True' for interactive mode. - # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. -) - -# out = flow.load_state("flow_state.json") -# temp = flow.dynamic_temperature() -# filter = flow.add_response_filter("Trump") -out = flow.run("Generate a 10,000 word blog on health and wellness.") -# out = flow.validate_response(out) -# out = flow.analyze_feedback(out) -# out = flow.print_history_and_memory() -# out = flow.save_state("flow_state.json") -print(out) From 80467525e2127f707899bce1d21d42425e18a1af Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 16:53:48 -0400 Subject: [PATCH 16/63] distilled whisperx Former-commit-id: 75ebbe04f8ceabb85149afac9a177c25ce699dcc --- example.py | 2 +- groupchat.py | 12 +-- swarms/models/__init__.py | 1 + swarms/models/distilled_whisperx.py | 161 +++++++++++++++++++++++++++- 4 files changed, 167 insertions(+), 9 deletions(-) diff --git a/example.py b/example.py index eb750eb7..aeae1b02 100644 --- a/example.py +++ b/example.py @@ -8,7 +8,7 @@ llm = OpenAIChat( # model_name="gpt-4" openai_api_key=api_key, temperature=0.5, - #max_tokens=100, + # max_tokens=100, ) ## Initialize the workflow diff --git a/groupchat.py b/groupchat.py index a97fbdd4..739181d1 100644 --- a/groupchat.py +++ b/groupchat.py @@ -15,28 +15,28 @@ flow1 = Flow( llm=llm, max_loops=1, system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE", - name='silly', + name="silly", dashboard=True, ) flow2 = Flow( llm=llm, max_loops=1, system_message="YOU ARE VERY SMART AND ANSWER RIDDLES", - name='detective', + name="detective", dashboard=True, ) flow3 = Flow( llm=llm, max_loops=1, system_message="YOU MAKE RIDDLES", - name='riddler', + name="riddler", dashboard=True, ) manager = Flow( llm=llm, max_loops=1, system_message="YOU ARE A GROUP CHAT MANAGER", - name='manager', + name="manager", dashboard=True, ) @@ -45,5 +45,5 @@ manager = Flow( agents = [flow1, flow2, flow3] group_chat = GroupChat(agents=agents, messages=[], max_round=10) -chat_manager = GroupChatManager(groupchat=group_chat, selector = manager) -chat_history = chat_manager("Write me a riddle") \ No newline at end of file +chat_manager = GroupChatManager(groupchat=group_chat, selector=manager) +chat_history = chat_manager("Write me a riddle") diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 328dd013..4cb61b9a 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -16,6 +16,7 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA +# from swarms.models.distilled_whisperx import DistilWhisperModel # from swarms.models.fuyu import Fuyu # Not working, wait until they update import sys diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py index 8062daa4..0a60aaac 100644 --- a/swarms/models/distilled_whisperx.py +++ b/swarms/models/distilled_whisperx.py @@ -1,3 +1,160 @@ -""" +import asyncio +import os +import time +from functools import wraps +from typing import Union -""" +import torch +from termcolor import colored +from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline + + +def async_retry(max_retries=3, exceptions=(Exception,), delay=1): + """ + A decorator for adding retry logic to async functions. + :param max_retries: Maximum number of retries before giving up. + :param exceptions: A tuple of exceptions to catch and retry on. + :param delay: Delay between retries. + """ + + def decorator(func): + @wraps(func) + async def wrapper(*args, **kwargs): + retries = max_retries + while retries: + try: + return await func(*args, **kwargs) + except exceptions as e: + retries -= 1 + if retries <= 0: + raise + print(f"Retry after exception: {e}, Attempts remaining: {retries}") + await asyncio.sleep(delay) + + return wrapper + + return decorator + + +class DistilWhisperModel: + """ + This class encapsulates the Distil-Whisper model for English speech recognition. + It allows for both synchronous and asynchronous transcription of short and long-form audio. + + Args: + model_id: The model ID to use. Defaults to "distil-whisper/distil-large-v2". + + + Attributes: + device: The device to use for inference. + torch_dtype: The torch data type to use for inference. + model_id: The model ID to use. + model: The model instance. + processor: The processor instance. + + Usage: + model_wrapper = DistilWhisperModel() + transcription = model_wrapper('path/to/audio.mp3') + + # For async usage + transcription = asyncio.run(model_wrapper.async_transcribe('path/to/audio.mp3')) + """ + + def __init__(self, model_id="distil-whisper/distil-large-v2"): + self.device = "cuda:0" if torch.cuda.is_available() else "cpu" + self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 + self.model_id = model_id + self.model = AutoModelForSpeechSeq2Seq.from_pretrained( + model_id, + torch_dtype=self.torch_dtype, + low_cpu_mem_usage=True, + use_safetensors=True, + ).to(self.device) + self.processor = AutoProcessor.from_pretrained(model_id) + + def __call__(self, inputs: Union[str, dict]): + return self.transcribe(inputs) + + def transcribe(self, inputs: Union[str, dict]): + """ + Synchronously transcribe the given audio input using the Distil-Whisper model. + :param inputs: A string representing the file path or a dict with audio data. + :return: The transcribed text. + """ + pipe = pipeline( + "automatic-speech-recognition", + model=self.model, + tokenizer=self.processor.tokenizer, + feature_extractor=self.processor.feature_extractor, + max_new_tokens=128, + torch_dtype=self.torch_dtype, + device=self.device, + ) + + return pipe(inputs)["text"] + + @async_retry() + async def async_transcribe(self, inputs: Union[str, dict]): + """ + Asynchronously transcribe the given audio input using the Distil-Whisper model. + :param inputs: A string representing the file path or a dict with audio data. + :return: The transcribed text. + """ + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self.transcribe, inputs) + + def real_time_transcribe(self, audio_file_path, chunk_duration=5): + """ + Simulates real-time transcription of an audio file, processing and printing results + in chunks with colored output for readability. + + :param audio_file_path: Path to the audio file to be transcribed. + :param chunk_duration: Duration in seconds of each audio chunk to be processed. + """ + if not os.path.isfile(audio_file_path): + print(colored("The audio file was not found.", "red")) + return + + # Assuming `chunk_duration` is in seconds and `processor` can handle chunk-wise processing + try: + with torch.no_grad(): + # Load the whole audio file, but process and transcribe it in chunks + audio_input = self.processor.audio_file_to_array(audio_file_path) + sample_rate = audio_input.sampling_rate + total_duration = len(audio_input.array) / sample_rate + chunks = [ + audio_input.array[i : i + sample_rate * chunk_duration] + for i in range( + 0, len(audio_input.array), sample_rate * chunk_duration + ) + ] + + print(colored("Starting real-time transcription...", "green")) + + for i, chunk in enumerate(chunks): + # Process the current chunk + processed_inputs = self.processor( + chunk, + sampling_rate=sample_rate, + return_tensors="pt", + padding=True, + ) + processed_inputs = processed_inputs.input_values.to(self.device) + + # Generate transcription for the chunk + logits = self.model.generate(processed_inputs) + transcription = self.processor.batch_decode( + logits, skip_special_tokens=True + )[0] + + # Print the chunk's transcription + print( + colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow") + + transcription + ) + + # Wait for the chunk's duration to simulate real-time processing + time.sleep(chunk_duration) + + except Exception as e: + print(colored(f"An error occurred during transcription: {e}", "red")) From 000fc8a131daae15a28642c4a08c90b968887196 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 17:05:06 -0400 Subject: [PATCH 17/63] tests for distilled whisperx Former-commit-id: 7e1d486a024024d3a05bfe6eefc09b36ce6a5600 --- swarms/models/__init__.py | 1 + tests/models/distilled_whisperx.py | 120 +++++++++++++++++++++++++++++ 2 files changed, 121 insertions(+) create mode 100644 tests/models/distilled_whisperx.py diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 4cb61b9a..a0bec07f 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -16,6 +16,7 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA + # from swarms.models.distilled_whisperx import DistilWhisperModel # from swarms.models.fuyu import Fuyu # Not working, wait until they update diff --git a/tests/models/distilled_whisperx.py b/tests/models/distilled_whisperx.py new file mode 100644 index 00000000..bab8cd0e --- /dev/null +++ b/tests/models/distilled_whisperx.py @@ -0,0 +1,120 @@ +# test_distilled_whisperx.py + +from unittest.mock import AsyncMock, MagicMock + +import pytest +import torch +from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor + +from swarms.models.distilled_whisperx import DistilWhisperModel, async_retry + + +# Fixtures for setting up model, processor, and audio files +@pytest.fixture(scope="module") +def model_id(): + return "distil-whisper/distil-large-v2" + + +@pytest.fixture(scope="module") +def whisper_model(model_id): + return DistilWhisperModel(model_id) + + +@pytest.fixture(scope="session") +def audio_file_path(tmp_path_factory): + # You would create a small temporary MP3 file here for testing + # or use a public domain MP3 file's path + return "path/to/valid_audio.mp3" + + +@pytest.fixture(scope="session") +def invalid_audio_file_path(): + return "path/to/invalid_audio.mp3" + + +@pytest.fixture(scope="session") +def audio_dict(): + # This should represent a valid audio dictionary as expected by the model + return {"array": torch.randn(1, 16000), "sampling_rate": 16000} + + +# Test initialization +def test_initialization(whisper_model): + assert whisper_model.model is not None + assert whisper_model.processor is not None + + +# Test successful transcription with file path +def test_transcribe_with_file_path(whisper_model, audio_file_path): + transcription = whisper_model.transcribe(audio_file_path) + assert isinstance(transcription, str) + + +# Test successful transcription with audio dict +def test_transcribe_with_audio_dict(whisper_model, audio_dict): + transcription = whisper_model.transcribe(audio_dict) + assert isinstance(transcription, str) + + +# Test for file not found error +def test_file_not_found(whisper_model, invalid_audio_file_path): + with pytest.raises(Exception): + whisper_model.transcribe(invalid_audio_file_path) + + +# Asynchronous tests +@pytest.mark.asyncio +async def test_async_transcription_success(whisper_model, audio_file_path): + transcription = await whisper_model.async_transcribe(audio_file_path) + assert isinstance(transcription, str) + + +@pytest.mark.asyncio +async def test_async_transcription_failure(whisper_model, invalid_audio_file_path): + with pytest.raises(Exception): + await whisper_model.async_transcribe(invalid_audio_file_path) + + +# Testing real-time transcription simulation +def test_real_time_transcription(whisper_model, audio_file_path, capsys): + whisper_model.real_time_transcribe(audio_file_path, chunk_duration=1) + captured = capsys.readouterr() + assert "Starting real-time transcription..." in captured.out + + +# Testing retry decorator for asynchronous function +@pytest.mark.asyncio +async def test_async_retry(): + @async_retry(max_retries=2, exceptions=(ValueError,), delay=0) + async def failing_func(): + raise ValueError("Test") + + with pytest.raises(ValueError): + await failing_func() + + +# Mocking the actual model to avoid GPU/CPU intensive operations during test +@pytest.fixture +def mocked_model(monkeypatch): + model_mock = AsyncMock(AutoModelForSpeechSeq2Seq) + processor_mock = MagicMock(AutoProcessor) + monkeypatch.setattr( + "swarms.models.distilled_whisperx.AutoModelForSpeechSeq2Seq.from_pretrained", + model_mock, + ) + monkeypatch.setattr( + "swarms.models.distilled_whisperx.AutoProcessor.from_pretrained", processor_mock + ) + return model_mock, processor_mock + + +@pytest.mark.asyncio +async def test_async_transcribe_with_mocked_model(mocked_model, audio_file_path): + model_mock, processor_mock = mocked_model + # Set up what the mock should return when it's called + model_mock.return_value.generate.return_value = torch.tensor([[0]]) + processor_mock.return_value.batch_decode.return_value = ["mocked transcription"] + model_wrapper = DistilWhisperModel() + transcription = await model_wrapper.async_transcribe(audio_file_path) + assert transcription == "mocked transcription" + From a39fdf566a4936d848e10b14cf25edf079ac215c Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 21:09:04 -0400 Subject: [PATCH 18/63] docs for DistilWhisperModel Former-commit-id: 6e6fe8dc52b5fc79c972017c2d48ea3eae1138ca --- docs/swarms/models/distilled_whisperx.md | 123 +++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 124 insertions(+) create mode 100644 docs/swarms/models/distilled_whisperx.md diff --git a/docs/swarms/models/distilled_whisperx.md b/docs/swarms/models/distilled_whisperx.md new file mode 100644 index 00000000..e9339c1e --- /dev/null +++ b/docs/swarms/models/distilled_whisperx.md @@ -0,0 +1,123 @@ +# DistilWhisperModel Documentation + +## Overview + +The `DistilWhisperModel` is a Python class designed to handle English speech recognition tasks. It leverages the capabilities of the Whisper model, which is fine-tuned for speech-to-text processes. It is designed for both synchronous and asynchronous transcription of audio inputs, offering flexibility for real-time applications or batch processing. + +## Installation + +Before you can use `DistilWhisperModel`, ensure you have the required libraries installed: + +```sh +pip3 install --upgrade swarms +``` + +## Initialization + +The `DistilWhisperModel` class is initialized with the following parameters: + +| Parameter | Type | Description | Default | +|-----------|------|-------------|---------| +| `model_id` | `str` | The identifier for the pre-trained Whisper model | `"distil-whisper/distil-large-v2"` | + +Example of initialization: + +```python +from swarms.models import DistilWhisperModel + +# Initialize with default model +model_wrapper = DistilWhisperModel() + +# Initialize with a specific model ID +model_wrapper = DistilWhisperModel(model_id='distil-whisper/distil-large-v2') +``` + +## Attributes + +After initialization, the `DistilWhisperModel` has several attributes: + +| Attribute | Type | Description | +|-----------|------|-------------| +| `device` | `str` | The device used for computation (`"cuda:0"` for GPU or `"cpu"`). | +| `torch_dtype` | `torch.dtype` | The data type used for the Torch tensors. | +| `model_id` | `str` | The model identifier string. | +| `model` | `torch.nn.Module` | The actual Whisper model loaded from the identifier. | +| `processor` | `transformers.AutoProcessor` | The processor for handling input data. | + +## Methods + +### `transcribe` + +Transcribes audio input synchronously. + +**Arguments**: + +| Argument | Type | Description | +|----------|------|-------------| +| `inputs` | `Union[str, dict]` | File path or audio data dictionary. | + +**Returns**: `str` - The transcribed text. + +**Usage Example**: + +```python +# Synchronous transcription +transcription = model_wrapper.transcribe('path/to/audio.mp3') +print(transcription) +``` + +### `async_transcribe` + +Transcribes audio input asynchronously. + +**Arguments**: + +| Argument | Type | Description | +|----------|------|-------------| +| `inputs` | `Union[str, dict]` | File path or audio data dictionary. | + +**Returns**: `Coroutine` - A coroutine that when awaited, returns the transcribed text. + +**Usage Example**: + +```python +import asyncio + +# Asynchronous transcription +transcription = asyncio.run(model_wrapper.async_transcribe('path/to/audio.mp3')) +print(transcription) +``` + +### `real_time_transcribe` + +Simulates real-time transcription of an audio file. + +**Arguments**: + +| Argument | Type | Description | +|----------|------|-------------| +| `audio_file_path` | `str` | Path to the audio file. | +| `chunk_duration` | `int` | Duration of audio chunks in seconds. | + +**Usage Example**: + +```python +# Real-time transcription simulation +model_wrapper.real_time_transcribe('path/to/audio.mp3', chunk_duration=5) +``` + +## Error Handling + +The `DistilWhisperModel` class incorporates error handling for file not found errors and generic exceptions during the transcription process. If a non-recoverable exception is raised, it is printed to the console in red to indicate failure. + +## Conclusion + +The `DistilWhisperModel` offers a convenient interface to the powerful Whisper model for speech recognition. Its design supports both batch and real-time transcription, catering to different application needs. The class's error handling and retry logic make it robust for real-world applications. + +## Additional Notes + +- Ensure you have appropriate permissions to read audio files when using file paths. +- Transcription quality depends on the audio quality and the Whisper model's performance on your dataset. +- Adjust `chunk_duration` according to the processing power of your system for real-time transcription. + +For a full list of models supported by `transformers.AutoModelForSpeechSeq2Seq`, visit the [Hugging Face Model Hub](https://huggingface.co/models). diff --git a/mkdocs.yml b/mkdocs.yml index bf155336..55c7cf3d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -106,6 +106,7 @@ nav: - Kosmos: "swarms/models/kosmos.md" - Nougat: "swarms/models/nougat.md" - LayoutLMDocumentQA: "swarms/models/layoutlm_document_qa.md" + - DistilWhisperModel: "swarms/models/distilled_whisperx.md" - swarms.structs: - Overview: "swarms/structs/overview.md" - Workflow: "swarms/structs/workflow.md" From 360666e179eb5ee83e4fec911a40357c0b809fdf Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 21:12:31 -0400 Subject: [PATCH 19/63] fuyu fix Former-commit-id: 1fb193288b7017b0ae6bf5f91adc9b492820e9e0 --- swarms/models/fuyu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index e8d16cdf..bdd3f904 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -61,6 +61,6 @@ class Fuyu: model_inputs[k] = v.to(self.device_map) output = self.model.generate( - **model_inputs, max_new_tokens=self.fmax_new_tokens + **model_inputs, max_new_tokens=self.max_new_tokens ) text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) From 79fac05df477327a4a6ba70417e8692172c148b8 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 4 Nov 2023 21:26:07 -0400 Subject: [PATCH 20/63] anthropic tests Former-commit-id: d4bd4fa4a47eaeba44a08164c0f464f3aaa24dcb --- swarms/models/anthropic.py | 24 +++++++- tests/models/anthropic.py | 116 +++++++++++++++++++++++++++++++++++++ 2 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 tests/models/anthropic.py diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 232ff647..e2066637 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -7,9 +7,31 @@ class Anthropic: Anthropic large language models. - Args: + model: The model to use. Defaults to "claude-2". + max_tokens_to_sample: The maximum number of tokens to sample. + temperature: The temperature to use for sampling. + top_k: The top_k to use for sampling. + top_p: The top_p to use for sampling. + streaming: Whether to stream the response or not. + default_request_timeout: The default request timeout to use. + + + Attributes: + model: The model to use. + max_tokens_to_sample: The maximum number of tokens to sample. + temperature: The temperature to use for sampling. + top_k: The top_k to use for sampling. + top_p: The top_p to use for sampling. + streaming: Whether to stream the response or not. + default_request_timeout: The default request timeout to use. + anthropic_api_url: The API URL to use. + anthropic_api_key: The API key to use. + Usage: + model_wrapper = Anthropic() + completion = model_wrapper("Hello, my name is") + print(completion) """ diff --git a/tests/models/anthropic.py b/tests/models/anthropic.py new file mode 100644 index 00000000..844415aa --- /dev/null +++ b/tests/models/anthropic.py @@ -0,0 +1,116 @@ +import os +import pytest +from unittest.mock import Mock, patch +from swarms.models.anthropic import Anthropic + +@pytest.fixture +def mock_anthropic_env(): + os.environ["ANTHROPIC_API_URL"] = "https://test.anthropic.com" + os.environ["ANTHROPIC_API_KEY"] = "test_api_key" + yield + del os.environ["ANTHROPIC_API_URL"] + del os.environ["ANTHROPIC_API_KEY"] + +@pytest.fixture +def mock_requests_post(): + with patch("requests.post") as mock_post: + yield mock_post + +@pytest.fixture +def anthropic_instance(): + return Anthropic(model="test-model") + +def test_anthropic_init_default_values(anthropic_instance): + assert anthropic_instance.model == "test-model" + assert anthropic_instance.max_tokens_to_sample == 256 + assert anthropic_instance.temperature is None + assert anthropic_instance.top_k is None + assert anthropic_instance.top_p is None + assert anthropic_instance.streaming is False + assert anthropic_instance.default_request_timeout == 600 + assert anthropic_instance.anthropic_api_url == "https://test.anthropic.com" + assert anthropic_instance.anthropic_api_key == "test_api_key" + +def test_anthropic_init_custom_values(): + anthropic_instance = Anthropic( + model="custom-model", + max_tokens_to_sample=128, + temperature=0.8, + top_k=5, + top_p=0.9, + streaming=True, + default_request_timeout=300, + ) + assert anthropic_instance.model == "custom-model" + assert anthropic_instance.max_tokens_to_sample == 128 + assert anthropic_instance.temperature == 0.8 + assert anthropic_instance.top_k == 5 + assert anthropic_instance.top_p == 0.9 + assert anthropic_instance.streaming is True + assert anthropic_instance.default_request_timeout == 300 + +def test_anthropic_default_params(anthropic_instance): + default_params = anthropic_instance._default_params() + assert default_params == { + "max_tokens_to_sample": 256, + "model": "test-model", + } + +def test_anthropic_run(mock_anthropic_env, mock_requests_post, anthropic_instance): + mock_response = Mock() + mock_response.json.return_value = {"completion": "Generated text"} + mock_requests_post.return_value = mock_response + + task = "Generate text" + stop = ["stop1", "stop2"] + + completion = anthropic_instance.run(task, stop) + + assert completion == "Generated text" + mock_requests_post.assert_called_once_with( + "https://test.anthropic.com/completions", + headers={"Authorization": "Bearer test_api_key"}, + json={ + "prompt": task, + "stop_sequences": stop, + "max_tokens_to_sample": 256, + "model": "test-model", + }, + timeout=600, + ) + +def test_anthropic_call(mock_anthropic_env, mock_requests_post, anthropic_instance): + mock_response = Mock() + mock_response.json.return_value = {"completion": "Generated text"} + mock_requests_post.return_value = mock_response + + task = "Generate text" + stop = ["stop1", "stop2"] + + completion = anthropic_instance(task, stop) + + assert completion == "Generated text" + mock_requests_post.assert_called_once_with( + "https://test.anthropic.com/completions", + headers={"Authorization": "Bearer test_api_key"}, + json={ + "prompt": task, + "stop_sequences": stop, + "max_tokens_to_sample": 256, + "model": "test-model", + }, + timeout=600, + ) + +def test_anthropic_exception_handling(mock_anthropic_env, mock_requests_post, anthropic_instance): + mock_response = Mock() + mock_response.json.return_value = {"error": "An error occurred"} + mock_requests_post.return_value = mock_response + + task = "Generate text" + stop = ["stop1", "stop2"] + + with pytest.raises(Exception) as excinfo: + anthropic_instance(task, stop) + + assert "An error occurred" in str(excinfo.value) From 0ef45c36f64459d97ec8839d4ddb57b5b648a11e Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 5 Nov 2023 10:37:10 -0500 Subject: [PATCH 21/63] auto saved + fixed run method of flow Former-commit-id: ba28f40e579861e8e1bb524f15c2866599b66d7d --- demos/positive_med.py | 4 +-- example.py | 8 ++--- flow_state.json | 14 -------- swarms/models/fuyu.py | 4 +-- swarms/structs/flow.py | 52 ++++++++++++++++-------------- tests/models/anthropic.py | 13 +++++++- tests/models/distilled_whisperx.py | 1 - 7 files changed, 47 insertions(+), 49 deletions(-) delete mode 100644 flow_state.json diff --git a/demos/positive_med.py b/demos/positive_med.py index e8f879c9..2d191c55 100644 --- a/demos/positive_med.py +++ b/demos/positive_med.py @@ -23,7 +23,7 @@ Distribution Agent: """ -from swarms import OpenAIChat +from swarms.models import OpenAIChat from termcolor import colored TOPIC_GENERATOR = f""" @@ -264,7 +264,7 @@ Denote the social media's by using the social media name in HTML like tags {{ARTICLE}} """ -llm = OpenAIChat(openai_api_key="") +llm = OpenAIChat(openai_api_key="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") def get_review_prompt(article): diff --git a/example.py b/example.py index aeae1b02..3af9fc57 100644 --- a/example.py +++ b/example.py @@ -1,7 +1,7 @@ from swarms.models import OpenAIChat from swarms.structs import Flow -api_key = "" +api_key = "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" # Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( @@ -14,7 +14,7 @@ llm = OpenAIChat( ## Initialize the workflow flow = Flow( llm=llm, - max_loops=1, + max_loops=2, dashboard=True, # stopping_condition=None, # You can define a stopping condition as needed. # loop_interval=1, @@ -31,5 +31,5 @@ out = flow.run("Generate a 10,000 word blog on health and wellness.") # out = flow.validate_response(out) # out = flow.analyze_feedback(out) # out = flow.print_history_and_memory() -# out = flow.save_state("flow_state.json") -print(out) +# # out = flow.save_state("flow_state.json") +# print(out) diff --git a/flow_state.json b/flow_state.json deleted file mode 100644 index 8ed134a0..00000000 --- a/flow_state.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "memory": [ - [ - "Human: Generate a 10,000 word blog on health and wellness." - ] - ], - "llm_params": {}, - "loop_interval": 1, - "retry_attempts": 3, - "retry_interval": 1, - "interactive": false, - "dashboard": true, - "dynamic_temperature": false -} \ No newline at end of file diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index bdd3f904..0fd1fd85 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -60,7 +60,5 @@ class Fuyu: for k, v in model_inputs.items(): model_inputs[k] = v.to(self.device_map) - output = self.model.generate( - **model_inputs, max_new_tokens=self.max_new_tokens - ) + output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens) text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index afbcf536..d40e4fb4 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -105,6 +105,8 @@ class Flow: system_message: str = FLOW_SYSTEM_PROMPT, # tools: List[BaseTool] = None, dynamic_temperature: bool = False, + saved_state: Optional[str] = None, + autosave: bool = False, **kwargs: Any, ): self.llm = llm @@ -124,6 +126,9 @@ class Flow: # self.tools = tools self.system_message = system_message self.name = name + self.saved_state = saved_state + self.autosave = autosave + self.response_filters = [] def provide_feedback(self, feedback: str) -> None: """Allow users to provide feedback on the responses.""" @@ -206,7 +211,7 @@ class Flow: print(dashboard) - def run(self, task: str, save: bool = True, **kwargs): + def run(self, task: str, **kwargs): """ Run the autonomous agent loop @@ -220,15 +225,15 @@ class Flow: 4. If stopping condition is not met, generate a response 5. Repeat until stopping condition is met or max_loops is reached - Example: - >>> out = flow.run("Generate a 10,000 word blog on health and wellness.") - """ - # Start with a new history or continue from the last saved state - if not self.memory or not self.memory[-1]: - history = [f"Human: {task}"] - else: - history = self.memory[-1] + # Restore from saved state if provided, ortherwise start with a new history + # if self.saved_state: + # self.load_state(self.saved_state) + # history = self.memory[-1] + # print(f"Loaded state from {self.saved_state}") + # else: + # history = [f"Human: {task}"] + # self.memory.append(history) response = task history = [f"Human: {task}"] @@ -237,12 +242,9 @@ class Flow: if self.dashboard: self.print_dashboard(task) - # Start or continue the loop process - for i in range(len(history), self.max_loops): + for i in range(self.max_loops): print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) print("\n") - response = history[-1].split(": ", 1)[-1] # Get the last response - if self._check_stopping_condition(response) or parse_done_token(response): break @@ -254,8 +256,8 @@ class Flow: while attempt < self.retry_attempts: try: response = self.llm( - self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) - ** kwargs, + self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response), + **kwargs, ) # print(f"Next query: {response}") # break @@ -277,8 +279,8 @@ class Flow: time.sleep(self.loop_interval) self.memory.append(history) - if save: - self.save("flow_history.json") + # if self.autosave: + # self.save_state("flow_state.json") return response # , history @@ -353,8 +355,8 @@ class Flow: time.sleep(self.loop_interval) self.memory.append(history) - if save: - self.save_state("flow_history.json") + # if save: + # self.save_state("flow_history.json") return response # , history @@ -409,7 +411,13 @@ class Flow: json.dump(self.memory, f) print(f"Saved flow history to {file_path}") - def load(self, file_path) -> None: + def load(self, file_path: str): + """ + Load the flow history from a file. + + Args: + file_path (str): The path to the file containing the saved flow history. + """ with open(file_path, "r") as f: self.memory = json.load(f) print(f"Loaded flow history from {file_path}") @@ -660,10 +668,6 @@ class Flow: with open(file_path, "r") as f: state = json.load(f) - # Assuming 'llm_class' is a class reference to the language - # llm_params = state.get("llm_params", {}) - # self.llm = self.llm(**llm_params) - # Restore other saved attributes self.memory = state.get("memory", []) self.max_loops = state.get("max_loops", 5) diff --git a/tests/models/anthropic.py b/tests/models/anthropic.py index 844415aa..4dbd365d 100644 --- a/tests/models/anthropic.py +++ b/tests/models/anthropic.py @@ -3,6 +3,7 @@ import pytest from unittest.mock import Mock, patch from swarms.models.anthropic import Anthropic + @pytest.fixture def mock_anthropic_env(): os.environ["ANTHROPIC_API_URL"] = "https://test.anthropic.com" @@ -11,15 +12,18 @@ def mock_anthropic_env(): del os.environ["ANTHROPIC_API_URL"] del os.environ["ANTHROPIC_API_KEY"] + @pytest.fixture def mock_requests_post(): with patch("requests.post") as mock_post: yield mock_post + @pytest.fixture def anthropic_instance(): return Anthropic(model="test-model") + def test_anthropic_init_default_values(anthropic_instance): assert anthropic_instance.model == "test-model" assert anthropic_instance.max_tokens_to_sample == 256 @@ -31,6 +35,7 @@ def test_anthropic_init_default_values(anthropic_instance): assert anthropic_instance.anthropic_api_url == "https://test.anthropic.com" assert anthropic_instance.anthropic_api_key == "test_api_key" + def test_anthropic_init_custom_values(): anthropic_instance = Anthropic( model="custom-model", @@ -49,6 +54,7 @@ def test_anthropic_init_custom_values(): assert anthropic_instance.streaming is True assert anthropic_instance.default_request_timeout == 300 + def test_anthropic_default_params(anthropic_instance): default_params = anthropic_instance._default_params() assert default_params == { @@ -56,6 +62,7 @@ def test_anthropic_default_params(anthropic_instance): "model": "test-model", } + def test_anthropic_run(mock_anthropic_env, mock_requests_post, anthropic_instance): mock_response = Mock() mock_response.json.return_value = {"completion": "Generated text"} @@ -79,6 +86,7 @@ def test_anthropic_run(mock_anthropic_env, mock_requests_post, anthropic_instanc timeout=600, ) + def test_anthropic_call(mock_anthropic_env, mock_requests_post, anthropic_instance): mock_response = Mock() mock_response.json.return_value = {"completion": "Generated text"} @@ -102,7 +110,10 @@ def test_anthropic_call(mock_anthropic_env, mock_requests_post, anthropic_instan timeout=600, ) -def test_anthropic_exception_handling(mock_anthropic_env, mock_requests_post, anthropic_instance): + +def test_anthropic_exception_handling( + mock_anthropic_env, mock_requests_post, anthropic_instance +): mock_response = Mock() mock_response.json.return_value = {"error": "An error occurred"} mock_requests_post.return_value = mock_response diff --git a/tests/models/distilled_whisperx.py b/tests/models/distilled_whisperx.py index bab8cd0e..4bdd10f3 100644 --- a/tests/models/distilled_whisperx.py +++ b/tests/models/distilled_whisperx.py @@ -117,4 +117,3 @@ async def test_async_transcribe_with_mocked_model(mocked_model, audio_file_path) model_wrapper = DistilWhisperModel() transcription = await model_wrapper.async_transcribe(audio_file_path) assert transcription == "mocked transcription" - From 70a20ad7a78a16ccf0d90ae7d754c0230013bd0c Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 5 Nov 2023 21:46:28 -0500 Subject: [PATCH 22/63] sequential workflow tests, prorotytpe with documentation Former-commit-id: 310230a417c7ae78dea19b5d11d3a20033c9993f --- demos/positive_med.py | 2 +- docs/swarms/structs/sequential_workflow.md | 577 +++++++++++++++++++++ example.py | 2 +- sequential_workflow_example.py | 37 ++ swarms/structs/flow.py | 94 +++- swarms/structs/sequential_workflow.py | 419 ++++++++++++--- swarms/swarms/autobloggen.py | 0 tests/structs/sequential_workflow.py | 306 +++++++++++ 8 files changed, 1348 insertions(+), 89 deletions(-) create mode 100644 docs/swarms/structs/sequential_workflow.md create mode 100644 sequential_workflow_example.py create mode 100644 swarms/swarms/autobloggen.py create mode 100644 tests/structs/sequential_workflow.py diff --git a/demos/positive_med.py b/demos/positive_med.py index 2d191c55..88226545 100644 --- a/demos/positive_med.py +++ b/demos/positive_med.py @@ -264,7 +264,7 @@ Denote the social media's by using the social media name in HTML like tags {{ARTICLE}} """ -llm = OpenAIChat(openai_api_key="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") +llm = OpenAIChat(openai_api_key="") def get_review_prompt(article): diff --git a/docs/swarms/structs/sequential_workflow.md b/docs/swarms/structs/sequential_workflow.md new file mode 100644 index 00000000..04587b89 --- /dev/null +++ b/docs/swarms/structs/sequential_workflow.md @@ -0,0 +1,577 @@ +# `SequentialWorkflow` Documentation + +The **SequentialWorkflow** class is a Python module designed to facilitate the execution of a sequence of tasks in a sequential manner. It is a part of the `swarms.structs` package and is particularly useful for orchestrating the execution of various callable objects, such as functions or models, in a predefined order. This documentation will provide an in-depth understanding of the **SequentialWorkflow** class, including its purpose, architecture, usage, and examples. + +## Purpose and Relevance + +The **SequentialWorkflow** class is essential for managing and executing a series of tasks or processes, where each task may depend on the outcome of the previous one. It is commonly used in various application scenarios, including but not limited to: + +1. **Natural Language Processing (NLP) Workflows:** In NLP workflows, multiple language models are employed sequentially to process and generate text. Each model may depend on the results of the previous one, making sequential execution crucial. + +2. **Data Analysis Pipelines:** Data analysis often involves a series of tasks such as data preprocessing, transformation, and modeling steps. These tasks must be performed sequentially to ensure data consistency and accuracy. + +3. **Task Automation:** In task automation scenarios, there is a need to execute a series of automated tasks in a specific order. Sequential execution ensures that each task is performed in a predefined sequence, maintaining the workflow's integrity. + +By providing a structured approach to managing these tasks, the **SequentialWorkflow** class helps developers streamline their workflow execution and improve code maintainability. + +## Key Concepts and Terminology + +Before delving into the details of the **SequentialWorkflow** class, let's define some key concepts and terminology that will be used throughout the documentation: + +### Task + +A **task** refers to a specific unit of work that needs to be executed as part of the workflow. Each task is associated with a description and can be implemented as a callable object, such as a function or a model. + +### Flow + +A **flow** represents a callable object that can be a task within the **SequentialWorkflow**. Flows encapsulate the logic and functionality of a particular task. Flows can be functions, models, or any callable object that can be executed. + +### Sequential Execution + +Sequential execution refers to the process of running tasks one after the other in a predefined order. In a **SequentialWorkflow**, tasks are executed sequentially, meaning that each task starts only after the previous one has completed. + +### Workflow + +A **workflow** is a predefined sequence of tasks that need to be executed in a specific order. It represents the overall process or pipeline that the **SequentialWorkflow** manages. + +### Dashboard (Optional) + +A **dashboard** is an optional feature of the **SequentialWorkflow** that provides real-time monitoring and visualization of the workflow's progress. It displays information such as the current task being executed, task results, and other relevant metadata. + +### Max Loops + +The **maximum number of times** the entire workflow can be run. This parameter allows developers to control how many times the workflow is executed. + +### Autosaving + +**Autosaving** is a feature that allows the **SequentialWorkflow** to automatically save its state to a file at specified intervals. This feature helps in resuming a workflow from where it left off, even after interruptions. + +Now that we have a clear understanding of the key concepts and terminology, let's explore the architecture and usage of the **SequentialWorkflow** class in more detail. + +## Architecture of SequentialWorkflow + +The architecture of the **SequentialWorkflow** class is designed to provide a structured and flexible way to define, manage, and execute a sequence of tasks. It comprises the following core components: + +1. **Task**: The **Task** class represents an individual unit of work within the workflow. Each task has a description, which serves as a human-readable identifier for the task. Tasks can be implemented as callable objects, allowing for great flexibility in defining their functionality. + +2. **Workflow**: The **SequentialWorkflow** class itself represents the workflow. It manages a list of tasks in the order they should be executed. Workflows can be run sequentially or asynchronously, depending on the use case. + +3. **Task Execution**: Task execution is the process of running each task in the workflow. Tasks are executed one after another in the order they were added to the workflow. Task results can be passed as inputs to subsequent tasks. + +4. **Dashboard (Optional)**: The **SequentialWorkflow** optionally includes a dashboard feature. The dashboard provides a visual interface for monitoring the progress of the workflow. It displays information about the current task, task results, and other relevant metadata. + +5. **State Management**: The **SequentialWorkflow** supports state management, allowing developers to save and load the state of the workflow to and from JSON files. This feature is valuable for resuming workflows after interruptions or for sharing workflow configurations. + +## Usage of SequentialWorkflow + +The **SequentialWorkflow** class is versatile and can be employed in a wide range of applications. Its usage typically involves the following steps: + +1. **Initialization**: Begin by initializing any callable objects or flows that will serve as tasks in the workflow. These callable objects can include functions, models, or any other Python objects that can be executed. + +2. **Workflow Creation**: Create an instance of the **SequentialWorkflow** class. Specify the maximum number of loops the workflow should run and whether a dashboard should be displayed. + +3. **Task Addition**: Add tasks to the workflow using the `add` method. Each task should be described using a human-readable description, and the associated flow (callable object) should be provided. Additional arguments and keyword arguments can be passed to the task. + +4. **Task Execution**: Execute the workflow using the `run` method. The tasks within the workflow will be executed sequentially, with task results passed as inputs to subsequent tasks. + +5. **Accessing Results**: After running the workflow, you can access the results of each task using the `get_task_results` method or by directly accessing the `result` attribute of each task. + +6. **Optional Features**: Optionally, you can enable features such as autosaving of the workflow state and utilize the dashboard for real-time monitoring. + + +## Installation + +Before using the Sequential Workflow library, you need to install it. You can install it via pip: + +```bash +pip3 install --upgrade swarms +``` + +## Quick Start + +Let's begin with a quick example to demonstrate how to create and run a Sequential Workflow. In this example, we'll create a workflow that generates a 10,000-word blog on "health and wellness" using an AI model and then summarizes the generated content. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Initialize the language model flow (e.g., GPT-3) +llm = OpenAIChat( + openai_api_key="YOUR_API_KEY", + temperature=0.5, + max_tokens=3000, +) + +# Initialize flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") +``` + +This quick example demonstrates the basic usage of the Sequential Workflow. It creates two tasks and executes them sequentially. + +## Class: `Task` + +### Description + +The `Task` class represents an individual task in the workflow. A task is essentially a callable object, such as a function or a class, that can be executed sequentially. Tasks can have arguments and keyword arguments. + +### Class Definition + +```python +class Task: + def __init__(self, description: str, flow: Union[Callable, Flow], args: List[Any] = [], kwargs: Dict[str, Any] = {}, result: Any = None, history: List[Any] = []) +``` + +### Parameters + +- `description` (str): A description of the task. +- `flow` (Union[Callable, Flow]): The callable object representing the task. It can be a function, class, or a `Flow` instance. +- `args` (List[Any]): A list of positional arguments to pass to the task when executed. Default is an empty list. +- `kwargs` (Dict[str, Any]): A dictionary of keyword arguments to pass to the task when executed. Default is an empty dictionary. +- `result` (Any): The result of the task's execution. Default is `None`. +- `history` (List[Any]): A list to store the historical results of the task. Default is an empty list. + +### Methods + +#### `execute()` + +Execute the task. + +```python +def execute(self): +``` + +This method executes the task and updates the `result` and `history` attributes of the task. It checks if the task is a `Flow` instance and if the 'task' argument is needed. + +## Class: `SequentialWorkflow` + +### Description + +The `SequentialWorkflow` class is responsible for managing a sequence of tasks and executing them in a sequential order. It provides methods for adding tasks, running the workflow, and managing the state of the tasks. + +### Class Definition + +```python +class SequentialWorkflow: + def __init__(self, max_loops: int = 1, autosave: bool = False, saved_state_filepath: Optional[str] = "sequential_workflow_state.json", restore_state_filepath: Optional[str] = None, dashboard: bool = False, tasks: List[Task] = []) +``` + +### Parameters + +- `max_loops` (int): The maximum number of times to run the workflow sequentially. Default is `1`. +- `autosave` (bool): Whether to enable autosaving of the workflow state. Default is `False`. +- `saved_state_filepath` (Optional[str]): The file path to save the workflow state when autosave is enabled. Default is `"sequential_workflow_state.json"`. +- `restore_state_filepath` (Optional[str]): The file path to restore the workflow state when initializing. Default is `None`. +- `dashboard` (bool): Whether to display a dashboard with workflow information. Default is `False`. +- `tasks` (List[Task]): A list of `Task` instances representing the tasks in the workflow. Default is an empty list. + +### Methods + +#### `add(task: str, flow: Union[Callable, Flow], *args, **kwargs)` + +Add a task to the workflow. + +```python +def add(self, task: str, flow: Union[Callable, Flow], *args, **kwargs) -> None: +``` + +This method adds a new task to the workflow. You can provide a description of the task, the callable object (function, class, or `Flow` instance), and any additional positional or keyword arguments required for the task. + +#### `reset_workflow()` + +Reset the workflow by clearing the results of each task. + +```python +def reset_workflow(self) -> None: +``` + +This method clears the results of each task in the workflow, allowing you to start fresh without reinitializing the workflow. + +#### `get_task_results()` + +Get the results of each task in the workflow. + +```python +def get_task_results(self) -> Dict[str, Any]: +``` + +This method returns a dictionary containing the results of each task in the workflow, where the keys are task descriptions, and the values are the corresponding results. + +#### `remove_task(task_description: str)` + +Remove a task from the workflow. + +```python +def remove_task(self, task_description: str) -> None: +``` + +This method removes a specific task from the workflow based on its description. + +#### `update_task(task_description: str, **updates)` + +Update the arguments of a task in the workflow. + +```python +def update_task(self, task_description: str, **updates) -> None: +``` + +This method allows you to update the arguments and keyword arguments of a task in the workflow. You specify the task's description and provide the updates as keyword arguments. + +#### `save_workflow_state(filepath: Optional[str] = "sequential_workflow_state.json", **kwargs)` + +Save the workflow state to a JSON file. + +```python +def save_workflow_state(self, filepath: Optional[str] = "sequential_workflow_state.json", **kwargs) -> None: +``` + +This method saves the current state of the workflow, including the results and history of each task, to a JSON file. You can specify the file path for saving the state. + +#### `load_workflow_state(filepath: str = None, **kwargs)` + +Load the workflow state from a JSON file and restore the workflow state. + +```python +def load_workflow_state(self, filepath: str = None, **kwargs) -> None: +``` + +This method loads a previously saved workflow state from a JSON file + + and restores the state, allowing you to continue the workflow from where it was saved. You can specify the file path for loading the state. + +#### `run()` + +Run the workflow sequentially. + +```python +def run(self) -> None: +``` + +This method executes the tasks in the workflow sequentially. It checks if a task is a `Flow` instance and handles the flow of data between tasks accordingly. + +#### `arun()` + +Asynchronously run the workflow. + +```python +async def arun(self) -> None: +``` + +This method asynchronously executes the tasks in the workflow sequentially. It's suitable for use cases where asynchronous execution is required. It also handles data flow between tasks. + +#### `workflow_bootup(**kwargs)` + +Display a bootup message for the workflow. + +```python +def workflow_bootup(self, **kwargs) -> None: +``` + +This method displays a bootup message when the workflow is initialized. You can customize the message by providing additional keyword arguments. + +#### `workflow_dashboard(**kwargs)` + +Display a dashboard for the workflow. + +```python +def workflow_dashboard(self, **kwargs) -> None: +``` + +This method displays a dashboard with information about the workflow, such as the number of tasks, maximum loops, and autosave settings. You can customize the dashboard by providing additional keyword arguments. + +## Examples + +Let's explore some examples to illustrate how to use the Sequential Workflow library effectively. + +Sure, I'll recreate the usage examples section for each method and use case using the provided foundation. Here are the examples: + +### Example 1: Adding Tasks to a Sequential Workflow + +In this example, we'll create a Sequential Workflow and add tasks to it. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Output the list of tasks in the workflow +print("Tasks in the workflow:") +for task in workflow.tasks: + print(f"Task: {task.description}") +``` + +In this example, we create a Sequential Workflow and add two tasks to it. + +### Example 2: Resetting a Sequential Workflow + +In this example, we'll create a Sequential Workflow, add tasks to it, and then reset it. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Reset the workflow +workflow.reset_workflow() + +# Output the list of tasks in the workflow after resetting +print("Tasks in the workflow after resetting:") +for task in workflow.tasks: + print(f"Task: {task.description}") +``` + +In this example, we create a Sequential Workflow, add two tasks to it, and then reset the workflow, clearing all task results. + +### Example 3: Getting Task Results from a Sequential Workflow + +In this example, we'll create a Sequential Workflow, add tasks to it, run the workflow, and then retrieve the results of each task. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Get and display the results of each task in the workflow +results = workflow.get_task_results() +for task_description, result in results.items(): + print(f"Task: {task_description}, Result: {result}") +``` + +In this example, we create a Sequential Workflow, add two tasks to it, run the workflow, and then retrieve and display the results of each task. + +### Example 4: Removing a Task from a Sequential Workflow + +In this example, we'll create a Sequential Workflow, add tasks to it, and then remove a specific task from the workflow. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Remove a specific task from the workflow +workflow.remove_task("Generate a 10,000 word blog on health and wellness.") + +# Output the list of tasks in the workflow after removal +print("Tasks in the workflow after removing a task:") +for task in workflow.tasks: + print(f"Task: {task.description}") +``` + +In this example, we create a Sequential Workflow, add two tasks to it, and then remove a specific task from the workflow. + +### Example 5: Updating Task Arguments in a Sequential Workflow + +In this example, we'll create a Sequential Workflow, add tasks to it, and then update the arguments of a specific task in the workflow. + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) +workflow.add("Summarize the generated blog", flow2) + +# Update the arguments of a specific task in the workflow +workflow.update_task("Generate a 10,000 word blog on health and wellness.", max_loops=2) + +# Output the list of tasks in the workflow after updating task arguments +print("Tasks in the workflow after updating task arguments:") +for task in workflow.tasks: + print(f"Task: {task.description}, Arguments: { + +task.arguments}") +``` + +In this example, we create a Sequential Workflow, add two tasks to it, and then update the arguments of a specific task in the workflow. + +These examples demonstrate various operations and use cases for working with a Sequential Workflow. + +# Why `SequentialWorkflow`? + +## Enhancing Autonomous Agent Development + +The development of autonomous agents, whether they are conversational AI, robotic systems, or any other AI-driven application, often involves complex workflows that require a sequence of tasks to be executed in a specific order. Managing and orchestrating these tasks efficiently is crucial for building reliable and effective agents. The Sequential Workflow module serves as a valuable tool for AI engineers in achieving this goal. + +## Reliability and Coordination + +One of the primary challenges in autonomous agent development is ensuring that tasks are executed in the correct sequence and that the results of one task can be used as inputs for subsequent tasks. The Sequential Workflow module simplifies this process by allowing AI engineers to define and manage workflows in a structured and organized manner. + +By using the Sequential Workflow module, AI engineers can achieve the following benefits: + +### 1. Improved Reliability + +Reliability is a critical aspect of autonomous agents. The ability to handle errors gracefully and recover from failures is essential for building robust systems. The Sequential Workflow module offers a systematic approach to task execution, making it easier to handle errors, retry failed tasks, and ensure that the agent continues to operate smoothly. + +### 2. Task Coordination + +Coordinating tasks in the correct order is essential for achieving the desired outcome. The Sequential Workflow module enforces task sequencing, ensuring that each task is executed only when its dependencies are satisfied. This eliminates the risk of executing tasks out of order, which can lead to incorrect results. + +### 3. Code Organization + +Managing complex workflows can become challenging without proper organization. The Sequential Workflow module encourages AI engineers to structure their code in a modular and maintainable way. Each task can be encapsulated as a separate unit, making it easier to understand, modify, and extend the agent's behavior. + +### 4. Workflow Visualization + +Visualization is a powerful tool for understanding and debugging workflows. The Sequential Workflow module can be extended to include a visualization dashboard, allowing AI engineers to monitor the progress of tasks, track results, and identify bottlenecks or performance issues. + +## TODO: Future Features + +While the Sequential Workflow module offers significant advantages, there are opportunities for further enhancement. Here is a list of potential features and improvements that can be added to make it even more versatile and adaptable for various AI engineering tasks: + +### 1. Asynchronous Support + +Adding support for asynchronous task execution can improve the efficiency of workflows, especially when dealing with tasks that involve waiting for external events or resources. + +### 2. Context Managers + +Introducing context manager support for tasks can simplify resource management, such as opening and closing files, database connections, or network connections within a task's context. + +### 3. Workflow History + +Maintaining a detailed history of workflow execution, including timestamps, task durations, and input/output data, can facilitate debugging and performance analysis. + +### 4. Parallel Processing + +Enhancing the module to support parallel processing with a pool of workers can significantly speed up the execution of tasks, especially for computationally intensive workflows. + +### 5. Error Handling Strategies + +Providing built-in error handling strategies, such as retries, fallbacks, and custom error handling functions, can make the module more robust in handling unexpected failures. + +## Conclusion + +The Sequential Workflow module is a valuable tool for AI engineers working on autonomous agents and complex AI-driven applications. It offers a structured and reliable approach to defining and executing workflows, ensuring that tasks are performed in the correct sequence. By using this module, AI engineers can enhance the reliability, coordination, and maintainability of their agents. + +As the field of AI continues to evolve, the demand for efficient workflow management tools will only increase. The Sequential Workflow module is a step towards meeting these demands and empowering AI engineers to create more reliable and capable autonomous agents. With future enhancements and features, it has the potential to become an indispensable asset in the AI engineer's toolkit. + +In summary, the Sequential Workflow module provides a foundation for orchestrating complex tasks and workflows, enabling AI engineers to focus on designing intelligent agents that can perform tasks with precision and reliability. \ No newline at end of file diff --git a/example.py b/example.py index 3af9fc57..8e34cce3 100644 --- a/example.py +++ b/example.py @@ -1,7 +1,7 @@ from swarms.models import OpenAIChat from swarms.structs import Flow -api_key = "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" +api_key = "" # Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( diff --git a/sequential_workflow_example.py b/sequential_workflow_example.py new file mode 100644 index 00000000..b9ab8196 --- /dev/null +++ b/sequential_workflow_example.py @@ -0,0 +1,37 @@ +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize the Flow with the language flow +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create another Flow for a different task +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + +# Suppose the next task takes the output of the first task as input +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index d40e4fb4..0f129314 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -1,8 +1,14 @@ """ TODO: +- add a method that scrapes all the methods from the llm object and outputs them as a string - Add tools - Add open interpreter style conversation - Add memory vector database retrieval +- add batch processing +- add async processing for run and batch run +- add plan module +- concurrent +- """ import json @@ -14,8 +20,15 @@ import inspect import random +# Prompts +DYNAMIC_STOP_PROMPT = """ +When you have finished the task from the Human, output a special token: +This will enable you to leave the autonomous loop. +""" + + # Constants -FLOW_SYSTEM_PROMPT = """ +FLOW_SYSTEM_PROMPT = f""" You are an autonomous agent granted autonomy from a Flow structure. Your role is to engage in multi-step conversations with your self or the user, generate long-form content like blogs, screenplays, or SOPs, @@ -23,19 +36,15 @@ and accomplish tasks. You can have internal dialogues with yourself or can inter to aid in these complex tasks. Your responses should be coherent, contextually relevant, and tailored to the task at hand. -When you have finished the task, and you feel as if you are done: output a special token: -This will enable you to leave the flow loop. +{DYNAMIC_STOP_PROMPT} """ -DYNAMIC_STOP_PROMPT = """ -When you have finished the task, and you feel as if you are done: output a special token: -This will enable you to leave the flow loop. -""" +# Utility functions -# Custome stopping condition +# Custom stopping condition def stop_when_repeats(response: str) -> bool: # Stop if the word stop appears in the response return "Stop" in response.lower() @@ -182,6 +191,7 @@ class Flow: def print_dashboard(self, task: str): """Print dashboard""" model_config = self.get_llm_init_params() + print(colored("Initializing Agent Dashboard...", "yellow")) dashboard = print( colored( @@ -195,6 +205,8 @@ class Flow: ---------------------------------------- Flow Configuration: + Name: {self.name} + System Prompt: {self.system_message} Task: {task} Max Loops: {self.max_loops} Stopping Condition: {self.stopping_condition} @@ -202,14 +214,35 @@ class Flow: Retry Attempts: {self.retry_attempts} Retry Interval: {self.retry_interval} Interactive: {self.interactive} - + Dashboard: {self.dashboard} + Dynamic Temperature: {self.dynamic_temperature} + Autosave: {self.autosave} + Saved State: {self.saved_state} + ---------------------------------------- """, "green", ) ) - print(dashboard) + # print(dashboard) + + def activate_autonomous_agent(self): + """Print the autonomous agent activation message""" + try: + print(colored("Initializing Autonomous Agent...", "yellow")) + # print(colored("Loading modules...", "yellow")) + # print(colored("Modules loaded successfully.", "green")) + print(colored("Autonomous Agent Activated.", "cyan", attrs=["bold"])) + print(colored("All systems operational. Executing task...", "green")) + except Exception as error: + print( + colored( + "Error activating autonomous agent. Try optimizing your parameters...", + "red", + ) + ) + print(error) def run(self, task: str, **kwargs): """ @@ -235,6 +268,11 @@ class Flow: # history = [f"Human: {task}"] # self.memory.append(history) + # print(colored(">>> Autonomous Agent Activated", "cyan", attrs=["bold"])) + self.activate_autonomous_agent() + + # if self.autosave: + response = task history = [f"Human: {task}"] @@ -284,7 +322,10 @@ class Flow: return response # , history - def __call__(self, task: str, save: bool = True, **kwargs): + async def arun(self, task: str, **kwargs): + """Async run""" + pass + """ Run the autonomous agent loop @@ -298,15 +339,17 @@ class Flow: 4. If stopping condition is not met, generate a response 5. Repeat until stopping condition is met or max_loops is reached - Example: - >>> out = flow.run("Generate a 10,000 word blog on health and wellness.") - """ - # Start with a new history or continue from the last saved state - if not self.memory or not self.memory[-1]: - history = [f"Human: {task}"] - else: - history = self.memory[-1] + # Restore from saved state if provided, ortherwise start with a new history + # if self.saved_state: + # self.load_state(self.saved_state) + # history = self.memory[-1] + # print(f"Loaded state from {self.saved_state}") + # else: + # history = [f"Human: {task}"] + # self.memory.append(history) + + print(colored(">>> Autonomous Agent Activated", "cyan", attrs=["bold"])) response = task history = [f"Human: {task}"] @@ -315,12 +358,9 @@ class Flow: if self.dashboard: self.print_dashboard(task) - # Start or continue the loop process - for i in range(len(history), self.max_loops): + for i in range(self.max_loops): print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) print("\n") - response = history[-1].split(": ", 1)[-1] # Get the last response - if self._check_stopping_condition(response) or parse_done_token(response): break @@ -332,8 +372,8 @@ class Flow: while attempt < self.retry_attempts: try: response = self.llm( - self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) - ** kwargs, + self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response), + **kwargs, ) # print(f"Next query: {response}") # break @@ -355,8 +395,8 @@ class Flow: time.sleep(self.loop_interval) self.memory.append(history) - # if save: - # self.save_state("flow_history.json") + # if self.autosave: + # self.save_state("flow_state.json") return response # , history diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index f27f3989..c89175f2 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -1,99 +1,398 @@ """ -Sequential Workflow +TODO: +- Add a method to update the arguments of a task +- Add a method to get the results of each task +- Add a method to get the results of a specific task +- Add a method to get the results of the workflow +- Add a method to get the results of the workflow as a dataframe -from swarms.models import OpenAIChat, Mistral -from swarms.structs import SequentialWorkflow +- Add a method to run the workflow in parallel with a pool of workers and a queue and a dashboard +- Add a dashboard to visualize the workflow +- Add async support +- Add context manager support +- Add workflow history +""" +import json +from dataclasses import dataclass, field +from typing import Any, Callable, Dict, List, Optional, Union +from termcolor import colored +from pydantic import BaseModel, validator -llm = OpenAIChat(openai_api_key="") -mistral = Mistral() +from swarms.structs.flow import Flow -# Max loops will run over the sequential pipeline twice -workflow = SequentialWorkflow(max_loops=2) -workflow.add("What's the weather in miami", llm) +# Define a generic Task that can handle different types of callable objects +@dataclass +class Task: + """ + Task class for running a task in a sequential workflow. -workflow.add("Create a report on these metrics", mistral) -workflow.run() + Examples: + >>> from swarms.structs import Task, Flow + >>> from swarms.models import OpenAIChat + >>> flow = Flow(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False) + >>> task = Task(description="What's the weather in miami", flow=flow) + >>> task.execute() + >>> task.result -""" -from dataclasses import dataclass, field -from typing import List, Any, Dict, Callable, Union -from swarms.models import OpenAIChat -from swarms.structs import Flow -# Define a generic Task that can handle different types of callable objects -@dataclass -class Task: + """ + description: str - model: Union[Callable, Flow] + flow: Union[Callable, Flow] args: List[Any] = field(default_factory=list) kwargs: Dict[str, Any] = field(default_factory=dict) result: Any = None + history: List[Any] = field(default_factory=list) def execute(self): - if isinstance(self.model, Flow): - self.result = self.model.run(*self.args, **self.kwargs) + """ + Execute the task. + + Raises: + ValueError: If a Flow instance is used as a task and the 'task' argument is not provided. + + + + """ + if isinstance(self.flow, Flow): + # Add a prompt to notify the Flow of the sequential workflow + if "prompt" in self.kwargs: + self.kwargs["prompt"] += ( + f"\n\nPrevious output: {self.result}" if self.result else "" + ) + else: + self.kwargs["prompt"] = f"Main task: {self.description}" + ( + f"\n\nPrevious output: {self.result}" if self.result else "" + ) + self.result = self.flow.run(*self.args, **self.kwargs) else: - self.result = self.model(*self.args, **self.kwargs) + self.result = self.flow(*self.args, **self.kwargs) + + self.history.append(self.result) # SequentialWorkflow class definition using dataclasses @dataclass class SequentialWorkflow: + """ + SequentialWorkflow class for running a sequence of tasks using N number of autonomous agents. + + Args: + max_loops (int): The maximum number of times to run the workflow. + dashboard (bool): Whether to display the dashboard for the workflow. + + + Attributes: + tasks (List[Task]): The list of tasks to execute. + max_loops (int): The maximum number of times to run the workflow. + dashboard (bool): Whether to display the dashboard for the workflow. + + + Examples: + >>> from swarms.models import OpenAIChat + >>> from swarms.structs import SequentialWorkflow + >>> llm = OpenAIChat(openai_api_key="") + >>> workflow = SequentialWorkflow(max_loops=1) + >>> workflow.add("What's the weather in miami", llm) + >>> workflow.add("Create a report on these metrics", llm) + >>> workflow.run() + >>> workflow.tasks + + """ + tasks: List[Task] = field(default_factory=list) max_loops: int = 1 + autosave: bool = False + saved_state_filepath: Optional[str] = "sequential_workflow_state.json" + restore_state_filepath: Optional[str] = None + dashboard: bool = False - def add( - self, description: str, model: Union[Callable, Flow], *args, **kwargs - ) -> None: + def add(self, task: str, flow: Union[Callable, Flow], *args, **kwargs) -> None: + """ + Add a task to the workflow. + + Args: + task (str): The task description or the initial input for the Flow. + flow (Union[Callable, Flow]): The model or flow to execute the task. + *args: Additional arguments to pass to the task execution. + **kwargs: Additional keyword arguments to pass to the task execution. + """ + # If the flow is a Flow instance, we include the task in kwargs for Flow.run() + if isinstance(flow, Flow): + kwargs["task"] = task # Set the task as a keyword argument for Flow + + # Append the task to the tasks list self.tasks.append( - Task(description=description, model=model, args=list(args), kwargs=kwargs) + Task(description=task, flow=flow, args=list(args), kwargs=kwargs) ) - def run(self) -> None: - for _ in range(self.max_loops): - for task in self.tasks: - # Check if the current task can be executed - if task.result is None: - task.execute() - # Pass the result as an argument to the next task if it exists - next_task_index = self.tasks.index(task) + 1 - if next_task_index < len(self.tasks): - next_task = self.tasks[next_task_index] - next_task.args.insert(0, task.result) + def reset_workflow(self) -> None: + """Resets the workflow by clearing the results of each task.""" + for task in self.tasks: + task.result = None + def get_task_results(self) -> Dict[str, Any]: + """ + Returns the results of each task in the workflow. -# Example usage -api_key = "" # Your actual API key here + Returns: + Dict[str, Any]: The results of each task in the workflow + """ + return {task.description: task.result for task in self.tasks} -# Initialize the language model -llm = OpenAIChat( - openai_api_key=api_key, - temperature=0.5, - max_tokens=3000, -) + def remove_task(self, task_description: str) -> None: + self.tasks = [ + task for task in self.tasks if task.description != task_description + ] -# Initialize the Flow with the language model -flow1 = Flow(llm=llm, max_loops=5, dashboard=True) + def update_task(self, task_description: str, **updates) -> None: + """ + Updates the arguments of a task in the workflow. -# Create another Flow for a different task -flow2 = Flow(llm=llm, max_loops=5, dashboard=True) + Args: + task_description (str): The description of the task to update. + **updates: The updates to apply to the task. -# Create the workflow -workflow = SequentialWorkflow(max_loops=1) + Raises: + ValueError: If the task is not found in the workflow. -# Add tasks to the workflow -workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + Examples: + >>> from swarms.models import OpenAIChat + >>> from swarms.structs import SequentialWorkflow + >>> llm = OpenAIChat(openai_api_key="") + >>> workflow = SequentialWorkflow(max_loops=1) + >>> workflow.add("What's the weather in miami", llm) + >>> workflow.add("Create a report on these metrics", llm) + >>> workflow.update_task("What's the weather in miami", max_tokens=1000) + >>> workflow.tasks[0].kwargs + {'max_tokens': 1000} -# Suppose the next task takes the output of the first task as input -workflow.add("Summarize the generated blog", flow2) + """ + for task in self.tasks: + if task.description == task_description: + task.kwargs.update(updates) + break + else: + raise ValueError(f"Task {task_description} not found in workflow.") -# Run the workflow -workflow.run() + def save_workflow_state( + self, filepath: Optional[str] = "sequential_workflow_state.json", **kwargs + ) -> None: + """ + Saves the workflow state to a json file. + + Args: + filepath (str): The path to save the workflow state to. + + Examples: + >>> from swarms.models import OpenAIChat + >>> from swarms.structs import SequentialWorkflow + >>> llm = OpenAIChat(openai_api_key="") + >>> workflow = SequentialWorkflow(max_loops=1) + >>> workflow.add("What's the weather in miami", llm) + >>> workflow.add("Create a report on these metrics", llm) + >>> workflow.save_workflow_state("sequential_workflow_state.json") + """ + filepath = filepath or self.saved_state_filepath + + with open(filepath, "w") as f: + # Saving the state as a json for simplicuty + state = { + "tasks": [ + { + "description": task.description, + "args": task.args, + "kwargs": task.kwargs, + "result": task.result, + "history": task.history, + } + for task in self.tasks + ], + "max_loops": self.max_loops, + } + json.dump(state, f, indent=4) + + def workflow_bootup(self, **kwargs) -> None: + bootup = print( + colored( + f""" + Sequential Workflow Initializing...""", + "green", + attrs=["bold", "underline"], + ) + ) + + def workflow_dashboard(self, **kwargs) -> None: + """ + Displays a dashboard for the workflow. + + Args: + **kwargs: Additional keyword arguments to pass to the dashboard. + + Examples: + >>> from swarms.models import OpenAIChat + >>> from swarms.structs import SequentialWorkflow + >>> llm = OpenAIChat(openai_api_key="") + >>> workflow = SequentialWorkflow(max_loops=1) + >>> workflow.add("What's the weather in miami", llm) + >>> workflow.add("Create a report on these metrics", llm) + >>> workflow.workflow_dashboard() + + """ + dashboard = print( + colored( + f""" + Sequential Workflow Dashboard + -------------------------------- + Tasks: {len(self.tasks)} + Max Loops: {self.max_loops} + Autosave: {self.autosave} + Autosave Filepath: {self.saved_state_filepath} + Restore Filepath: {self.restore_state_filepath} + -------------------------------- + Metadata: + kwargs: {kwargs} + + + + + """, + "cyan", + attrs=["bold", "underline"], + ) + ) + + def load_workflow_state(self, filepath: str = None, **kwargs) -> None: + """ + Loads the workflow state from a json file and restores the workflow state. + + Args: + filepath (str): The path to load the workflow state from. + + Examples: + >>> from swarms.models import OpenAIChat + >>> from swarms.structs import SequentialWorkflow + >>> llm = OpenAIChat(openai_api_key="") + >>> workflow = SequentialWorkflow(max_loops=1) + >>> workflow.add("What's the weather in miami", llm) + >>> workflow.add("Create a report on these metrics", llm) + >>> workflow.save_workflow_state("sequential_workflow_state.json") + >>> workflow.load_workflow_state("sequential_workflow_state.json") + + """ + filepath = filepath or self.restore_state_filepath + + with open(filepath, "r") as f: + state = json.load(f) + self.max_loops = state["max_loops"] + self.tasks = [] + for task_state in state["tasks"]: + task = Task( + description=task_state["description"], + flow=task_state["flow"], + args=task_state["args"], + kwargs=task_state["kwargs"], + result=task_state["result"], + history=task_state["history"], + ) + self.tasks.append(task) + + def run(self) -> None: + """ + Run the workflow. + + Raises: + ValueError: If a Flow instance is used as a task and the 'task' argument is not provided. + + """ + try: + self.workflow_bootup() + for _ in range(self.max_loops): + for task in self.tasks: + # Check if the current task can be executed + if task.result is None: + # Check if the flow is a Flow and a 'task' argument is needed + if isinstance(task.flow, Flow): + # Ensure that 'task' is provided in the kwargs + if "task" not in task.kwargs: + raise ValueError( + f"The 'task' argument is required for the Flow flow execution in '{task.description}'" + ) + # Separate the 'task' argument from other kwargs + flow_task_arg = task.kwargs.pop("task") + task.result = task.flow.run( + flow_task_arg, *task.args, **task.kwargs + ) + else: + # If it's not a Flow instance, call the flow directly + task.result = task.flow(*task.args, **task.kwargs) + + # Pass the result as an argument to the next task if it exists + next_task_index = self.tasks.index(task) + 1 + if next_task_index < len(self.tasks): + next_task = self.tasks[next_task_index] + if isinstance(next_task.flow, Flow): + # For Flow flows, 'task' should be a keyword argument + next_task.kwargs["task"] = task.result + else: + # For other callable flows, the result is added to args + next_task.args.insert(0, task.result) + + # Autosave the workflow state + if self.autosave: + self.save_workflow_state("sequential_workflow_state.json") + except Exception as e: + print( + colored( + f"Error initializing the Sequential workflow: {e} try optimizing your inputs like the flow class and task description", + "red", + attrs=["bold", "underline"], + ) + ) + + async def arun(self) -> None: + """ + Asynchronously run the workflow. + + Raises: + ValueError: If a Flow instance is used as a task and the 'task' argument is not provided. + + """ + for _ in range(self.max_loops): + for task in self.tasks: + # Check if the current task can be executed + if task.result is None: + # Check if the flow is a Flow and a 'task' argument is needed + if isinstance(task.flow, Flow): + # Ensure that 'task' is provided in the kwargs + if "task" not in task.kwargs: + raise ValueError( + f"The 'task' argument is required for the Flow flow execution in '{task.description}'" + ) + # Separate the 'task' argument from other kwargs + flow_task_arg = task.kwargs.pop("task") + task.result = await task.flow.arun( + flow_task_arg, *task.args, **task.kwargs + ) + else: + # If it's not a Flow instance, call the flow directly + task.result = await task.flow(*task.args, **task.kwargs) + + # Pass the result as an argument to the next task if it exists + next_task_index = self.tasks.index(task) + 1 + if next_task_index < len(self.tasks): + next_task = self.tasks[next_task_index] + if isinstance(next_task.flow, Flow): + # For Flow flows, 'task' should be a keyword argument + next_task.kwargs["task"] = task.result + else: + # For other callable flows, the result is added to args + next_task.args.insert(0, task.result) -# Output the results -for task in workflow.tasks: - print(f"Task: {task.description}, Result: {task.result}") + # Autosave the workflow state + if self.autosave: + self.save_workflow_state("sequential_workflow_state.json") diff --git a/swarms/swarms/autobloggen.py b/swarms/swarms/autobloggen.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/structs/sequential_workflow.py b/tests/structs/sequential_workflow.py new file mode 100644 index 00000000..64b51f28 --- /dev/null +++ b/tests/structs/sequential_workflow.py @@ -0,0 +1,306 @@ +import asyncio +import os +from unittest.mock import patch + +import pytest + +from swarms.models import OpenAIChat +from swarms.structs.flow import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow, Task + +# Mock the OpenAI API key using environment variables +os.environ["OPENAI_API_KEY"] = "mocked_api_key" + + + +# Mock OpenAIChat class for testing +class MockOpenAIChat: + def __init__(self, *args, **kwargs): + pass + + def run(self, *args, **kwargs): + return "Mocked result" + +# Mock Flow class for testing +class MockFlow: + def __init__(self, *args, **kwargs): + pass + + def run(self, *args, **kwargs): + return "Mocked result" + +# Mock SequentialWorkflow class for testing +class MockSequentialWorkflow: + def __init__(self, *args, **kwargs): + pass + + def add(self, *args, **kwargs): + pass + + def run(self): + pass + +# Test Task class +def test_task_initialization(): + description = "Sample Task" + flow = MockOpenAIChat() + task = Task(description=description, flow=flow) + assert task.description == description + assert task.flow == flow + +def test_task_execute(): + description = "Sample Task" + flow = MockOpenAIChat() + task = Task(description=description, flow=flow) + task.execute() + assert task.result == "Mocked result" + +# Test SequentialWorkflow class +def test_sequential_workflow_initialization(): + workflow = SequentialWorkflow() + assert isinstance(workflow, SequentialWorkflow) + assert len(workflow.tasks) == 0 + assert workflow.max_loops == 1 + assert workflow.autosave == False + assert workflow.saved_state_filepath == "sequential_workflow_state.json" + assert workflow.restore_state_filepath == None + assert workflow.dashboard == False + +def test_sequential_workflow_add_task(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + assert len(workflow.tasks) == 1 + assert workflow.tasks[0].description == task_description + assert workflow.tasks[0].flow == task_flow + +def test_sequential_workflow_reset_workflow(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.reset_workflow() + assert workflow.tasks[0].result == None + +def test_sequential_workflow_get_task_results(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + results = workflow.get_task_results() + assert len(results) == 1 + assert task_description in results + assert results[task_description] == "Mocked result" + +def test_sequential_workflow_remove_task(): + workflow = SequentialWorkflow() + task1_description = "Task 1" + task2_description = "Task 2" + task1_flow = MockOpenAIChat() + task2_flow = MockOpenAIChat() + workflow.add(task1_description, task1_flow) + workflow.add(task2_description, task2_flow) + workflow.remove_task(task1_description) + assert len(workflow.tasks) == 1 + assert workflow.tasks[0].description == task2_description + +def test_sequential_workflow_update_task(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.update_task(task_description, max_tokens=1000) + assert workflow.tasks[0].kwargs["max_tokens"] == 1000 + +def test_sequential_workflow_save_workflow_state(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.save_workflow_state("test_state.json") + assert os.path.exists("test_state.json") + os.remove("test_state.json") + +def test_sequential_workflow_load_workflow_state(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.save_workflow_state("test_state.json") + workflow.load_workflow_state("test_state.json") + assert len(workflow.tasks) == 1 + assert workflow.tasks[0].description == task_description + os.remove("test_state.json") + +def test_sequential_workflow_run(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockOpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + assert workflow.tasks[0].result == "Mocked result" + +def test_sequential_workflow_workflow_bootup(capfd): + workflow = SequentialWorkflow() + workflow.workflow_bootup() + out, _ = capfd.readouterr() + assert "Sequential Workflow Initializing..." in out + +def test_sequential_workflow_workflow_dashboard(capfd): + workflow = SequentialWorkflow() + workflow.workflow_dashboard() + out, _ = capfd.readouterr() + assert "Sequential Workflow Dashboard" in out + +# Mock Flow class for async testing +class MockAsyncFlow: + def __init__(self, *args, **kwargs): + pass + + async def arun(self, *args, **kwargs): + return "Mocked result" + +# Test async execution in SequentialWorkflow +@pytest.mark.asyncio +async def test_sequential_workflow_arun(): + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = MockAsyncFlow() + workflow.add(task_description, task_flow) + await workflow.arun() + assert workflow.tasks[0].result == "Mocked result" + + + + +def test_real_world_usage_with_openai_key(): + # Initialize the language model + llm = OpenAIChat() + assert isinstance(llm, OpenAIChat) + +def test_real_world_usage_with_flow_and_openai_key(): + # Initialize a flow with the language model + flow = Flow(llm=OpenAIChat()) + assert isinstance(flow, Flow) + +def test_real_world_usage_with_sequential_workflow(): + # Initialize a sequential workflow + workflow = SequentialWorkflow() + assert isinstance(workflow, SequentialWorkflow) + +def test_real_world_usage_add_tasks(): + # Create a sequential workflow and add tasks + workflow = SequentialWorkflow() + task1_description = "Task 1" + task2_description = "Task 2" + task1_flow = OpenAIChat() + task2_flow = OpenAIChat() + workflow.add(task1_description, task1_flow) + workflow.add(task2_description, task2_flow) + assert len(workflow.tasks) == 2 + assert workflow.tasks[0].description == task1_description + assert workflow.tasks[1].description == task2_description + +def test_real_world_usage_run_workflow(): + # Create a sequential workflow, add a task, and run the workflow + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = OpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + assert workflow.tasks[0].result is not None + +def test_real_world_usage_dashboard_display(): + # Create a sequential workflow, add tasks, and display the dashboard + workflow = SequentialWorkflow() + task1_description = "Task 1" + task2_description = "Task 2" + task1_flow = OpenAIChat() + task2_flow = OpenAIChat() + workflow.add(task1_description, task1_flow) + workflow.add(task2_description, task2_flow) + with patch("builtins.print") as mock_print: + workflow.workflow_dashboard() + mock_print.assert_called() + +def test_real_world_usage_async_execution(): + # Create a sequential workflow, add an async task, and run the workflow asynchronously + workflow = SequentialWorkflow() + task_description = "Sample Task" + async_task_flow = OpenAIChat() + + async def async_run_workflow(): + await workflow.arun() + + workflow.add(task_description, async_task_flow) + asyncio.run(async_run_workflow()) + assert workflow.tasks[0].result is not None + +def test_real_world_usage_multiple_loops(): + # Create a sequential workflow with multiple loops, add a task, and run the workflow + workflow = SequentialWorkflow(max_loops=3) + task_description = "Sample Task" + task_flow = OpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + assert workflow.tasks[0].result is not None + +def test_real_world_usage_autosave_state(): + # Create a sequential workflow with autosave, add a task, run the workflow, and check if state is saved + workflow = SequentialWorkflow(autosave=True) + task_description = "Sample Task" + task_flow = OpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + assert workflow.tasks[0].result is not None + assert os.path.exists("sequential_workflow_state.json") + os.remove("sequential_workflow_state.json") + +def test_real_world_usage_load_state(): + # Create a sequential workflow, add a task, save state, load state, and run the workflow + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = OpenAIChat() + workflow.add(task_description, task_flow) + workflow.run() + workflow.save_workflow_state("test_state.json") + workflow.load_workflow_state("test_state.json") + workflow.run() + assert workflow.tasks[0].result is not None + os.remove("test_state.json") + +def test_real_world_usage_update_task_args(): + # Create a sequential workflow, add a task, and update task arguments + workflow = SequentialWorkflow() + task_description = "Sample Task" + task_flow = OpenAIChat() + workflow.add(task_description, task_flow) + workflow.update_task(task_description, max_tokens=1000) + assert workflow.tasks[0].kwargs["max_tokens"] == 1000 + +def test_real_world_usage_remove_task(): + # Create a sequential workflow, add tasks, remove a task, and run the workflow + workflow = SequentialWorkflow() + task1_description = "Task 1" + task2_description = "Task 2" + task1_flow = OpenAIChat() + task2_flow = OpenAIChat() + workflow.add(task1_description, task1_flow) + workflow.add(task2_description, task2_flow) + workflow.remove_task(task1_description) + workflow.run() + assert len(workflow.tasks) == 1 + assert workflow.tasks[0].description == task2_description + +def test_real_world_usage_with_environment_variables(): + # Ensure that the OpenAI API key is set using environment variables + assert "OPENAI_API_KEY" in os.environ + assert os.environ["OPENAI_API_KEY"] == "mocked_api_key" + del os.environ["OPENAI_API_KEY"] # Clean up after the test + +def test_real_world_usage_no_openai_key(): + # Ensure that an exception is raised when the OpenAI API key is not set + with pytest.raises(ValueError): + llm = OpenAIChat() # API key not provided, should raise an exception \ No newline at end of file From 8c21a2aa5ebefc30e8eb29ad1fb2b8c4a3404452 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 5 Nov 2023 23:26:59 -0500 Subject: [PATCH 23/63] sequential workflow docs Former-commit-id: c94512d6548c2076d7bf4cdb67b59f4a87c54b67 --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index 55c7cf3d..abd2bd42 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -111,6 +111,7 @@ nav: - Overview: "swarms/structs/overview.md" - Workflow: "swarms/structs/workflow.md" - Flow: "swarms/structs/flow.md" + - SequentialWorkflow: 'swarms/structs/sequential_workflow.md' - swarms.memory: - PineconeVectorStoreStore: "swarms/memory/pinecone.md" - PGVectorStore: "swarms/memory/pg.md" From 841fd3128d743513a7a7098ee7d4a34b51bb0788 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 08:35:45 -0500 Subject: [PATCH 24/63] workflow states Former-commit-id: 383412bace9c4a0bafefdefdcc528b0620ff6c38 --- README.md | 45 ++++++++++++++++++++++ docs/swarms/structs/sequential_workflow.md | 39 ++++++++++++++++++- 2 files changed, 83 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 68d7ba05..f94221d4 100644 --- a/README.md +++ b/README.md @@ -98,6 +98,51 @@ god_mode.print_responses(task) ------ +### `SequentialWorkflow` +- Execute tasks step by step by passing in an LLM and the task description! +- Pass in flows with various LLMs +- Save and restore Workflow states! +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +api_key = ( + "" # Your actual API key here +) + +# Initialize the language flow +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Initialize the Flow with the language flow +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create another Flow for a different task +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + +# Suppose the next task takes the output of the first task as input +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") + +``` + ### `OmniModalAgent` - OmniModal Agent is an LLM that access to 10+ multi-modal encoders and diffusers! It can generate images, videos, speech, music and so much more, get started with: diff --git a/docs/swarms/structs/sequential_workflow.md b/docs/swarms/structs/sequential_workflow.md index 04587b89..12b38409 100644 --- a/docs/swarms/structs/sequential_workflow.md +++ b/docs/swarms/structs/sequential_workflow.md @@ -574,4 +574,41 @@ The Sequential Workflow module is a valuable tool for AI engineers working on au As the field of AI continues to evolve, the demand for efficient workflow management tools will only increase. The Sequential Workflow module is a step towards meeting these demands and empowering AI engineers to create more reliable and capable autonomous agents. With future enhancements and features, it has the potential to become an indispensable asset in the AI engineer's toolkit. -In summary, the Sequential Workflow module provides a foundation for orchestrating complex tasks and workflows, enabling AI engineers to focus on designing intelligent agents that can perform tasks with precision and reliability. \ No newline at end of file +In summary, the Sequential Workflow module provides a foundation for orchestrating complex tasks and workflows, enabling AI engineers to focus on designing intelligent agents that can perform tasks with precision and reliability. + + +## Frequently Asked Questions (FAQs) + +### Q1: What is the difference between a task and a flow in Sequential Workflows? + +**A1:** In Sequential Workflows, a **task** refers to a specific unit of work that needs to be executed. It can be implemented as a callable object, such as a Python function, and is the fundamental building block of a workflow. + +A **flow**, on the other hand, is an encapsulation of a task within the workflow. Flows define the order in which tasks are executed and can be thought of as task containers. They allow you to specify dependencies, error handling, and other workflow-related configurations. + +### Q2: Can I run tasks in parallel within a Sequential Workflow? + +**A2:** Yes, you can run tasks in parallel within a Sequential Workflow by using parallel execution techniques. This advanced feature allows you to execute multiple tasks concurrently, improving performance and efficiency. You can explore this feature further in the guide's section on "Parallel Execution." + +### Q3: How do I handle errors within Sequential Workflows? + +**A3:** Error handling within Sequential Workflows can be implemented by adding error-handling logic within your task functions. You can catch exceptions and handle errors gracefully, ensuring that your workflow can recover from unexpected scenarios. The guide also covers more advanced error handling strategies, such as retrying failed tasks and handling specific error types. + +### Q4: What are some real-world use cases for Sequential Workflows? + +**A4:** Sequential Workflows can be applied to a wide range of real-world use cases, including: + +- **Data ETL (Extract, Transform, Load) Processes:** Automating data pipelines that involve data extraction, transformation, and loading into databases or data warehouses. + +- **Batch Processing:** Running batch jobs that process large volumes of data or perform data analysis. + +- **Automation of DevOps Tasks:** Streamlining DevOps processes such as deployment, provisioning, and monitoring. + +- **Cross-system Integrations:** Automating interactions between different systems, services, or APIs. + +- **Report Generation:** Generating reports and documents automatically based on data inputs. + +- **Workflow Orchestration:** Orchestrating complex workflows involving multiple steps and dependencies. + +- **Resource Provisioning:** Automatically provisioning and managing cloud resources. + +These are just a few examples, and Sequential Workflows can be tailored to various automation needs across industries. From 1c4a9898f20aca71c53dc0de5d3ce5b819baf025 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 11:36:08 -0500 Subject: [PATCH 25/63] sequential workflow Former-commit-id: 71da697cc8dedac243bd73b9b6c8b09def6e3717 --- docs/examples/reliable_autonomous_agents.md | 229 ++++++++++++++++++++ pyproject.toml | 2 +- swarms/structs/__init__.py | 3 +- swarms/structs/sequential_workflow.py | 8 +- 4 files changed, 236 insertions(+), 6 deletions(-) create mode 100644 docs/examples/reliable_autonomous_agents.md diff --git a/docs/examples/reliable_autonomous_agents.md b/docs/examples/reliable_autonomous_agents.md new file mode 100644 index 00000000..21d0478b --- /dev/null +++ b/docs/examples/reliable_autonomous_agents.md @@ -0,0 +1,229 @@ +# Reliable Enterprise-Grade Autonomous Agents in Less Than 5 lines of Code +======================================================================== + +Welcome to this comprehensive walkthrough guide tutorial on the SequentialWorkflow feature of the Swarms Framework! In this tutorial, we will explore the purpose, usage, and key concepts of the SequentialWorkflow class, which is a part of the swarms package. Whether you are a beginner, intermediate, or expert developer, this tutorial will provide you with a clear understanding of how to effectively use the SequentialWorkflow class in your projects. + +AI engineering is a dynamic and evolving field that involves the development and deployment of intelligent systems and applications. In this ever-changing landscape, AI engineers often face the challenge of orchestrating complex sequences of tasks, managing data flows, and ensuring the smooth execution of AI workflows. This is where the Workflow Class, such as the SequentialWorkflow class we discussed earlier, plays a pivotal role in enabling AI engineers to achieve their goals efficiently and effectively. + +The Versatile World of AI Workflows +AI workflows encompass a wide range of tasks and processes, from data preprocessing and model training to natural language understanding and decision-making. These workflows are the backbone of AI systems, guiding them through intricate sequences of actions to deliver meaningful results. Here are some of the diverse use cases where the Workflow Class can empower AI engineers: + +1. Natural Language Processing (NLP) Pipelines +AI engineers often build NLP pipelines that involve multiple stages such as text preprocessing, tokenization, feature extraction, model inference, and post-processing. The Workflow Class enables the orderly execution of these stages, ensuring that textual data flows seamlessly through each step, resulting in accurate and coherent NLP outcomes. + +2. Data Ingestion and Transformation +AI projects frequently require the ingestion of diverse data sources, including structured databases, unstructured text, and multimedia content. The Workflow Class can be used to design data ingestion workflows that extract, transform, and load (ETL) data efficiently, making it ready for downstream AI tasks like training and analysis. + +3. Autonomous Agents and Robotics +In autonomous robotics and intelligent agent systems, workflows are essential for decision-making, sensor fusion, motion planning, and control. AI engineers can use the Workflow Class to create structured sequences of actions that guide robots and agents through dynamic environments, enabling them to make informed decisions and accomplish tasks autonomously. + +4. Machine Learning Model Training +Training machine learning models involves a series of steps, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation. The Workflow Class simplifies the orchestration of these steps, allowing AI engineers to experiment with different configurations and track the progress of model training. + +5. Content Generation and Summarization +AI-driven content generation tasks, such as generating articles, reports, or summaries, often require multiple steps, including content creation and post-processing. The Workflow Class can be used to create content generation workflows, ensuring that the generated content meets quality and coherence criteria. + +6. Adaptive Decision-Making +In AI systems that make real-time decisions based on changing data and environments, workflows facilitate adaptive decision-making. Engineers can use the Workflow Class to design decision-making pipelines that take into account the latest information and make informed choices. + +Enabling Efficiency and Maintainability +The Workflow Class provides AI engineers with a structured and maintainable approach to building, executing, and managing complex AI workflows. It offers the following advantages: + +Modularity: Workflows can be modularly designed, allowing engineers to focus on individual task implementations and ensuring code reusability. + +Debugging and Testing: The Workflow Class simplifies debugging and testing by providing a clear sequence of tasks and well-defined inputs and outputs for each task. + +Scalability: As AI projects grow in complexity, the Workflow Class can help manage and scale workflows by adding or modifying tasks as needed. + +Error Handling: The class supports error handling strategies, enabling engineers to define how to handle unexpected failures gracefully. + +Maintainability: With structured workflows, AI engineers can easily maintain and update AI systems as requirements evolve or new data sources become available. + +The Workflow Class, such as the SequentialWorkflow class, is an indispensable tool in the toolkit of AI engineers. It empowers engineers to design, execute, and manage AI workflows across a diverse range of use cases. By providing structure, modularity, and maintainability to AI projects, the Workflow Class contributes significantly to the efficiency and success of AI engineering endeavors. As the field of AI continues to advance, harnessing the power of workflow orchestration will remain a key ingredient in building intelligent and adaptable systems, now letโ€™s get started with SequentialWorkflow. + +## Official Swarms Links +Here is the Swarms website: + +Here is the Swarms Github: + +Here are the Swarms docs: + +And, join the Swarm community! + +Book a call with The Swarm Corporation here if youโ€™re interested in high performance custom swarms! + +Now letโ€™s beginโ€ฆ + +## Installation +Before we dive into the tutorial, make sure you have the following prerequisites in place: + +Python installed on your system. +The swarms library installed. You can install it via pip using the following command: + +`pip3 install --upgrade swarms` + +Additionally, you will need an API key for the OpenAIChat model to run the provided code examples. Replace "YOUR_API_KEY" with your actual API key in the code examples where applicable. + +## Getting Started +Letโ€™s start by importing the necessary modules and initializing the OpenAIChat model, which we will use in our workflow tasks. + + +```python +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Replace "YOUR_API_KEY" with your actual OpenAI API key +api_key = "YOUR_API_KEY" + +# Initialize the language model flow (e.g., GPT-3) +llm = OpenAIChat( + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) +We have initialized the OpenAIChat model, which will be used as a callable object in our tasks. Now, letโ€™s proceed to create the SequentialWorkflow. + +Creating a SequentialWorkflow +To create a SequentialWorkflow, follow these steps: + +# Initialize Flows for individual tasks +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) +# Create the Sequential Workflow +workflow = SequentialWorkflow(max_loops=1) +`````` +In this code snippet, we have initialized two Flow instances (flow1 and flow2) representing individual tasks within our workflow. These flows will use the OpenAIChat model we initialized earlier. We then create a SequentialWorkflow instance named workflow with a maximum loop count of 1. The max_loops parameter determines how many times the entire workflow can be run, and we set it to 1 for this example. + +Adding Tasks to the SequentialWorkflow +Now that we have created the SequentialWorkflow, letโ€™s add tasks to it. In our example, weโ€™ll create two tasks: one for generating a 10,000-word blog on โ€œhealth and wellnessโ€ and another for summarizing the generated blog. + +``` +### Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + +`workflow.add("Summarize the generated blog", flow2)` + +The workflow.add() method is used to add tasks to the workflow. Each task is described using a human-readable description, such as "Generate a 10,000 word blog on health and wellness," and is associated with a flow (callable object) that will be executed as the task. In our example, flow1 and flow2 represent the tasks. + +Running the SequentialWorkflow +With tasks added to the SequentialWorkflow, we can now run the workflow sequentially using the workflow.run() method. + +### Run the workflow +`workflow.run()` +Executing workflow.run() will start the execution of tasks in the order they were added to the workflow. In our example, it will first generate the blog and then summarize it. + +Accessing Task Results +After running the workflow, you can access the results of each task using the get_task_results() method. + +# Get and display the results of each task in the workflow +```python +results = workflow.get_task_results() +for task_description, result in results.items(): + print(f"Task: {task_description}, Result: {result}") +``` +The workflow.get_task_results() method returns a dictionary where the keys are task descriptions, and the values are the corresponding results. You can then iterate through the results and print them, as shown in the code snippet. + +Resetting a SequentialWorkflow +Sometimes, you might need to reset a SequentialWorkflow to start fresh. You can use the workflow.reset_workflow() method for this purpose. + +### Reset the workflow +`workflow.reset_workflow()` +Resetting the workflow clears the results of each task, allowing you to rerun the workflow from the beginning without reinitializing it. + +Updating Task Arguments +You can also update the arguments of a specific task in the workflow using the workflow.update_task() method. + +### Update the arguments of a specific task in the workflow +`workflow.update_task("Generate a 10,000 word blog on health and wellness.", max_loops=2)` + +In this example, we update the max_loops argument of the task with the description "Generate a 10,000 word blog on health and wellness" to 2. This can be useful if you want to change the behavior of a specific task without recreating the entire workflow. + +# Conclusion: Mastering Workflow Orchestration in AI Engineering +In the ever-evolving landscape of artificial intelligence (AI), where the pace of innovation and complexity of tasks are ever-increasing, harnessing the power of workflow orchestration is paramount. In this comprehensive walkthrough guide, weโ€™ve embarked on a journey through the world of workflow orchestration, focusing on the Workflow Class, with a specific emphasis on the SequentialWorkflow class. As we conclude this exploration, weโ€™ve delved deep into the intricacies of orchestrating AI workflows, and itโ€™s time to reflect on the valuable insights gained and the immense potential that this knowledge unlocks for AI engineers. + +The Art of Workflow Orchestration +At its core, workflow orchestration is the art of designing, managing, and executing sequences of tasks or processes in a structured and efficient manner. In the realm of AI engineering, where tasks can range from data preprocessing and model training to decision-making and autonomous actions, mastering workflow orchestration is a game-changer. It empowers AI engineers to streamline their work, ensure reliable execution, and deliver impactful results. + +The Workflow Class, and particularly the SequentialWorkflow class weโ€™ve explored, acts as a guiding light in this intricate journey. It provides AI engineers with a toolbox of tools and techniques to conquer the challenges of orchestrating AI workflows effectively. Through a disciplined approach and adherence to best practices, AI engineers can achieve the following: + +1. Structured Workflow Design +A well-structured workflow is the cornerstone of any successful AI project. The Workflow Class encourages AI engineers to break down complex tasks into manageable units. Each task becomes a building block that contributes to the overarching goal. Whether itโ€™s preprocessing data, training a machine learning model, or generating content, structured workflow design ensures clarity, modularity, and maintainability. + +2. Efficient Task Sequencing +In AI, the order of tasks often matters. One taskโ€™s output can be another taskโ€™s input, and ensuring the correct sequence of execution is crucial. The SequentialWorkflow class enforces this sequential execution, eliminating the risk of running tasks out of order. It ensures that the workflow progresses systematically, following the predefined sequence of tasks. + +3. Error Resilience and Recovery +AI systems must be resilient in the face of unexpected errors and failures. The Workflow Class equips AI engineers with error handling strategies, such as retries and fallbacks. These strategies provide the ability to gracefully handle issues, recover from failures, and continue the workflowโ€™s execution without disruption. + +4. Code Modularity and Reusability +Building AI workflows often involves implementing various tasks, each with its own logic. The Workflow Class encourages code modularity, allowing AI engineers to encapsulate tasks as separate units. This modularity promotes code reusability, making it easier to adapt and expand workflows as AI projects evolve. + +5. Efficient Debugging and Testing +Debugging and testing AI workflows can be challenging without clear structure and boundaries. The Workflow Class provides a clear sequence of tasks with well-defined inputs and outputs. This structure simplifies the debugging process, as AI engineers can isolate and test individual tasks, ensuring that each component functions as intended. + +6. Scalability and Adaptability +As AI projects grow in complexity, the Workflow Class scales effortlessly. AI engineers can add or modify tasks as needed, accommodating new data sources, algorithms, or requirements. This scalability ensures that workflows remain adaptable to changing demands and evolving AI landscapes. + +7. Maintainability and Future-Proofing +Maintaining AI systems over time is a crucial aspect of engineering. The Workflow Class fosters maintainability by providing a clear roadmap of tasks and their interactions. AI engineers can revisit, update, and extend workflows with confidence, ensuring that AI systems remain effective and relevant in the long run. + +Empowering AI Engineers +The knowledge and skills gained from this walkthrough guide go beyond technical proficiency. They empower AI engineers to be architects of intelligent systems, capable of orchestrating AI workflows that solve real-world problems. The Workflow Class is a versatile instrument in their hands, enabling them to tackle diverse use cases and engineering challenges. + +Diverse Use Cases for Workflow Class +Throughout this guide, we explored a myriad of use cases where the Workflow Class shines: + +Natural Language Processing (NLP) Pipelines: In NLP, workflows involve multiple stages, and the Workflow Class ensures orderly execution, resulting in coherent NLP outcomes. +Data Ingestion and Transformation: Data is the lifeblood of AI, and structured data workflows ensure efficient data preparation for downstream tasks. +Autonomous Agents and Robotics: For robots and intelligent agents, workflows enable autonomous decision-making and task execution. +Machine Learning Model Training: Model training workflows encompass numerous steps, and structured orchestration simplifies the process. +Content Generation and Summarization: Workflows for content generation ensure that generated content meets quality and coherence criteria. +Adaptive Decision-Making: In dynamic environments, workflows facilitate adaptive decision-making based on real-time data. +Efficiency and Maintainability +AI engineers not only have the tools to tackle these use cases but also the means to do so efficiently. The Workflow Class fosters efficiency and maintainability, making AI engineering endeavors more manageable: + +Modularity: Encapsulate tasks as separate units, promoting code reusability and maintainability. +Debugging and Testing: Streamline debugging and testing through clear task boundaries and well-defined inputs and outputs. +Scalability: As AI projects grow, workflows scale with ease, accommodating new components and requirements. +Error Handling: Gracefully handle errors and failures, ensuring that AI systems continue to operate smoothly. +Maintainability: AI systems remain adaptable and maintainable, even as the AI landscape evolves and requirements change. +The Future of AI Engineering +As AI engineering continues to advance, workflow orchestration will play an increasingly pivotal role. The Workflow Class is not a static tool; it is a dynamic enabler of innovation. In the future, we can expect further enhancements and features to meet the evolving demands of AI engineering: + +1. Asynchronous Support +Support for asynchronous task execution will improve the efficiency of workflows, especially when tasks involve waiting for external events or resources. + +2. Context Managers +Introducing context manager support for tasks can simplify resource management, such as opening and closing files or database connections. + +3. Workflow History +Maintaining a detailed history of workflow execution, including timestamps, task durations, and input/output data, will facilitate debugging and performance analysis. + +4. Parallel Processing +Enhancing the module to support parallel processing with a pool of workers can significantly speed up the execution of tasks, especially for computationally intensive workflows. + +5. Error Handling Strategies +Providing built-in error handling strategies, such as retries, fallbacks, and circuit breakers, will further enhance the resilience of workflows. + +Closing Thoughts +In conclusion, the journey through workflow orchestration in AI engineering has been both enlightening and empowering. The Workflow Class, and particularly the SequentialWorkflow class, has proven to be an invaluable ally in the AI engineerโ€™s toolkit. It offers structure, modularity, and efficiency, ensuring that AI projects progress smoothly from inception to deployment. + +As AI continues to permeate every aspect of our lives, the skills acquired in this guide will remain highly relevant and sought after. AI engineers armed with workflow orchestration expertise will continue to push the boundaries of what is possible, solving complex problems, and driving innovation. + +But beyond the technical aspects, this guide also emphasizes the importance of creativity, adaptability, and problem-solving. AI engineering is not just about mastering tools; itโ€™s about using them to make a meaningful impact on the world. + +So, whether youโ€™re just starting your journey into AI engineering or youโ€™re a seasoned professional seeking to expand your horizons, remember that the power of workflow orchestration lies not only in the code but in the limitless potential it unlocks for you as an AI engineer. As you embark on your own AI adventures, may this guide serve as a reliable companion, illuminating your path and inspiring your journey towards AI excellence. + +The world of AI is waiting for your innovation and creativity. With workflow orchestration as your guide, you have the tools to shape the future. The possibilities are boundless, and the future is yours to create. + +Official Swarms Links +Here is the Swarms website: + +Here is the Swarms Github: + +Here are the Swarms docs: + +And, join the Swarm community! + +Book a call with The Swarm Corporation here if youโ€™re interested in high performance custom swarms! \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 4af20ee0..e3a29e78 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "1.9.3" +version = "1.9.5" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index d360fa78..a842359c 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -1,5 +1,6 @@ from swarms.structs.workflow import Workflow from swarms.structs.task import Task from swarms.structs.flow import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow -__all__ = ["Workflow", "Task", "Flow"] +__all__ = ["Workflow", "Task", "Flow", "SequentialWorkflow"] diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index c89175f2..802e5442 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -16,8 +16,8 @@ TODO: import json from dataclasses import dataclass, field from typing import Any, Callable, Dict, List, Optional, Union + from termcolor import colored -from pydantic import BaseModel, validator from swarms.structs.flow import Flow @@ -217,9 +217,9 @@ class SequentialWorkflow: json.dump(state, f, indent=4) def workflow_bootup(self, **kwargs) -> None: - bootup = print( + print( colored( - f""" + """ Sequential Workflow Initializing...""", "green", attrs=["bold", "underline"], @@ -243,7 +243,7 @@ class SequentialWorkflow: >>> workflow.workflow_dashboard() """ - dashboard = print( + print( colored( f""" Sequential Workflow Dashboard From b39949f1d52daf2d9263c8a09e693c2bb3c82ac7 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 11:59:50 -0500 Subject: [PATCH 26/63] swarms docs corporate + sequential workflow Former-commit-id: 6010c9d689cbab181c23c3f63ae86d083f8a6731 --- docs/examples/reliable_autonomous_agents.md | 82 ++++++++++++--------- mkdocs.yml | 30 ++++---- 2 files changed, 62 insertions(+), 50 deletions(-) diff --git a/docs/examples/reliable_autonomous_agents.md b/docs/examples/reliable_autonomous_agents.md index 21d0478b..f2988075 100644 --- a/docs/examples/reliable_autonomous_agents.md +++ b/docs/examples/reliable_autonomous_agents.md @@ -1,43 +1,43 @@ -# Reliable Enterprise-Grade Autonomous Agents in Less Than 5 lines of Code +# Enterprise-Grade Workflow Automation With Autonomous Agents ======================================================================== Welcome to this comprehensive walkthrough guide tutorial on the SequentialWorkflow feature of the Swarms Framework! In this tutorial, we will explore the purpose, usage, and key concepts of the SequentialWorkflow class, which is a part of the swarms package. Whether you are a beginner, intermediate, or expert developer, this tutorial will provide you with a clear understanding of how to effectively use the SequentialWorkflow class in your projects. AI engineering is a dynamic and evolving field that involves the development and deployment of intelligent systems and applications. In this ever-changing landscape, AI engineers often face the challenge of orchestrating complex sequences of tasks, managing data flows, and ensuring the smooth execution of AI workflows. This is where the Workflow Class, such as the SequentialWorkflow class we discussed earlier, plays a pivotal role in enabling AI engineers to achieve their goals efficiently and effectively. -The Versatile World of AI Workflows +## The Versatile World of AI Workflows AI workflows encompass a wide range of tasks and processes, from data preprocessing and model training to natural language understanding and decision-making. These workflows are the backbone of AI systems, guiding them through intricate sequences of actions to deliver meaningful results. Here are some of the diverse use cases where the Workflow Class can empower AI engineers: -1. Natural Language Processing (NLP) Pipelines +### 1. Natural Language Processing (NLP) Pipelines AI engineers often build NLP pipelines that involve multiple stages such as text preprocessing, tokenization, feature extraction, model inference, and post-processing. The Workflow Class enables the orderly execution of these stages, ensuring that textual data flows seamlessly through each step, resulting in accurate and coherent NLP outcomes. -2. Data Ingestion and Transformation +### 2. Data Ingestion and Transformation AI projects frequently require the ingestion of diverse data sources, including structured databases, unstructured text, and multimedia content. The Workflow Class can be used to design data ingestion workflows that extract, transform, and load (ETL) data efficiently, making it ready for downstream AI tasks like training and analysis. -3. Autonomous Agents and Robotics +### 3. Autonomous Agents and Robotics In autonomous robotics and intelligent agent systems, workflows are essential for decision-making, sensor fusion, motion planning, and control. AI engineers can use the Workflow Class to create structured sequences of actions that guide robots and agents through dynamic environments, enabling them to make informed decisions and accomplish tasks autonomously. -4. Machine Learning Model Training +### 4. Machine Learning Model Training Training machine learning models involves a series of steps, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation. The Workflow Class simplifies the orchestration of these steps, allowing AI engineers to experiment with different configurations and track the progress of model training. -5. Content Generation and Summarization +### 5. Content Generation and Summarization AI-driven content generation tasks, such as generating articles, reports, or summaries, often require multiple steps, including content creation and post-processing. The Workflow Class can be used to create content generation workflows, ensuring that the generated content meets quality and coherence criteria. -6. Adaptive Decision-Making +### 6. Adaptive Decision-Making In AI systems that make real-time decisions based on changing data and environments, workflows facilitate adaptive decision-making. Engineers can use the Workflow Class to design decision-making pipelines that take into account the latest information and make informed choices. -Enabling Efficiency and Maintainability +## Enabling Efficiency and Maintainability The Workflow Class provides AI engineers with a structured and maintainable approach to building, executing, and managing complex AI workflows. It offers the following advantages: -Modularity: Workflows can be modularly designed, allowing engineers to focus on individual task implementations and ensuring code reusability. +- Modularity: Workflows can be modularly designed, allowing engineers to focus on individual task implementations and ensuring code reusability. -Debugging and Testing: The Workflow Class simplifies debugging and testing by providing a clear sequence of tasks and well-defined inputs and outputs for each task. +- Debugging and Testing: The Workflow Class simplifies debugging and testing by providing a clear sequence of tasks and well-defined inputs and outputs for each task. -Scalability: As AI projects grow in complexity, the Workflow Class can help manage and scale workflows by adding or modifying tasks as needed. +- Scalability: As AI projects grow in complexity, the Workflow Class can help manage and scale workflows by adding or modifying tasks as needed. -Error Handling: The class supports error handling strategies, enabling engineers to define how to handle unexpected failures gracefully. +- Error Handling: The class supports error handling strategies, enabling engineers to define how to handle unexpected failures gracefully. -Maintainability: With structured workflows, AI engineers can easily maintain and update AI systems as requirements evolve or new data sources become available. +- Maintainability: With structured workflows, AI engineers can easily maintain and update AI systems as requirements evolve or new data sources become available. The Workflow Class, such as the SequentialWorkflow class, is an indispensable tool in the toolkit of AI engineers. It empowers engineers to design, execute, and manage AI workflows across a diverse range of use cases. By providing structure, modularity, and maintainability to AI projects, the Workflow Class contributes significantly to the efficiency and success of AI engineering endeavors. As the field of AI continues to advance, harnessing the power of workflow orchestration will remain a key ingredient in building intelligent and adaptable systems, now letโ€™s get started with SequentialWorkflow. @@ -142,71 +142,81 @@ In this example, we update the max_loops argument of the task with the descripti # Conclusion: Mastering Workflow Orchestration in AI Engineering In the ever-evolving landscape of artificial intelligence (AI), where the pace of innovation and complexity of tasks are ever-increasing, harnessing the power of workflow orchestration is paramount. In this comprehensive walkthrough guide, weโ€™ve embarked on a journey through the world of workflow orchestration, focusing on the Workflow Class, with a specific emphasis on the SequentialWorkflow class. As we conclude this exploration, weโ€™ve delved deep into the intricacies of orchestrating AI workflows, and itโ€™s time to reflect on the valuable insights gained and the immense potential that this knowledge unlocks for AI engineers. -The Art of Workflow Orchestration +## The Art of Workflow Orchestration At its core, workflow orchestration is the art of designing, managing, and executing sequences of tasks or processes in a structured and efficient manner. In the realm of AI engineering, where tasks can range from data preprocessing and model training to decision-making and autonomous actions, mastering workflow orchestration is a game-changer. It empowers AI engineers to streamline their work, ensure reliable execution, and deliver impactful results. The Workflow Class, and particularly the SequentialWorkflow class weโ€™ve explored, acts as a guiding light in this intricate journey. It provides AI engineers with a toolbox of tools and techniques to conquer the challenges of orchestrating AI workflows effectively. Through a disciplined approach and adherence to best practices, AI engineers can achieve the following: -1. Structured Workflow Design +### 1. Structured Workflow Design A well-structured workflow is the cornerstone of any successful AI project. The Workflow Class encourages AI engineers to break down complex tasks into manageable units. Each task becomes a building block that contributes to the overarching goal. Whether itโ€™s preprocessing data, training a machine learning model, or generating content, structured workflow design ensures clarity, modularity, and maintainability. -2. Efficient Task Sequencing +### 2. Efficient Task Sequencing In AI, the order of tasks often matters. One taskโ€™s output can be another taskโ€™s input, and ensuring the correct sequence of execution is crucial. The SequentialWorkflow class enforces this sequential execution, eliminating the risk of running tasks out of order. It ensures that the workflow progresses systematically, following the predefined sequence of tasks. -3. Error Resilience and Recovery +### 3. Error Resilience and Recovery AI systems must be resilient in the face of unexpected errors and failures. The Workflow Class equips AI engineers with error handling strategies, such as retries and fallbacks. These strategies provide the ability to gracefully handle issues, recover from failures, and continue the workflowโ€™s execution without disruption. -4. Code Modularity and Reusability +### 4. Code Modularity and Reusability Building AI workflows often involves implementing various tasks, each with its own logic. The Workflow Class encourages code modularity, allowing AI engineers to encapsulate tasks as separate units. This modularity promotes code reusability, making it easier to adapt and expand workflows as AI projects evolve. -5. Efficient Debugging and Testing +### 5. Efficient Debugging and Testing Debugging and testing AI workflows can be challenging without clear structure and boundaries. The Workflow Class provides a clear sequence of tasks with well-defined inputs and outputs. This structure simplifies the debugging process, as AI engineers can isolate and test individual tasks, ensuring that each component functions as intended. -6. Scalability and Adaptability +### 6. Scalability and Adaptability As AI projects grow in complexity, the Workflow Class scales effortlessly. AI engineers can add or modify tasks as needed, accommodating new data sources, algorithms, or requirements. This scalability ensures that workflows remain adaptable to changing demands and evolving AI landscapes. -7. Maintainability and Future-Proofing +### 7. Maintainability and Future-Proofing Maintaining AI systems over time is a crucial aspect of engineering. The Workflow Class fosters maintainability by providing a clear roadmap of tasks and their interactions. AI engineers can revisit, update, and extend workflows with confidence, ensuring that AI systems remain effective and relevant in the long run. -Empowering AI Engineers +## Empowering AI Engineers The knowledge and skills gained from this walkthrough guide go beyond technical proficiency. They empower AI engineers to be architects of intelligent systems, capable of orchestrating AI workflows that solve real-world problems. The Workflow Class is a versatile instrument in their hands, enabling them to tackle diverse use cases and engineering challenges. -Diverse Use Cases for Workflow Class +## Diverse Use Cases for Workflow Class Throughout this guide, we explored a myriad of use cases where the Workflow Class shines: Natural Language Processing (NLP) Pipelines: In NLP, workflows involve multiple stages, and the Workflow Class ensures orderly execution, resulting in coherent NLP outcomes. + Data Ingestion and Transformation: Data is the lifeblood of AI, and structured data workflows ensure efficient data preparation for downstream tasks. + Autonomous Agents and Robotics: For robots and intelligent agents, workflows enable autonomous decision-making and task execution. + Machine Learning Model Training: Model training workflows encompass numerous steps, and structured orchestration simplifies the process. + Content Generation and Summarization: Workflows for content generation ensure that generated content meets quality and coherence criteria. + Adaptive Decision-Making: In dynamic environments, workflows facilitate adaptive decision-making based on real-time data. -Efficiency and Maintainability + +## Efficiency and Maintainability AI engineers not only have the tools to tackle these use cases but also the means to do so efficiently. The Workflow Class fosters efficiency and maintainability, making AI engineering endeavors more manageable: -Modularity: Encapsulate tasks as separate units, promoting code reusability and maintainability. -Debugging and Testing: Streamline debugging and testing through clear task boundaries and well-defined inputs and outputs. -Scalability: As AI projects grow, workflows scale with ease, accommodating new components and requirements. +- Modularity: Encapsulate tasks as separate units, promoting code reusability and maintainability. + +- Debugging and Testing: Streamline debugging and testing through clear task boundaries and well-defined inputs and outputs. + +- Scalability: As AI projects grow, workflows scale with ease, accommodating new components and requirements. Error Handling: Gracefully handle errors and failures, ensuring that AI systems continue to operate smoothly. -Maintainability: AI systems remain adaptable and maintainable, even as the AI landscape evolves and requirements change. -The Future of AI Engineering + +- Maintainability: AI systems remain adaptable and maintainable, even as the AI landscape evolves and requirements change. + +## The Future of AI Engineering As AI engineering continues to advance, workflow orchestration will play an increasingly pivotal role. The Workflow Class is not a static tool; it is a dynamic enabler of innovation. In the future, we can expect further enhancements and features to meet the evolving demands of AI engineering: -1. Asynchronous Support +### 1. Asynchronous Support Support for asynchronous task execution will improve the efficiency of workflows, especially when tasks involve waiting for external events or resources. -2. Context Managers +### 2. Context Managers Introducing context manager support for tasks can simplify resource management, such as opening and closing files or database connections. -3. Workflow History +### 3. Workflow History Maintaining a detailed history of workflow execution, including timestamps, task durations, and input/output data, will facilitate debugging and performance analysis. -4. Parallel Processing +### 4. Parallel Processing Enhancing the module to support parallel processing with a pool of workers can significantly speed up the execution of tasks, especially for computationally intensive workflows. -5. Error Handling Strategies +### 5. Error Handling Strategies Providing built-in error handling strategies, such as retries, fallbacks, and circuit breakers, will further enhance the resilience of workflows. -Closing Thoughts +## Closing Thoughts In conclusion, the journey through workflow orchestration in AI engineering has been both enlightening and empowering. The Workflow Class, and particularly the SequentialWorkflow class, has proven to be an invaluable ally in the AI engineerโ€™s toolkit. It offers structure, modularity, and efficiency, ensuring that AI projects progress smoothly from inception to deployment. As AI continues to permeate every aspect of our lives, the skills acquired in this guide will remain highly relevant and sought after. AI engineers armed with workflow orchestration expertise will continue to push the boundaries of what is possible, solving complex problems, and driving innovation. diff --git a/mkdocs.yml b/mkdocs.yml index abd2bd42..f7fd7f90 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,20 +61,6 @@ nav: - Home: - Overview: "index.md" - Contributing: "contributing.md" - - FAQ: "faq.md" - - Purpose: "purpose.md" - - Roadmap: "roadmap.md" - - Weaknesses: "failures.md" - - Design: "design.md" - - Flywheel: "flywheel.md" - - Bounties: "bounties.md" - - Metric: "metric.md" - - Distribution: "distribution" - - Research: "research.md" - - Demos: "demos.md" - - Architecture: "architecture.md" - - Checklist: "checklist.md" - - Hiring: "hiring.md" - Swarms: - Overview: "swarms/index.md" - swarms.swarms: @@ -122,11 +108,27 @@ nav: - Overview: "examples/index.md" - Structs: - Flow: "examples/flow.md" + - SequentialWorkflow: "examples/reliable_autonomous_agents.md" - Agents: - OmniAgent: "examples/omni_agent.md" - Worker: - Basic: "examples/worker.md" - StackedWorker: "examples/stacked_worker.md" +- Corporate: + - FAQ: "faq.md" + - Purpose: "purpose.md" + - Roadmap: "roadmap.md" + - Weaknesses: "failures.md" + - Design: "design.md" + - Flywheel: "flywheel.md" + - Bounties: "bounties.md" + - Metric: "metric.md" + - Distribution: "distribution" + - Research: "research.md" + - Demos: "demos.md" + - Architecture: "architecture.md" + - Checklist: "checklist.md" + - Hiring: "hiring.md" - Applications: - CustomerSupport: - Overview: "applications/customer_support.md" From d003c9cd82fc55b21117332f101dcf82c28b4126 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 12:37:35 -0500 Subject: [PATCH 27/63] multi modal auto agent + removed workflow.py Former-commit-id: c520cda250ab96f27fc26e602418c339f0fee313 --- mkdocs.yml | 12 ++++++------ multi_modal_auto_agent.py | 30 ++++++++++++++++++++++++++++++ swarms/structs/base.py | 5 +++++ swarms/structs/flow.py | 24 +++++++++++++----------- swarms/swarms/base.py | 2 -- workflow.py | 11 ----------- 6 files changed, 54 insertions(+), 30 deletions(-) create mode 100644 multi_modal_auto_agent.py create mode 100644 swarms/structs/base.py delete mode 100644 workflow.py diff --git a/mkdocs.yml b/mkdocs.yml index f7fd7f90..e3c93d94 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -114,6 +114,11 @@ nav: - Worker: - Basic: "examples/worker.md" - StackedWorker: "examples/stacked_worker.md" +- Applications: + - CustomerSupport: + - Overview: "applications/customer_support.md" + - Marketing: + - Overview: "applications/marketing_agencies.md" - Corporate: - FAQ: "faq.md" - Purpose: "purpose.md" @@ -128,9 +133,4 @@ nav: - Demos: "demos.md" - Architecture: "architecture.md" - Checklist: "checklist.md" - - Hiring: "hiring.md" -- Applications: - - CustomerSupport: - - Overview: "applications/customer_support.md" - - Marketing: - - Overview: "applications/marketing_agencies.md" + - Hiring: "hiring.md" \ No newline at end of file diff --git a/multi_modal_auto_agent.py b/multi_modal_auto_agent.py new file mode 100644 index 00000000..b462795f --- /dev/null +++ b/multi_modal_auto_agent.py @@ -0,0 +1,30 @@ +from swarms.structs import Flow +from swarms.models import Idefics + +# Multi Modality Auto Agent +llm = Idefics(max_length=2000) + +task = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG" + +## Initialize the workflow +flow = Flow( + llm=llm, + max_loops=2, + dashboard=True, + # stopping_condition=None, # You can define a stopping condition as needed. + # loop_interval=1, + # retry_attempts=3, + # retry_interval=1, + # interactive=False, # Set to 'True' for interactive mode. + # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. +) + +# out = flow.load_state("flow_state.json") +# temp = flow.dynamic_temperature() +# filter = flow.add_response_filter("Trump") +out = flow.run(task) +# out = flow.validate_response(out) +# out = flow.analyze_feedback(out) +# out = flow.print_history_and_memory() +# # out = flow.save_state("flow_state.json") +# print(out) diff --git a/swarms/structs/base.py b/swarms/structs/base.py new file mode 100644 index 00000000..f33a204e --- /dev/null +++ b/swarms/structs/base.py @@ -0,0 +1,5 @@ +""" +Base Structure for all Swarm Structures + + +""" \ No newline at end of file diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 0f129314..117172ea 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -111,10 +111,10 @@ class Flow: interactive: bool = False, dashboard: bool = False, name: str = "Flow agent", - system_message: str = FLOW_SYSTEM_PROMPT, + system_prompt: str = FLOW_SYSTEM_PROMPT, # tools: List[BaseTool] = None, dynamic_temperature: bool = False, - saved_state: Optional[str] = None, + saved_state_path: Optional[str] = "flow_state.json", autosave: bool = False, **kwargs: Any, ): @@ -133,9 +133,9 @@ class Flow: self.dashboard = dashboard self.dynamic_temperature = dynamic_temperature # self.tools = tools - self.system_message = system_message + self.system_prompt = system_prompt self.name = name - self.saved_state = saved_state + self.saved_state_path = saved_state_path self.autosave = autosave self.response_filters = [] @@ -206,7 +206,7 @@ class Flow: Flow Configuration: Name: {self.name} - System Prompt: {self.system_message} + System Prompt: {self.system_prompt} Task: {task} Max Loops: {self.max_loops} Stopping Condition: {self.stopping_condition} @@ -317,8 +317,10 @@ class Flow: time.sleep(self.loop_interval) self.memory.append(history) - # if self.autosave: - # self.save_state("flow_state.json") + if self.autosave: + save_path = self.saved_state_path or "flow_state.json" + print(colored(f"Autosaving flow state to {save_path}", "green")) + self.save_state(save_path) return response # , history @@ -422,7 +424,7 @@ class Flow: Returns: str: The agent history prompt """ - system_prompt = system_prompt or self.system_message + system_prompt = system_prompt or self.system_prompt agent_history_prompt = f""" SYSTEM_PROMPT: {system_prompt} @@ -736,7 +738,7 @@ class Flow: """ prompt = f""" - SYSTEM_PROMPT: {self.system_message} + SYSTEM_PROMPT: {self.system_prompt} History: {history} @@ -745,6 +747,6 @@ class Flow: response = self.llm(prompt, **kwargs) return {"role": self.name, "content": response} - def update_system_message(self, system_message: str): + def update_system_prompt(self, system_prompt: str): """Upddate the system message""" - self.system_message = system_message + self.system_prompt = system_prompt diff --git a/swarms/swarms/base.py b/swarms/swarms/base.py index 21f30ae3..e99c9b38 100644 --- a/swarms/swarms/base.py +++ b/swarms/swarms/base.py @@ -78,8 +78,6 @@ class AbstractSwarm(ABC): Scale down the number of workers - - """ # TODO: Pass in abstract LLM class that can utilize Hf or Anthropic models, Move away from OPENAI diff --git a/workflow.py b/workflow.py deleted file mode 100644 index bc757108..00000000 --- a/workflow.py +++ /dev/null @@ -1,11 +0,0 @@ -from swarms.models import OpenAIChat -from swarms.structs import Workflow - - -llm = OpenAIChat(openai_api_key="") - -workflow = Workflow(llm) - -workflow.add("What's the weather in miami") - -workflow.run() From 42b5c4664d7acae72cd536a17a2a3d8a993c08fc Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 12:56:17 -0500 Subject: [PATCH 28/63] docs clean up -> corporate folder Former-commit-id: 881ec11f07e786c50ad631ac5a527d0cc3c61742 --- docs/{ => corporate}/architecture.md | 0 docs/{ => corporate}/bounties.md | 0 docs/{ => corporate}/checklist.md | 0 docs/{ => corporate}/cost_analysis.md | 0 docs/{ => corporate}/demos.md | 0 docs/{ => corporate}/design.md | 0 docs/{ => corporate}/distribution.md | 0 docs/{ => corporate}/failures.md | 0 docs/{ => corporate}/faq.md | 0 docs/{ => corporate}/flywheel.md | 0 docs/{ => corporate}/hiring.md | 0 docs/{ => corporate}/metric.md | 0 docs/{ => corporate}/purpose.md | 0 docs/{ => corporate}/research.md | 0 docs/{ => corporate}/roadmap.md | 0 docs/examples/ideas.md | 63 +++++++++++++++++++++++++++ docs/swarms/models/fuyu.md | 7 --- docs/swarms/swarms/groupchat.md | 0 mkdocs.yml | 30 +++++++------ swarms/utils/code_interpreter.py | 11 ++++- 20 files changed, 89 insertions(+), 22 deletions(-) rename docs/{ => corporate}/architecture.md (100%) rename docs/{ => corporate}/bounties.md (100%) rename docs/{ => corporate}/checklist.md (100%) rename docs/{ => corporate}/cost_analysis.md (100%) rename docs/{ => corporate}/demos.md (100%) rename docs/{ => corporate}/design.md (100%) rename docs/{ => corporate}/distribution.md (100%) rename docs/{ => corporate}/failures.md (100%) rename docs/{ => corporate}/faq.md (100%) rename docs/{ => corporate}/flywheel.md (100%) rename docs/{ => corporate}/hiring.md (100%) rename docs/{ => corporate}/metric.md (100%) rename docs/{ => corporate}/purpose.md (100%) rename docs/{ => corporate}/research.md (100%) rename docs/{ => corporate}/roadmap.md (100%) create mode 100644 docs/examples/ideas.md create mode 100644 docs/swarms/swarms/groupchat.md diff --git a/docs/architecture.md b/docs/corporate/architecture.md similarity index 100% rename from docs/architecture.md rename to docs/corporate/architecture.md diff --git a/docs/bounties.md b/docs/corporate/bounties.md similarity index 100% rename from docs/bounties.md rename to docs/corporate/bounties.md diff --git a/docs/checklist.md b/docs/corporate/checklist.md similarity index 100% rename from docs/checklist.md rename to docs/corporate/checklist.md diff --git a/docs/cost_analysis.md b/docs/corporate/cost_analysis.md similarity index 100% rename from docs/cost_analysis.md rename to docs/corporate/cost_analysis.md diff --git a/docs/demos.md b/docs/corporate/demos.md similarity index 100% rename from docs/demos.md rename to docs/corporate/demos.md diff --git a/docs/design.md b/docs/corporate/design.md similarity index 100% rename from docs/design.md rename to docs/corporate/design.md diff --git a/docs/distribution.md b/docs/corporate/distribution.md similarity index 100% rename from docs/distribution.md rename to docs/corporate/distribution.md diff --git a/docs/failures.md b/docs/corporate/failures.md similarity index 100% rename from docs/failures.md rename to docs/corporate/failures.md diff --git a/docs/faq.md b/docs/corporate/faq.md similarity index 100% rename from docs/faq.md rename to docs/corporate/faq.md diff --git a/docs/flywheel.md b/docs/corporate/flywheel.md similarity index 100% rename from docs/flywheel.md rename to docs/corporate/flywheel.md diff --git a/docs/hiring.md b/docs/corporate/hiring.md similarity index 100% rename from docs/hiring.md rename to docs/corporate/hiring.md diff --git a/docs/metric.md b/docs/corporate/metric.md similarity index 100% rename from docs/metric.md rename to docs/corporate/metric.md diff --git a/docs/purpose.md b/docs/corporate/purpose.md similarity index 100% rename from docs/purpose.md rename to docs/corporate/purpose.md diff --git a/docs/research.md b/docs/corporate/research.md similarity index 100% rename from docs/research.md rename to docs/corporate/research.md diff --git a/docs/roadmap.md b/docs/corporate/roadmap.md similarity index 100% rename from docs/roadmap.md rename to docs/corporate/roadmap.md diff --git a/docs/examples/ideas.md b/docs/examples/ideas.md new file mode 100644 index 00000000..a0a9c9b7 --- /dev/null +++ b/docs/examples/ideas.md @@ -0,0 +1,63 @@ +# 2O+ Autonomous Agent Blogs + +1. **The Ultimate Guide to Deploying Production-Ready Autonomous Agents with Swarms** + - A comprehensive start-to-finish guide on implementing Swarms in a production environment. + +2. **5 Steps to Elevate Your AI with Swarms Multi-Modal Autonomous Agents** + - A walkthrough highlighting the simplicity of Swarmsโ€™ setup and deployment for various AI applications. + +3. **Integrating Swarms Into Your Enterprise Workflow: A Step-By-Step Tutorial** + - A practical guide focusing on integrating Swarms into existing enterprise systems. + +4. **Swarmsโ€™ Flow: Streamlining AI Deployment in Your Business** + - Exploring the benefits and technicalities of using the Flow feature to simplify complex AI workflows. + +5. **From Zero to Hero: Building Your First Enterprise-Grade AI Agent with Swarms** + - A beginner-friendly walkthrough for building and deploying an AI agent using Swarms. + +6. **Scaling AI with Swarms: Managing Multi-Agent Systems Efficiently** + - Strategies and best practices for scaling multi-agent systems in enterprise settings. + +7. **Creating Resilient AI Systems with Swarms' Autonomous Agents** + - Discussing the robustness of Swarms agents and how they maintain performance under stress. + +8. **Unlocking New Capabilities: Advanced Features of Swarms for AI Engineers** + - Diving into the more sophisticated features of Swarms and how they can be leveraged in complex projects. + +9. **Swarmsโ€™ Quick Wins: Implementing AI Agents in Less Than 5 Lines of Code** + - A focused guide on rapidly deploying functional AI agents with minimal coding. + +10. **Benchmarking Your AI: Performance Metrics with Swarms** + - How to use Swarms to measure and optimize the performance of AI agents. + +11. **Swarms Case Studies: Real-World Success Stories from AI Engineers** + - Sharing stories and testimonials of how various organizations successfully implemented Swarms. + +12. **Effortless Multi-Modal Model Deployment: A Swarms Walkthrough** + - Explaining how to use Swarms to deploy multi-modal models with ease. + +13. **Future-Proof Your AI: Adapting to New Tech with Swarms** + - How Swarms' flexible architecture allows for easy updates and adaptation to new AI technologies. + +14. **Enterprise AI Security: Ensuring Your Swarms Agents are Hack-Proof** + - Best practices for securing autonomous agents in enterprise applications. + +15. **Migrating to Swarms: Transitioning From Legacy Systems** + - A guide for AI engineers on migrating existing AI systems to Swarms without downtime. + +16. **Multi-Agent Collaboration: How Swarms Facilitates Teamwork Among AI** + - An insight into how Swarms allows for multiple AI agents to work together seamlessly. + +17. **The Engineer's Toolkit: Swarms' Features Every AI Developer Must Know** + - Highlighting the most useful tools and features of Swarms from an AI developerโ€™s perspective. + +18. **Swarms for Different Industries: Customizing AI Agents for Niche Markets** + - Exploring how Swarms can be tailored to fit the needs of various industries such as healthcare, finance, and retail. + +19. **Building Intelligent Workflows with Swarmsโ€™ Flow** + - A tutorial on using the Flow feature to create intelligent, responsive AI-driven workflows. + +20. **Troubleshooting Common Issues When Deploying Swarms Autonomous Agents** + - A problem-solving guide for AI engineers on overcoming common challenges when implementing Swarms agents. + +Each blog or walkthrough can be structured to not only showcase the functionality and benefits of the Swarms framework but also to establish the brand as a thought leader in the space of enterprise AI solutions. \ No newline at end of file diff --git a/docs/swarms/models/fuyu.md b/docs/swarms/models/fuyu.md index e342e51e..021469e8 100644 --- a/docs/swarms/models/fuyu.md +++ b/docs/swarms/models/fuyu.md @@ -42,13 +42,6 @@ from swarms.models import Fuyu fuyu = Fuyu() ``` -### Example 1 - Initialization - -```python -from swarms.models import Fuyu - -fuyu = Fuyu() -``` 2. Generate Text with Fuyu: diff --git a/docs/swarms/swarms/groupchat.md b/docs/swarms/swarms/groupchat.md new file mode 100644 index 00000000..e69de29b diff --git a/mkdocs.yml b/mkdocs.yml index e3c93d94..aff83631 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -67,6 +67,7 @@ nav: - AbstractSwarm: "swarms/swarms/abstractswarm.md" - AutoScaler: "swarms/swarms/autoscaler.md" - GodMode: "swarms/swarms/godmode.md" + - Groupchat: "swarms/swarms/groupchat.md" - swarms.workers: - AbstractWorker: "swarms/workers/base.md" - Overview: "swarms/workers/index.md" @@ -114,23 +115,24 @@ nav: - Worker: - Basic: "examples/worker.md" - StackedWorker: "examples/stacked_worker.md" + - 2O+ Autonomous Agent Blogs: "examples/ideas.md" - Applications: - CustomerSupport: - Overview: "applications/customer_support.md" - Marketing: - Overview: "applications/marketing_agencies.md" - Corporate: - - FAQ: "faq.md" - - Purpose: "purpose.md" - - Roadmap: "roadmap.md" - - Weaknesses: "failures.md" - - Design: "design.md" - - Flywheel: "flywheel.md" - - Bounties: "bounties.md" - - Metric: "metric.md" - - Distribution: "distribution" - - Research: "research.md" - - Demos: "demos.md" - - Architecture: "architecture.md" - - Checklist: "checklist.md" - - Hiring: "hiring.md" \ No newline at end of file + - FAQ: "corporate/faq.md" + - Purpose: "corporate/purpose.md" + - Roadmap: "corporate/roadmap.md" + - Weaknesses: "corporate/failures.md" + - Design: "corporate/design.md" + - Flywheel: "corporate/flywheel.md" + - Bounties: "corporate/bounties.md" + - Metric: "corporate/metric.md" + - Distribution: "corporate/distribution" + - Research: "corporate/research.md" + - Demos: "corporate/demos.md" + - Architecture: "corporate/architecture.md" + - Checklist: "corporate/checklist.md" + - Hiring: "corporate/hiring.md" diff --git a/swarms/utils/code_interpreter.py b/swarms/utils/code_interpreter.py index cf557385..af6eb327 100644 --- a/swarms/utils/code_interpreter.py +++ b/swarms/utils/code_interpreter.py @@ -24,7 +24,16 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): """ SubprocessCodeinterpreter is a base class for code interpreters that run code in a subprocess. - + + Attributes: + start_cmd (str): The command to start the subprocess. Should be a string that can be split by spaces. + process (subprocess.Popen): The subprocess that is running the code. + debug_mode (bool): Whether to print debug statements. + output_queue (queue.Queue): A queue that is filled with output from the subprocess. + done (threading.Event): An event that is set when the subprocess is done running code. + + Example: + >>> from swarms.utils.code_interpreter import SubprocessCodeInterpreter """ From 27046af620843a1d92bc681166fc17654c370ab9 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 12:57:39 -0500 Subject: [PATCH 29/63] m Former-commit-id: a97f759ae50a93b5747df65262fa9f388ec6b0a2 --- mkdocs.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index aff83631..338b0cda 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -107,14 +107,10 @@ nav: - PdfChunker: "swarms/chunkers/pdf_chunker.md" - Walkthroughs: - Overview: "examples/index.md" - - Structs: + - Agents: - Flow: "examples/flow.md" - SequentialWorkflow: "examples/reliable_autonomous_agents.md" - - Agents: - OmniAgent: "examples/omni_agent.md" - - Worker: - - Basic: "examples/worker.md" - - StackedWorker: "examples/stacked_worker.md" - 2O+ Autonomous Agent Blogs: "examples/ideas.md" - Applications: - CustomerSupport: From b189c7a49ab319d93d76fa7c690a68af278a386a Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 12:57:47 -0500 Subject: [PATCH 30/63] guides Former-commit-id: fe48ec1393fdad65221dd9238eda95db503da899 --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 338b0cda..58430091 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -105,7 +105,7 @@ nav: - swarms.chunkers: - BaseChunker: "swarms/chunkers/basechunker.md" - PdfChunker: "swarms/chunkers/pdf_chunker.md" -- Walkthroughs: +- Guides: - Overview: "examples/index.md" - Agents: - Flow: "examples/flow.md" From 381e5505a58b2d617617af5c47c66bb51c84fb16 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 16:23:49 -0500 Subject: [PATCH 31/63] GPT4Vision + Dalle3 -> modules + tests + documentation Former-commit-id: fd8919dde5f1b38b823fd4862046f051154abb63 --- dalle3.py | 6 + docs/swarms/models/gpt4v.md | 251 ++++++++++++++++++ gpt4vision_example.py | 7 + sequential_workflow_example.py | 4 +- swarms/models/dalle3.py | 175 +++++++++++++ swarms/models/gpt4v.py | 288 +++++++++++++++++++++ swarms/structs/base.py | 2 +- swarms/utils/code_interpreter.py | 2 +- tests/models/dalle3.py | 374 +++++++++++++++++++++++++++ tests/models/gpt4v.py | 321 +++++++++++++++++++++++ tests/structs/sequential_workflow.py | 35 ++- 11 files changed, 1456 insertions(+), 9 deletions(-) create mode 100644 dalle3.py create mode 100644 docs/swarms/models/gpt4v.md create mode 100644 gpt4vision_example.py create mode 100644 swarms/models/dalle3.py create mode 100644 swarms/models/gpt4v.py create mode 100644 tests/models/dalle3.py create mode 100644 tests/models/gpt4v.py diff --git a/dalle3.py b/dalle3.py new file mode 100644 index 00000000..ac9ba760 --- /dev/null +++ b/dalle3.py @@ -0,0 +1,6 @@ +from swarms.models.dalle3 import Dalle3 + +model = Dalle3() + +task = "A painting of a dog" +img = model(task) diff --git a/docs/swarms/models/gpt4v.md b/docs/swarms/models/gpt4v.md new file mode 100644 index 00000000..2af4348b --- /dev/null +++ b/docs/swarms/models/gpt4v.md @@ -0,0 +1,251 @@ +# GPT4Vision Documentation + +## Table of Contents +- [Overview](#overview) +- [Installation](#installation) +- [Initialization](#initialization) +- [Methods](#methods) + - [process_img](#process_img) + - [__call__](#__call__) + - [run](#run) + - [arun](#arun) +- [Configuration Options](#configuration-options) +- [Usage Examples](#usage-examples) +- [Additional Tips](#additional-tips) +- [References and Resources](#references-and-resources) + +--- + +## Overview + +The GPT4Vision Model API is designed to provide an easy-to-use interface for interacting with the OpenAI GPT-4 Vision model. This model can generate textual descriptions for images and answer questions related to visual content. Whether you want to describe images or perform other vision-related tasks, GPT4Vision makes it simple and efficient. + +The library offers a straightforward way to send images and tasks to the GPT-4 Vision model and retrieve the generated responses. It handles API communication, authentication, and retries, making it a powerful tool for developers working with computer vision and natural language processing tasks. + +## Installation + +To use the GPT4Vision Model API, you need to install the required dependencies and configure your environment. Follow these steps to get started: + +1. Install the required Python package: + + ```bash + pip3 install --upgrade swarms + ``` + +2. Make sure you have an OpenAI API key. You can obtain one by signing up on the [OpenAI platform](https://beta.openai.com/signup/). + +3. Set your OpenAI API key as an environment variable. You can do this in your code or your environment configuration. Alternatively, you can provide the API key directly when initializing the `GPT4Vision` class. + +## Initialization + +To start using the GPT4Vision Model API, you need to create an instance of the `GPT4Vision` class. You can customize its behavior by providing various configuration options, but it also comes with sensible defaults. + +Here's how you can initialize the `GPT4Vision` class: + +```python +from swarms.models.gpt4v import GPT4Vision + +gpt4vision = GPT4Vision( + api_key="Your Key" +) +``` + +The above code initializes the `GPT4Vision` class with default settings. You can adjust these settings as needed. + +## Methods + +### `process_img` + +The `process_img` method is used to preprocess an image before sending it to the GPT-4 Vision model. It takes the image path as input and returns the processed image in a format suitable for API requests. + +```python +processed_img = gpt4vision.process_img(img_path) +``` + +- `img_path` (str): The file path or URL of the image to be processed. + +### `__call__` + +The `__call__` method is the main method for interacting with the GPT-4 Vision model. It sends the image and tasks to the model and returns the generated response. + +```python +response = gpt4vision(img, tasks) +``` + +- `img` (Union[str, List[str]]): Either a single image URL or a list of image URLs to be used for the API request. +- `tasks` (List[str]): A list of tasks or questions related to the image(s). + +This method returns a `GPT4VisionResponse` object, which contains the generated answer. + +### `run` + +The `run` method is an alternative way to interact with the GPT-4 Vision model. It takes a single task and image URL as input and returns the generated response. + +```python +response = gpt4vision.run(task, img) +``` + +- `task` (str): The task or question related to the image. +- `img` (str): The image URL to be used for the API request. + +This method simplifies interactions when dealing with a single task and image. + +### `arun` + +The `arun` method is an asynchronous version of the `run` method. It allows for asynchronous processing of API requests, which can be useful in certain scenarios. + +```python +import asyncio + +async def main(): + response = await gpt4vision.arun(task, img) + print(response) + +loop = asyncio.get_event_loop() +loop.run_until_complete(main()) +``` + +- `task` (str): The task or question related to the image. +- `img` (str): The image URL to be used for the API request. + +## Configuration Options + +The `GPT4Vision` class provides several configuration options that allow you to customize its behavior: + +- `max_retries` (int): The maximum number of retries to make to the API. Default: 3 +- `backoff_factor` (float): The backoff factor to use for exponential backoff. Default: 2.0 +- `timeout_seconds` (int): The timeout in seconds for the API request. Default: 10 +- `api_key` (str): The API key to use for the API request. Default: None (set via environment variable) +- `quality` (str): The quality of the image to generate. Options: 'low' or 'high'. Default: 'low' +- `max_tokens` (int): The maximum number of tokens to use for the API request. Default: 200 + +## Usage Examples + +### Example 1: Generating Image Descriptions + +```python +gpt4vision = GPT4Vision() +img = "https://example.com/image.jpg" +tasks = ["Describe this image."] +response = gpt4vision(img, tasks) +print(response.answer) +``` + +In this example, we create an instance of `GPT4Vision`, provide an image URL, and ask the model to describe the image. The response contains the generated description. + +### Example 2: Custom Configuration + +```python +custom_config = { + "max_retries": 5, + "timeout_seconds": 20, + "quality": "high", + "max_tokens": 300, +} +gpt4vision = GPT4Vision(**custom_config) +img = "https://example.com/another_image.jpg" +tasks = ["What objects can you identify in this image?"] +response = gpt4vision(img, tasks) +print(response.answer) +``` + +In this example, we create an instance of `GPT4Vision` with custom configuration options. We set a higher timeout, request high-quality images, and allow more tokens in the response. + +### Example 3: Using the `run` Method + +```python +gpt4vision = GPT4Vision() +img = "https://example.com/image.jpg" +task = "Describe this image in detail." +response = gpt4vision.run(task, img) +print(response) +``` + +In this example, we use the `run` method to simplify the interaction by providing a single task and image URL. + +# Model Usage and Image Understanding + +The GPT-4 Vision model processes images in a unique way, allowing it to answer questions about both or each of the images independently. Here's an overview: + +| Purpose | Description | +| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | +| Image Understanding | The model is shown two copies of the same image and can answer questions about both or each of the images independently. | + +# Image Detail Control + +You have control over how the model processes the image and generates textual understanding by using the `detail` parameter, which has two options: `low` and `high`. + +| Detail | Description | +| -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| low | Disables the "high-res" model. The model receives a low-res 512 x 512 version of the image and represents the image with a budget of 65 tokens. Ideal for use cases not requiring high detail. | +| high | Enables "high-res" mode. The model first sees the low-res image and then creates detailed crops of input images as 512px squares based on the input image size. Uses a total of 129 tokens. | + +# Managing Images + +To use the Chat Completions API effectively, you must manage the images you pass to the model. Here are some key considerations: + +| Management Aspect | Description | +| ------------------------- | ------------------------------------------------------------------------------------------------- | +| Image Reuse | To pass the same image multiple times, include the image with each API request. | +| Image Size Optimization | Improve latency by downsizing images to meet the expected size requirements. | +| Image Deletion | After processing, images are deleted from OpenAI servers and not retained. No data is used for training. | + +# Limitations + +While GPT-4 with Vision is powerful, it has some limitations: + +| Limitation | Description | +| -------------------------------------------- | --------------------------------------------------------------------------------------------------- | +| Medical Images | Not suitable for interpreting specialized medical images like CT scans. | +| Non-English Text | May not perform optimally when handling non-Latin alphabets, such as Japanese or Korean. | +| Large Text in Images | Enlarge text within images for readability, but avoid cropping important details. | +| Rotated or Upside-Down Text/Images | May misinterpret rotated or upside-down text or images. | +| Complex Visual Elements | May struggle to understand complex graphs or text with varying colors or styles. | +| Spatial Reasoning | Struggles with tasks requiring precise spatial localization, such as identifying chess positions. | +| Accuracy | May generate incorrect descriptions or captions in certain scenarios. | +| Panoramic and Fisheye Images | Struggles with panoramic and fisheye images. | + +# Calculating Costs + +Image inputs are metered and charged in tokens. The token cost depends on the image size and detail option. + +| Example | Token Cost | +| --------------------------------------------- | ----------- | +| 1024 x 1024 square image in detail: high mode | 765 tokens | +| 2048 x 4096 image in detail: high mode | 1105 tokens | +| 4096 x 8192 image in detail: low mode | 85 tokens | + +# FAQ + +Here are some frequently asked questions about GPT-4 with Vision: + +| Question | Answer | +| -------------------------------------------- | -------------------------------------------------------------------------------------------------- | +| Fine-Tuning Image Capabilities | No, fine-tuning the image capabilities of GPT-4 is not supported at this time. | +| Generating Images | GPT-4 is used for understanding images, not generating them. | +| Supported Image File Types | Supported image file types include PNG (.png), JPEG (.jpeg and .jpg), WEBP (.webp), and non-animated GIF (.gif). | +| Image Size Limitations | Image uploads are restricted to 20MB per image. | +| Image Deletion | Uploaded images are automatically deleted after processing by the model. | +| Learning More | For more details about GPT-4 with Vision, refer to the GPT-4 with Vision system card. | +| CAPTCHA Submission | CAPTCHAs are blocked for safety reasons. | +| Rate Limits | Image processing counts toward your tokens per minute (TPM) limit. Refer to the calculating costs section for details. | +| Image Metadata | The model does not receive image metadata. | +| Handling Unclear Images | If an image is unclear, the model will do its best to interpret it, but results may be less accurate. | + + + +## Additional Tips + +- Make sure to handle potential exceptions and errors when making API requests. The library includes retries and error handling, but it's essential to handle exceptions gracefully in your code. +- Experiment with different configuration options to optimize the trade-off between response quality and response time based on your specific requirements. + +## References and Resources + +- [OpenAI Platform](https://beta.openai.com/signup/): Sign up for an OpenAI API key. +- [OpenAI API Documentation](https://platform.openai.com/docs/api-reference/chat/create): Official API documentation for the GPT-4 Vision model. + +Now you have a comprehensive understanding of the GPT4Vision Model API, its configuration options, and how to use it for various computer vision and natural language processing tasks. Start experimenting and integrating it into your projects to leverage the power of GPT-4 Vision for image-related tasks. + +# Conclusion + +With GPT-4 Vision, you have a powerful tool for understanding and generating textual descriptions for images. By considering its capabilities, limitations, and cost calculations, you can effectively leverage this model for various image-related tasks. \ No newline at end of file diff --git a/gpt4vision_example.py b/gpt4vision_example.py new file mode 100644 index 00000000..7306fc56 --- /dev/null +++ b/gpt4vision_example.py @@ -0,0 +1,7 @@ +from swarms.models.gpt4v import GPT4Vision + +gpt4vision = GPT4Vision(api_key="") +task = "What is the following image about?" +img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + +answer = gpt4vision.run(task, img) diff --git a/sequential_workflow_example.py b/sequential_workflow_example.py index b9ab8196..feb6c748 100644 --- a/sequential_workflow_example.py +++ b/sequential_workflow_example.py @@ -3,9 +3,7 @@ from swarms.structs import Flow from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language flow llm = OpenAIChat( diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py new file mode 100644 index 00000000..f22b11e0 --- /dev/null +++ b/swarms/models/dalle3.py @@ -0,0 +1,175 @@ +import openai +import logging +import os +from dataclasses import dataclass +from functools import lru_cache +from termcolor import colored +from openai import OpenAI +from dotenv import load_dotenv +from pydantic import BaseModel, validator +from PIL import Image +from io import BytesIO + + +load_dotenv() + +api_key = os.getenv("OPENAI_API_KEY") + +# Configure Logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +@dataclass +class Dalle3: + """ + Dalle3 model class + + Attributes: + ----------- + image_url: str + The image url generated by the Dalle3 API + + Methods: + -------- + __call__(self, task: str) -> Dalle3: + Makes a call to the Dalle3 API and returns the image url + + Example: + -------- + >>> dalle3 = Dalle3() + >>> task = "A painting of a dog" + >>> image_url = dalle3(task) + >>> print(image_url) + https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png + + """ + + model: str = "dall-e-3" + img: str = None + size: str = "1024x1024" + max_retries: int = 3 + quality: str = "standard" + n: int = 4 + client = OpenAI( + api_key=api_key, + max_retries=max_retries, + ) + + class Config: + """Config class for the Dalle3 model""" + + arbitrary_types_allowed = True + + @validator("max_retries", "time_seconds") + def must_be_positive(cls, value): + if value <= 0: + raise ValueError("Must be positive") + return value + + def read_img(self, img: str): + """Read the image using pil""" + img = Image.open(img) + return img + + def set_width_height(self, img: str, width: int, height: int): + """Set the width and height of the image""" + img = self.read_img(img) + img = img.resize((width, height)) + return img + + def convert_to_bytesio(self, img: str, format: str = "PNG"): + """Convert the image to an bytes io object""" + byte_stream = BytesIO() + img.save(byte_stream, format=format) + byte_array = byte_stream.getvalue() + return byte_array + + # @lru_cache(maxsize=32) + def __call__(self, task: str): + """ + Text to image conversion using the Dalle3 API + + Parameters: + ----------- + task: str + The task to be converted to an image + + Returns: + -------- + Dalle3: + An instance of the Dalle3 class with the image url generated by the Dalle3 API + + Example: + -------- + >>> dalle3 = Dalle3() + >>> task = "A painting of a dog" + >>> image_url = dalle3(task) + >>> print(image_url) + https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png + """ + try: + # Making a call to the the Dalle3 API + response = self.client.images.generate( + # model=self.model, + prompt=task, + # size=self.size, + # quality=self.quality, + n=self.n, + ) + # Extracting the image url from the response + img = response.data[0].url + return img + except openai.OpenAIError as error: + # Handling exceptions and printing the errors details + print( + colored( + f"Error running Dalle3: {error} try optimizing your api key and or try again", + "red", + ) + ) + raise error + + def create_variations(self, img: str): + """ + Create variations of an image using the Dalle3 API + + Parameters: + ----------- + img: str + The image to be used for the API request + + Returns: + -------- + img: str + The image url generated by the Dalle3 API + + Example: + -------- + >>> dalle3 = Dalle3() + >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + >>> img = dalle3.create_variations(img) + >>> print(img) + + + """ + try: + + response = self.client.images.create_variation( + img = open(img, "rb"), + n=self.n, + size=self.size + ) + img = response.data[0].url + + return img + except (Exception, openai.OpenAIError) as error: + print( + colored( + f"Error running Dalle3: {error} try optimizing your api key and or try again", + "red", + ) + ) + print(colored(f"Error running Dalle3: {error.http_status}", "red")) + print(colored(f"Error running Dalle3: {error.error}", "red")) + raise error \ No newline at end of file diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py new file mode 100644 index 00000000..a7f8f1c1 --- /dev/null +++ b/swarms/models/gpt4v.py @@ -0,0 +1,288 @@ +import base64 +import logging +import os +import time +from dataclasses import dataclass +from typing import List, Optional, Union + +import requests +from dotenv import load_dotenv +from openai import OpenAI +from termcolor import colored + +# ENV +load_dotenv() + + +def logging_config(): + """Configures logging""" + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + logger = logging.getLogger(__name__) + + return logger + + +@dataclass +class GPT4VisionResponse: + """A response structure for GPT-4""" + + answer: str + + +@dataclass +class GPT4Vision: + """ + GPT4Vision model class + + Attributes: + ----------- + max_retries: int + The maximum number of retries to make to the API + backoff_factor: float + The backoff factor to use for exponential backoff + timeout_seconds: int + The timeout in seconds for the API request + api_key: str + The API key to use for the API request + quality: str + The quality of the image to generate + max_tokens: int + The maximum number of tokens to use for the API request + + Methods: + -------- + process_img(self, img_path: str) -> str: + Processes the image to be used for the API request + __call__(self, img: Union[str, List[str]], tasks: List[str]) -> GPT4VisionResponse: + Makes a call to the GPT-4 Vision API and returns the image url + + Example: + >>> gpt4vision = GPT4Vision() + >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + >>> tasks = ["A painting of a dog"] + >>> answer = gpt4vision(img, tasks) + >>> print(answer) + + + """ + + max_retries: int = 3 + model: str = "gpt-4-vision-preview" + backoff_factor: float = 2.0 + timeout_seconds: int = 10 + api_key: Optional[str] = None or os.getenv("OPENAI_API_KEY") + # 'Low' or 'High' for respesctively fast or high quality, but high more token usage + quality: str = "low" + # Max tokens to use for the API request, the maximum might be 3,000 but we don't know + max_tokens: int = 200 + client = OpenAI( + api_key=api_key, + max_retries=max_retries, + ) + logger = logging_config() + + class Config: + """Config class for the GPT4Vision model""" + + arbitary_types_allowed = True + + def process_img(self, img: str) -> str: + """Processes the image to be used for the API request""" + with open(img, "rb") as image_file: + return base64.b64encode(image_file.read()).decode("utf-8") + + def __call__( + self, + img: Union[str, List[str]], + tasks: List[str], + ) -> GPT4VisionResponse: + """ + Calls the GPT-4 Vision API and returns the image url + + Parameters: + ----------- + img: Union[str, List[str]] + The image to be used for the API request + tasks: List[str] + The tasks to be used for the API request + + Returns: + -------- + answer: GPT4VisionResponse + The response from the API request + + Example: + -------- + >>> gpt4vision = GPT4Vision() + >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + >>> tasks = ["A painting of a dog"] + >>> answer = gpt4vision(img, tasks) + >>> print(answer) + + + """ + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}", + } + + # Image content + image_content = [ + {"type": "imavge_url", "image_url": img} + if img.startswith("http") + else {"type": "image", "data": img} + for img in img + ] + + messages = [ + { + "role": "user", + "content": image_content + [{"type": "text", "text": q} for q in tasks], + } + ] + + payload = { + "model": "gpt-4-vision-preview", + "messages": messages, + "max_tokens": self.max_tokens, + "detail": self.quality, + } + + for attempt in range(self.max_retries): + try: + response = requests.post( + "https://api.openai.com/v1/chat/completions", + headers=headers, + json=payload, + timeout=self.timeout_seconds, + ) + response.raise_for_status() + answer = response.json()["choices"][0]["message"]["content"]["text"] + return GPT4VisionResponse(answer=answer) + except requests.exceptions.HTTPError as error: + self.logger.error( + f"HTTP error: {error.response.status_code}, {error.response.text}" + ) + if error.response.status_code in [429, 500, 503]: + # Exponential backoff = 429(too many requesys) + # And 503 = (Service unavailable) errors + time.sleep(self.backoff_factor**attempt) + else: + break + + except requests.exceptions.RequestException as error: + self.logger.error(f"Request error: {error}") + time.sleep(self.backoff_factor**attempt) + except Exception as error: + self.logger.error( + f"Unexpected Error: {error} try optimizing your api key and try again" + ) + raise error from None + + raise TimeoutError("API Request timed out after multiple retries") + + def run(self, task: str, img: str) -> str: + """ + Runs the GPT-4 Vision API + + Parameters: + ----------- + task: str + The task to be used for the API request + img: str + The image to be used for the API request + + Returns: + -------- + out: str + The response from the API request + + Example: + -------- + >>> gpt4vision = GPT4Vision() + >>> task = "A painting of a dog" + >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + >>> answer = gpt4vision.run(task, img) + >>> print(answer) + """ + try: + response = self.client.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": f"{task}"}, + { + "type": "image_url", + "image_url": f"{img}", + }, + ], + } + ], + max_tokens=self.max_tokens, + ) + + out = response.choices[0].text + return out + except Exception as error: + print( + colored( + f"Error when calling GPT4Vision, Error: {error} Try optimizing your key, and try again", + "red", + ) + ) + + async def arun(self, task: str, img: str) -> str: + """ + Asynchronous run method for GPT-4 Vision + + Parameters: + ----------- + task: str + The task to be used for the API request + img: str + The image to be used for the API request + + Returns: + -------- + out: str + The response from the API request + + Example: + -------- + >>> gpt4vision = GPT4Vision() + >>> task = "A painting of a dog" + >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + >>> answer = await gpt4vision.arun(task, img) + >>> print(answer) + """ + try: + response = await self.client.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": f"{task}"}, + { + "type": "image_url", + "image_url": f"{img}", + }, + ], + } + ], + max_tokens=self.max_tokens, + ) + out = response.choices[0].text + return out + except Exception as error: + print( + colored( + f"Error when calling GPT4Vision, Error: {error} Try optimizing your key, and try again", + "red", + ) + ) diff --git a/swarms/structs/base.py b/swarms/structs/base.py index f33a204e..4208ba39 100644 --- a/swarms/structs/base.py +++ b/swarms/structs/base.py @@ -2,4 +2,4 @@ Base Structure for all Swarm Structures -""" \ No newline at end of file +""" diff --git a/swarms/utils/code_interpreter.py b/swarms/utils/code_interpreter.py index af6eb327..2448edc7 100644 --- a/swarms/utils/code_interpreter.py +++ b/swarms/utils/code_interpreter.py @@ -24,7 +24,7 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): """ SubprocessCodeinterpreter is a base class for code interpreters that run code in a subprocess. - + Attributes: start_cmd (str): The command to start the subprocess. Should be a string that can be split by spaces. process (subprocess.Popen): The subprocess that is running the code. diff --git a/tests/models/dalle3.py b/tests/models/dalle3.py new file mode 100644 index 00000000..ff1489ea --- /dev/null +++ b/tests/models/dalle3.py @@ -0,0 +1,374 @@ +import os +from unittest.mock import Mock + +import pytest +from openai import OpenAIError +from PIL import Image +from termcolor import colored + +from dalle3 import Dalle3 + + +# Mocking the OpenAI client to avoid making actual API calls during testing +@pytest.fixture +def mock_openai_client(): + return Mock() + + +@pytest.fixture +def dalle3(mock_openai_client): + return Dalle3(client=mock_openai_client) + + +def test_dalle3_call_success(dalle3, mock_openai_client): + # Arrange + task = "A painting of a dog" + expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + mock_openai_client.images.generate.return_value = Mock(data=[Mock(url=expected_img_url)]) + + # Act + img_url = dalle3(task) + + # Assert + assert img_url == expected_img_url + mock_openai_client.images.generate.assert_called_once_with(prompt=task, n=4) + + +def test_dalle3_call_failure(dalle3, mock_openai_client, capsys): + # Arrange + task = "Invalid task" + expected_error_message = "Error running Dalle3: API Error" + + # Mocking OpenAIError + mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + + # Act and assert + with pytest.raises(OpenAIError) as excinfo: + dalle3(task) + + assert str(excinfo.value) == expected_error_message + mock_openai_client.images.generate.assert_called_once_with(prompt=task, n=4) + + # Ensure the error message is printed in red + captured = capsys.readouterr() + assert colored(expected_error_message, "red") in captured.out + + +def test_dalle3_create_variations_success(dalle3, mock_openai_client): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + mock_openai_client.images.create_variation.return_value = Mock(data=[Mock(url=expected_variation_url)]) + + # Act + variation_img_url = dalle3.create_variations(img_url) + + # Assert + assert variation_img_url == expected_variation_url + mock_openai_client.images.create_variation.assert_called_once() + _, kwargs = mock_openai_client.images.create_variation.call_args + assert kwargs["img"] is not None + assert kwargs["n"] == 4 + assert kwargs["size"] == "1024x1024" + + +def test_dalle3_create_variations_failure(dalle3, mock_openai_client, capsys): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_error_message = "Error running Dalle3: API Error" + + # Mocking OpenAIError + mock_openai_client.images.create_variation.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + + # Act and assert + with pytest.raises(OpenAIError) as excinfo: + dalle3.create_variations(img_url) + + assert str(excinfo.value) == expected_error_message + mock_openai_client.images.create_variation.assert_called_once() + + # Ensure the error message is printed in red + captured = capsys.readouterr() + assert colored(expected_error_message, "red") in captured.out + + +def test_dalle3_read_img(): + # Arrange + img_path = "test_image.png" + img = Image.new("RGB", (512, 512)) + + # Save the image temporarily + img.save(img_path) + + # Act + dalle3 = Dalle3() + img_loaded = dalle3.read_img(img_path) + + # Assert + assert isinstance(img_loaded, Image.Image) + + # Clean up + os.remove(img_path) + + +def test_dalle3_set_width_height(): + # Arrange + img = Image.new("RGB", (512, 512)) + width = 256 + height = 256 + + # Act + dalle3 = Dalle3() + img_resized = dalle3.set_width_height(img, width, height) + + # Assert + assert img_resized.size == (width, height) + + +def test_dalle3_convert_to_bytesio(): + # Arrange + img = Image.new("RGB", (512, 512)) + expected_format = "PNG" + + # Act + dalle3 = Dalle3() + img_bytes = dalle3.convert_to_bytesio(img, format=expected_format) + + # Assert + assert isinstance(img_bytes, bytes) + assert img_bytes.startswith(b"\x89PNG") + + +def test_dalle3_call_multiple_times(dalle3, mock_openai_client): + # Arrange + task = "A painting of a dog" + expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + mock_openai_client.images.generate.return_value = Mock(data=[Mock(url=expected_img_url)]) + + # Act + img_url1 = dalle3(task) + img_url2 = dalle3(task) + + # Assert + assert img_url1 == expected_img_url + assert img_url2 == expected_img_url + assert mock_openai_client.images.generate.call_count == 2 + + +def test_dalle3_call_with_large_input(dalle3, mock_openai_client): + # Arrange + task = "A" * 2048 # Input longer than API's limit + expected_error_message = "Error running Dalle3: API Error" + mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + + # Act and assert + with pytest.raises(OpenAIError) as excinfo: + dalle3(task) + + assert str(excinfo.value) == expected_error_message + + +def test_dalle3_create_variations_with_invalid_image_url(dalle3, mock_openai_client): + # Arrange + img_url = "https://invalid-image-url.com" + expected_error_message = "Error running Dalle3: Invalid image URL" + + # Act and assert + with pytest.raises(ValueError) as excinfo: + dalle3.create_variations(img_url) + + assert str(excinfo.value) == expected_error_message + + +def test_dalle3_set_width_height_invalid_dimensions(dalle3): + # Arrange + img = dalle3.read_img("test_image.png") + width = 0 + height = -1 + + # Act and assert + with pytest.raises(ValueError): + dalle3.set_width_height(img, width, height) + + +def test_dalle3_convert_to_bytesio_invalid_format(dalle3): + # Arrange + img = dalle3.read_img("test_image.png") + invalid_format = "invalid_format" + + # Act and assert + with pytest.raises(ValueError): + dalle3.convert_to_bytesio(img, format=invalid_format) + + +def test_dalle3_call_with_retry(dalle3, mock_openai_client): + # Arrange + task = "A painting of a dog" + expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + + # Simulate a retry scenario + mock_openai_client.images.generate.side_effect = [ + OpenAIError("Temporary error", http_status=500, error="Internal Server Error"), + Mock(data=[Mock(url=expected_img_url)]), + ] + + # Act + img_url = dalle3(task) + + # Assert + assert img_url == expected_img_url + assert mock_openai_client.images.generate.call_count == 2 + + +def test_dalle3_create_variations_with_retry(dalle3, mock_openai_client): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + + # Simulate a retry scenario + mock_openai_client.images.create_variation.side_effect = [ + OpenAIError("Temporary error", http_status=500, error="Internal Server Error"), + Mock(data=[Mock(url=expected_variation_url)]), + ] + + # Act + variation_img_url = dalle3.create_variations(img_url) + + # Assert + assert variation_img_url == expected_variation_url + assert mock_openai_client.images.create_variation.call_count == 2 + + +def test_dalle3_call_exception_logging(dalle3, mock_openai_client, capsys): + # Arrange + task = "A painting of a dog" + expected_error_message = "Error running Dalle3: API Error" + + # Mocking OpenAIError + mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + + # Act + with pytest.raises(OpenAIError): + dalle3(task) + + # Assert that the error message is logged + captured = capsys.readouterr() + assert expected_error_message in captured.err + + +def test_dalle3_create_variations_exception_logging(dalle3, mock_openai_client, capsys): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_error_message = "Error running Dalle3: API Error" + + # Mocking OpenAIError + mock_openai_client.images.create_variation.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + + # Act + with pytest.raises(OpenAIError): + dalle3.create_variations(img_url) + + # Assert that the error message is logged + captured = capsys.readouterr() + assert expected_error_message in captured.err + + +def test_dalle3_read_img_invalid_path(dalle3): + # Arrange + invalid_img_path = "invalid_image_path.png" + + # Act and assert + with pytest.raises(FileNotFoundError): + dalle3.read_img(invalid_img_path) + + +def test_dalle3_call_no_api_key(): + # Arrange + task = "A painting of a dog" + dalle3 = Dalle3(api_key=None) + expected_error_message = "Error running Dalle3: API Key is missing" + + # Act and assert + with pytest.raises(ValueError) as excinfo: + dalle3(task) + + assert str(excinfo.value) == expected_error_message + + +def test_dalle3_create_variations_no_api_key(): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + dalle3 = Dalle3(api_key=None) + expected_error_message = "Error running Dalle3: API Key is missing" + + # Act and assert + with pytest.raises(ValueError) as excinfo: + dalle3.create_variations(img_url) + + assert str(excinfo.value) == expected_error_message + + +def test_dalle3_call_with_retry_max_retries_exceeded(dalle3, mock_openai_client): + # Arrange + task = "A painting of a dog" + + # Simulate max retries exceeded + mock_openai_client.images.generate.side_effect = OpenAIError("Temporary error", http_status=500, error="Internal Server Error") + + # Act and assert + with pytest.raises(OpenAIError) as excinfo: + dalle3(task) + + assert "Retry limit exceeded" in str(excinfo.value) + + +def test_dalle3_create_variations_with_retry_max_retries_exceeded(dalle3, mock_openai_client): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + + # Simulate max retries exceeded + mock_openai_client.images.create_variation.side_effect = OpenAIError("Temporary error", http_status=500, error="Internal Server Error") + + # Act and assert + with pytest.raises(OpenAIError) as excinfo: + dalle3.create_variations(img_url) + + assert "Retry limit exceeded" in str(excinfo.value) + + +def test_dalle3_call_retry_with_success(dalle3, mock_openai_client): + # Arrange + task = "A painting of a dog" + expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + + # Simulate success after a retry + mock_openai_client.images.generate.side_effect = [ + OpenAIError("Temporary error", http_status=500, error="Internal Server Error"), + Mock(data=[Mock(url=expected_img_url)]), + ] + + # Act + img_url = dalle3(task) + + # Assert + assert img_url == expected_img_url + assert mock_openai_client.images.generate.call_count == 2 + + +def test_dalle3_create_variations_retry_with_success(dalle3, mock_openai_client): + # Arrange + img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + + # Simulate success after a retry + mock_openai_client.images.create_variation.side_effect = [ + OpenAIError("Temporary error", http_status=500, error="Internal Server Error"), + Mock(data=[Mock(url=expected_variation_url)]), + ] + + # Act + variation_img_url = dalle3.create_variations(img_url) + + # Assert + assert variation_img_url == expected_variation_url + assert mock_openai_client.images.create_variation.call_count == 2 diff --git a/tests/models/gpt4v.py b/tests/models/gpt4v.py new file mode 100644 index 00000000..40ccc7f5 --- /dev/null +++ b/tests/models/gpt4v.py @@ -0,0 +1,321 @@ +import logging +import os +from unittest.mock import Mock + +import pytest +from dotenv import load_dotenv +from requests.exceptions import ConnectionError, HTTPError, RequestException, Timeout + +from swarms.models.gpt4v import GPT4Vision, GPT4VisionResponse + +load_dotenv + +api_key = os.getenv("OPENAI_API_KEY") + +# Mock the OpenAI client +@pytest.fixture +def mock_openai_client(): + return Mock() + +@pytest.fixture +def gpt4vision(mock_openai_client): + return GPT4Vision(client=mock_openai_client) + +def test_gpt4vision_default_values(): + # Arrange and Act + gpt4vision = GPT4Vision() + + # Assert + assert gpt4vision.max_retries == 3 + assert gpt4vision.model == "gpt-4-vision-preview" + assert gpt4vision.backoff_factor == 2.0 + assert gpt4vision.timeout_seconds == 10 + assert gpt4vision.api_key is None + assert gpt4vision.quality == "low" + assert gpt4vision.max_tokens == 200 + +def test_gpt4vision_api_key_from_env_variable(): + # Arrange + api_key = os.environ["OPENAI_API_KEY"] + + # Act + gpt4vision = GPT4Vision() + + # Assert + assert gpt4vision.api_key == api_key + +def test_gpt4vision_set_api_key(): + # Arrange + gpt4vision = GPT4Vision(api_key=api_key) + + # Assert + assert gpt4vision.api_key == api_key + +def test_gpt4vision_invalid_max_retries(): + # Arrange and Act + with pytest.raises(ValueError): + GPT4Vision(max_retries=-1) + +def test_gpt4vision_invalid_backoff_factor(): + # Arrange and Act + with pytest.raises(ValueError): + GPT4Vision(backoff_factor=-1) + +def test_gpt4vision_invalid_timeout_seconds(): + # Arrange and Act + with pytest.raises(ValueError): + GPT4Vision(timeout_seconds=-1) + +def test_gpt4vision_invalid_max_tokens(): + # Arrange and Act + with pytest.raises(ValueError): + GPT4Vision(max_tokens=-1) + +def test_gpt4vision_logger_initialized(): + # Arrange + gpt4vision = GPT4Vision() + + # Assert + assert isinstance(gpt4vision.logger, logging.Logger) + +def test_gpt4vision_process_img_nonexistent_file(): + # Arrange + gpt4vision = GPT4Vision() + img_path = "nonexistent_image.jpg" + + # Act and Assert + with pytest.raises(FileNotFoundError): + gpt4vision.process_img(img_path) + +def test_gpt4vision_call_single_task_single_image_no_openai_client(gpt4vision): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + # Act and Assert + with pytest.raises(AttributeError): + gpt4vision(img_url, [task]) + +def test_gpt4vision_call_single_task_single_image_empty_response(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + mock_openai_client.chat.completions.create.return_value.choices = [] + + # Act + response = gpt4vision(img_url, [task]) + + # Assert + assert response.answer == "" + mock_openai_client.chat.completions.create.assert_called_once() + +def test_gpt4vision_call_multiple_tasks_single_image_empty_responses(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + tasks = ["Describe this image.", "What's in this picture?"] + + mock_openai_client.chat.completions.create.return_value.choices = [] + + # Act + responses = gpt4vision(img_url, tasks) + + # Assert + assert all(response.answer == "" for response in responses) + assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + +def test_gpt4vision_call_single_task_single_image_timeout(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + mock_openai_client.chat.completions.create.side_effect = Timeout("Request timed out") + + # Act and Assert + with pytest.raises(Timeout): + gpt4vision(img_url, [task]) + +def test_gpt4vision_call_retry_with_success_after_timeout(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + # Simulate success after a timeout and retry + mock_openai_client.chat.completions.create.side_effect = [ + Timeout("Request timed out"), + {"choices": [{"message": {"content": {"text": "A description of the image."}}}],} + ] + + # Act + response = gpt4vision(img_url, [task]) + + # Assert + assert response.answer == "A description of the image." + assert mock_openai_client.chat.completions.create.call_count == 2 # Should be called twice + + +def test_gpt4vision_process_img(): + # Arrange + img_path = "test_image.jpg" + gpt4vision = GPT4Vision() + + # Act + img_data = gpt4vision.process_img(img_path) + + # Assert + assert img_data.startswith("/9j/") # Base64-encoded image data + + +def test_gpt4vision_call_single_task_single_image(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + expected_response = GPT4VisionResponse(answer="A description of the image.") + + mock_openai_client.chat.completions.create.return_value.choices[0].text = expected_response.answer + + # Act + response = gpt4vision(img_url, [task]) + + # Assert + assert response == expected_response + mock_openai_client.chat.completions.create.assert_called_once() + + +def test_gpt4vision_call_single_task_multiple_images(gpt4vision, mock_openai_client): + # Arrange + img_urls = ["https://example.com/image1.jpg", "https://example.com/image2.jpg"] + task = "Describe these images." + + expected_response = GPT4VisionResponse(answer="Descriptions of the images.") + + mock_openai_client.chat.completions.create.return_value.choices[0].text = expected_response.answer + + # Act + response = gpt4vision(img_urls, [task]) + + # Assert + assert response == expected_response + mock_openai_client.chat.completions.create.assert_called_once() + + +def test_gpt4vision_call_multiple_tasks_single_image(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + tasks = ["Describe this image.", "What's in this picture?"] + + expected_responses = [ + GPT4VisionResponse(answer="A description of the image."), + GPT4VisionResponse(answer="It contains various objects."), + ] + + def create_mock_response(response): + return {"choices": [{"message": {"content": {"text": response.answer}}}]} + + mock_openai_client.chat.completions.create.side_effect = [create_mock_response(response) for response in expected_responses] + + # Act + responses = gpt4vision(img_url, tasks) + + # Assert + assert responses == expected_responses + assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + def test_gpt4vision_call_multiple_tasks_single_image(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + tasks = ["Describe this image.", "What's in this picture?"] + + expected_responses = [ + GPT4VisionResponse(answer="A description of the image."), + GPT4VisionResponse(answer="It contains various objects."), + ] + + mock_openai_client.chat.completions.create.side_effect = [ + {"choices": [{"message": {"content": {"text": expected_responses[i].answer}}}] } for i in range(len(expected_responses)) + ] + + # Act + responses = gpt4vision(img_url, tasks) + + # Assert + assert responses == expected_responses + assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + + +def test_gpt4vision_call_multiple_tasks_multiple_images(gpt4vision, mock_openai_client): + # Arrange + img_urls = ["https://images.unsplash.com/photo-1694734479857-626882b6db37?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D", "https://images.unsplash.com/photo-1694734479898-6ac4633158ac?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"] + tasks = ["Describe these images.", "What's in these pictures?"] + + expected_responses = [ + GPT4VisionResponse(answer="Descriptions of the images."), + GPT4VisionResponse(answer="They contain various objects.") + ] + + mock_openai_client.chat.completions.create.side_effect = [ + {"choices": [{"message": {"content": {"text": response.answer}}}] } for response in expected_responses + ] + + # Act + responses = gpt4vision(img_urls, tasks) + + + # Assert + assert responses == expected_responses + assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + + +def test_gpt4vision_call_http_error(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + mock_openai_client.chat.completions.create.side_effect = HTTPError("HTTP Error") + + # Act and Assert + with pytest.raises(HTTPError): + gpt4vision(img_url, [task]) + + +def test_gpt4vision_call_request_error(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + mock_openai_client.chat.completions.create.side_effect = RequestException("Request Error") + + # Act and Assert + with pytest.raises(RequestException): + gpt4vision(img_url, [task]) + + +def test_gpt4vision_call_connection_error(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + mock_openai_client.chat.completions.create.side_effect = ConnectionError("Connection Error") + + # Act and Assert + with pytest.raises(ConnectionError): + gpt4vision(img_url, [task]) + + +def test_gpt4vision_call_retry_with_success(gpt4vision, mock_openai_client): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + task = "Describe this image." + + # Simulate success after a retry + mock_openai_client.chat.completions.create.side_effect = [ + RequestException("Temporary error"), + {"choices": [{"text": "A description of the image."}]} # fixed dictionary syntax + ] + + # Act + response = gpt4vision(img_url, [task]) + + # Assert + assert response.answer == "A description of the image." + assert mock_openai_client.chat.completions.create.call_count == 2 # Should be called twice diff --git a/tests/structs/sequential_workflow.py b/tests/structs/sequential_workflow.py index 64b51f28..7bd3e4a4 100644 --- a/tests/structs/sequential_workflow.py +++ b/tests/structs/sequential_workflow.py @@ -12,7 +12,6 @@ from swarms.structs.sequential_workflow import SequentialWorkflow, Task os.environ["OPENAI_API_KEY"] = "mocked_api_key" - # Mock OpenAIChat class for testing class MockOpenAIChat: def __init__(self, *args, **kwargs): @@ -21,6 +20,7 @@ class MockOpenAIChat: def run(self, *args, **kwargs): return "Mocked result" + # Mock Flow class for testing class MockFlow: def __init__(self, *args, **kwargs): @@ -29,6 +29,7 @@ class MockFlow: def run(self, *args, **kwargs): return "Mocked result" + # Mock SequentialWorkflow class for testing class MockSequentialWorkflow: def __init__(self, *args, **kwargs): @@ -40,6 +41,7 @@ class MockSequentialWorkflow: def run(self): pass + # Test Task class def test_task_initialization(): description = "Sample Task" @@ -48,6 +50,7 @@ def test_task_initialization(): assert task.description == description assert task.flow == flow + def test_task_execute(): description = "Sample Task" flow = MockOpenAIChat() @@ -55,6 +58,7 @@ def test_task_execute(): task.execute() assert task.result == "Mocked result" + # Test SequentialWorkflow class def test_sequential_workflow_initialization(): workflow = SequentialWorkflow() @@ -66,6 +70,7 @@ def test_sequential_workflow_initialization(): assert workflow.restore_state_filepath == None assert workflow.dashboard == False + def test_sequential_workflow_add_task(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -75,6 +80,7 @@ def test_sequential_workflow_add_task(): assert workflow.tasks[0].description == task_description assert workflow.tasks[0].flow == task_flow + def test_sequential_workflow_reset_workflow(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -83,6 +89,7 @@ def test_sequential_workflow_reset_workflow(): workflow.reset_workflow() assert workflow.tasks[0].result == None + def test_sequential_workflow_get_task_results(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -94,6 +101,7 @@ def test_sequential_workflow_get_task_results(): assert task_description in results assert results[task_description] == "Mocked result" + def test_sequential_workflow_remove_task(): workflow = SequentialWorkflow() task1_description = "Task 1" @@ -106,6 +114,7 @@ def test_sequential_workflow_remove_task(): assert len(workflow.tasks) == 1 assert workflow.tasks[0].description == task2_description + def test_sequential_workflow_update_task(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -114,6 +123,7 @@ def test_sequential_workflow_update_task(): workflow.update_task(task_description, max_tokens=1000) assert workflow.tasks[0].kwargs["max_tokens"] == 1000 + def test_sequential_workflow_save_workflow_state(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -123,6 +133,7 @@ def test_sequential_workflow_save_workflow_state(): assert os.path.exists("test_state.json") os.remove("test_state.json") + def test_sequential_workflow_load_workflow_state(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -134,6 +145,7 @@ def test_sequential_workflow_load_workflow_state(): assert workflow.tasks[0].description == task_description os.remove("test_state.json") + def test_sequential_workflow_run(): workflow = SequentialWorkflow() task_description = "Sample Task" @@ -142,18 +154,21 @@ def test_sequential_workflow_run(): workflow.run() assert workflow.tasks[0].result == "Mocked result" + def test_sequential_workflow_workflow_bootup(capfd): workflow = SequentialWorkflow() workflow.workflow_bootup() out, _ = capfd.readouterr() assert "Sequential Workflow Initializing..." in out + def test_sequential_workflow_workflow_dashboard(capfd): workflow = SequentialWorkflow() workflow.workflow_dashboard() out, _ = capfd.readouterr() assert "Sequential Workflow Dashboard" in out + # Mock Flow class for async testing class MockAsyncFlow: def __init__(self, *args, **kwargs): @@ -162,6 +177,7 @@ class MockAsyncFlow: async def arun(self, *args, **kwargs): return "Mocked result" + # Test async execution in SequentialWorkflow @pytest.mark.asyncio async def test_sequential_workflow_arun(): @@ -173,23 +189,24 @@ async def test_sequential_workflow_arun(): assert workflow.tasks[0].result == "Mocked result" - - def test_real_world_usage_with_openai_key(): # Initialize the language model llm = OpenAIChat() assert isinstance(llm, OpenAIChat) + def test_real_world_usage_with_flow_and_openai_key(): # Initialize a flow with the language model flow = Flow(llm=OpenAIChat()) assert isinstance(flow, Flow) + def test_real_world_usage_with_sequential_workflow(): # Initialize a sequential workflow workflow = SequentialWorkflow() assert isinstance(workflow, SequentialWorkflow) + def test_real_world_usage_add_tasks(): # Create a sequential workflow and add tasks workflow = SequentialWorkflow() @@ -203,6 +220,7 @@ def test_real_world_usage_add_tasks(): assert workflow.tasks[0].description == task1_description assert workflow.tasks[1].description == task2_description + def test_real_world_usage_run_workflow(): # Create a sequential workflow, add a task, and run the workflow workflow = SequentialWorkflow() @@ -212,6 +230,7 @@ def test_real_world_usage_run_workflow(): workflow.run() assert workflow.tasks[0].result is not None + def test_real_world_usage_dashboard_display(): # Create a sequential workflow, add tasks, and display the dashboard workflow = SequentialWorkflow() @@ -225,6 +244,7 @@ def test_real_world_usage_dashboard_display(): workflow.workflow_dashboard() mock_print.assert_called() + def test_real_world_usage_async_execution(): # Create a sequential workflow, add an async task, and run the workflow asynchronously workflow = SequentialWorkflow() @@ -238,6 +258,7 @@ def test_real_world_usage_async_execution(): asyncio.run(async_run_workflow()) assert workflow.tasks[0].result is not None + def test_real_world_usage_multiple_loops(): # Create a sequential workflow with multiple loops, add a task, and run the workflow workflow = SequentialWorkflow(max_loops=3) @@ -247,6 +268,7 @@ def test_real_world_usage_multiple_loops(): workflow.run() assert workflow.tasks[0].result is not None + def test_real_world_usage_autosave_state(): # Create a sequential workflow with autosave, add a task, run the workflow, and check if state is saved workflow = SequentialWorkflow(autosave=True) @@ -258,6 +280,7 @@ def test_real_world_usage_autosave_state(): assert os.path.exists("sequential_workflow_state.json") os.remove("sequential_workflow_state.json") + def test_real_world_usage_load_state(): # Create a sequential workflow, add a task, save state, load state, and run the workflow workflow = SequentialWorkflow() @@ -271,6 +294,7 @@ def test_real_world_usage_load_state(): assert workflow.tasks[0].result is not None os.remove("test_state.json") + def test_real_world_usage_update_task_args(): # Create a sequential workflow, add a task, and update task arguments workflow = SequentialWorkflow() @@ -280,6 +304,7 @@ def test_real_world_usage_update_task_args(): workflow.update_task(task_description, max_tokens=1000) assert workflow.tasks[0].kwargs["max_tokens"] == 1000 + def test_real_world_usage_remove_task(): # Create a sequential workflow, add tasks, remove a task, and run the workflow workflow = SequentialWorkflow() @@ -294,13 +319,15 @@ def test_real_world_usage_remove_task(): assert len(workflow.tasks) == 1 assert workflow.tasks[0].description == task2_description + def test_real_world_usage_with_environment_variables(): # Ensure that the OpenAI API key is set using environment variables assert "OPENAI_API_KEY" in os.environ assert os.environ["OPENAI_API_KEY"] == "mocked_api_key" del os.environ["OPENAI_API_KEY"] # Clean up after the test + def test_real_world_usage_no_openai_key(): # Ensure that an exception is raised when the OpenAI API key is not set with pytest.raises(ValueError): - llm = OpenAIChat() # API key not provided, should raise an exception \ No newline at end of file + llm = OpenAIChat() # API key not provided, should raise an exception From ba35f84199b3db99bed7761573190b77c71e1d97 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 16:31:32 -0500 Subject: [PATCH 32/63] docs Former-commit-id: b7aa21f92b26c9442720ddafad1198d5021f2cf6 --- docs/swarms/models/dalle3.md | 261 +++++++++++++++++++++++++++++++++++ mkdocs.yml | 2 + 2 files changed, 263 insertions(+) create mode 100644 docs/swarms/models/dalle3.md diff --git a/docs/swarms/models/dalle3.md b/docs/swarms/models/dalle3.md new file mode 100644 index 00000000..ff12b130 --- /dev/null +++ b/docs/swarms/models/dalle3.md @@ -0,0 +1,261 @@ +# `Dalle3` Documentation + +## Table of Contents + +1. [Introduction](#introduction) +2. [Installation](#installation) +3. [Quick Start](#quick-start) +4. [Dalle3 Class](#dalle3-class) + - [Attributes](#attributes) + - [Methods](#methods) +5. [Usage Examples](#usage-examples) +6. [Error Handling](#error-handling) +7. [Advanced Usage](#advanced-usage) +8. [References](#references) + +--- + +## Introduction + +The Dalle3 library is a Python module that provides an easy-to-use interface for generating images from text descriptions using the DALLยทE 3 model by OpenAI. DALLยทE 3 is a powerful language model capable of converting textual prompts into images. This documentation will guide you through the installation, setup, and usage of the Dalle3 library. + +--- + +## Installation + +To install the Dalle3 library, you can use pip: + +```bash +pip install dalle3 +``` + +--- + +## Quick Start + +Let's get started with a quick example of using the Dalle3 library to generate an image from a text prompt: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class +dalle = Dalle3() + +# Define a text prompt +task = "A painting of a dog" + +# Generate an image from the text prompt +image_url = dalle3(task) + +# Print the generated image URL +print(image_url) +``` + +This example demonstrates the basic usage of the Dalle3 library to convert a text prompt into an image. The generated image URL will be printed to the console. + +--- + +## Dalle3 Class + +The Dalle3 library provides a `Dalle3` class that allows you to interact with the DALLยทE 3 model. This class has several attributes and methods for generating images from text prompts. + +### Attributes + +- `model` (str): The name of the DALLยทE 3 model. Default: "dall-e-3". +- `img` (str): The image URL generated by the Dalle3 API. +- `size` (str): The size of the generated image. Default: "1024x1024". +- `max_retries` (int): The maximum number of API request retries. Default: 3. +- `quality` (str): The quality of the generated image. Default: "standard". +- `n` (int): The number of variations to create. Default: 4. + +### Methods + +#### `__call__(self, task: str) -> Dalle3` + +This method makes a call to the Dalle3 API and returns the image URL generated from the provided text prompt. + +Parameters: +- `task` (str): The text prompt to be converted to an image. + +Returns: +- `Dalle3`: An instance of the Dalle3 class with the image URL generated by the Dalle3 API. + +#### `create_variations(self, img: str)` + +This method creates variations of an image using the Dalle3 API. + +Parameters: +- `img` (str): The image to be used for the API request. + +Returns: +- `img` (str): The image URL of the generated variations. + +--- + +## Usage Examples + +### Example 1: Basic Image Generation + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class +dalle3 = Dalle3() + +# Define a text prompt +task = "A painting of a dog" + +# Generate an image from the text prompt +image_url = dalle3(task) + +# Print the generated image URL +print(image_url) +``` + +### Example 2: Creating Image Variations + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class +dalle3 = Dalle3() + +# Define the URL of an existing image +img_url = "https://images.unsplash.com/photo-1694734479898-6ac4633158ac?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D + +# Create variations of the image +variations_url = dalle3.create_variations(img_url) + +# Print the URLs of the generated variations +print(variations_url) +``` + +Certainly! Here are additional examples that cover various edge cases and methods of the `Dalle3` class in the Dalle3 library: + +### Example 3: Customizing Image Size + +You can customize the size of the generated image by specifying the `size` parameter when creating an instance of the `Dalle3` class. Here's how to generate a smaller image: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class with a custom image size +dalle3 = Dalle3(size="512x512") + +# Define a text prompt +task = "A small painting of a cat" + +# Generate a smaller image from the text prompt +image_url = dalle3(task) + +# Print the generated image URL +print(image_url) +``` + +### Example 4: Adjusting Retry Limit + +You can adjust the maximum number of API request retries using the `max_retries` parameter. Here's how to increase the retry limit: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class with a higher retry limit +dalle3 = Dalle3(max_retries=5) + +# Define a text prompt +task = "An image of a landscape" + +# Generate an image with a higher retry limit +image_url = dalle3(task) + +# Print the generated image URL +print(image_url) +``` + +### Example 5: Generating Image Variations + +To create variations of an existing image, you can use the `create_variations` method. Here's an example: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class +dalle3 = Dalle3() + +# Define the URL of an existing image +img_url = "https://images.unsplash.com/photo-1677290043066-12eccd944004?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + +# Create variations of the image +variations_url = dalle3.create_variations(img_url) + +# Print the URLs of the generated variations +print(variations_url) +``` + +### Example 6: Handling API Errors + +The Dalle3 library provides error handling for API-related issues. Here's how to handle and display API errors: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class +dalle3 = Dalle3() + +# Define a text prompt +task = "Invalid prompt that may cause an API error" + +try: + # Attempt to generate an image with an invalid prompt + image_url = dalle3(task) + print(image_url) +except Exception as e: + print(f"Error occurred: {str(e)}") +``` + +### Example 7: Customizing Image Quality + +You can customize the quality of the generated image by specifying the `quality` parameter. Here's how to generate a high-quality image: + +```python +from swarms.models.dalle3 import Dalle3 + +# Create an instance of the Dalle3 class with high quality +dalle3 = Dalle3(quality="high") + +# Define a text prompt +task = "A high-quality image of a sunset" + +# Generate a high-quality image from the text prompt +image_url = dalle3(task) + +# Print the generated image URL +print(image_url) +``` + + +--- + +## Error Handling + +The Dalle3 library provides error handling for API-related issues. If an error occurs during API communication, the library will handle it and provide detailed error messages. Make sure to handle exceptions appropriately in your code. + +--- + +## Advanced Usage + +For advanced usage and customization of the Dalle3 library, you can explore the attributes and methods of the `Dalle3` class. Adjusting parameters such as `size`, `max_retries`, and `quality` allows you to fine-tune the image generation process to your specific needs. + +--- + +## References + +For more information about the DALLยทE 3 model and the Dalle3 library, you can refer to the official OpenAI documentation and resources. + +- [OpenAI API Documentation](https://beta.openai.com/docs/) +- [DALLยทE 3 Model Information](https://openai.com/research/dall-e-3) +- [Dalle3 GitHub Repository](https://github.com/openai/dall-e-3) + +--- + +This concludes the documentation for the Dalle3 library. You can now use the library to generate images from text prompts and explore its advanced features for various applications. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 58430091..7413e809 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -92,6 +92,8 @@ nav: - BingChat: "swarms/models/bingchat.md" - Kosmos: "swarms/models/kosmos.md" - Nougat: "swarms/models/nougat.md" + - Dalle3: "swarms/models/dalle3.md" + - GPT4V: "swarms/models/gpt4v.md" - LayoutLMDocumentQA: "swarms/models/layoutlm_document_qa.md" - DistilWhisperModel: "swarms/models/distilled_whisperx.md" - swarms.structs: From e6dcbce7de7339ad21b54231201b60e1cedbeedb Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 16:32:27 -0500 Subject: [PATCH 33/63] docs for gpt4v Former-commit-id: c7d128d8601a007dc89bfc0589d26da4138606c2 --- docs/swarms/models/gpt4v.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/swarms/models/gpt4v.md b/docs/swarms/models/gpt4v.md index 2af4348b..3fe3d81c 100644 --- a/docs/swarms/models/gpt4v.md +++ b/docs/swarms/models/gpt4v.md @@ -1,4 +1,4 @@ -# GPT4Vision Documentation +# `GPT4Vision` Documentation ## Table of Contents - [Overview](#overview) From 77b93eee62dbc6272992d33631db94d9d1bc4f33 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 16:49:10 -0500 Subject: [PATCH 34/63] dalle3 Former-commit-id: 4fb38c1c62d39cfdfe8c0d45f51ae9523eadeee5 --- docs/swarms/models/dalle3.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/swarms/models/dalle3.md b/docs/swarms/models/dalle3.md index ff12b130..346489c7 100644 --- a/docs/swarms/models/dalle3.md +++ b/docs/swarms/models/dalle3.md @@ -23,10 +23,10 @@ The Dalle3 library is a Python module that provides an easy-to-use interface for ## Installation -To install the Dalle3 library, you can use pip: +To use the Dalle3 model, you must first install swarms: ```bash -pip install dalle3 +pip install swarms ``` --- From b3a39e578d5410c7f7324e622eed1a16e467c668 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 17:25:05 -0500 Subject: [PATCH 35/63] Dependencies clean up Former-commit-id: a70a2b05b5c7ba27bdc27a00678e097a8432964a --- README.md | 30 +++++++++++++++++++++++------- pyproject.toml | 5 +---- swarms/models/__init__.py | 4 ++++ swarms/models/dalle3.py | 19 +++++++++---------- 4 files changed, 37 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index f94221d4..a80a307a 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ We have a small gallery of examples to run here, [for more check out the docs to ### `Flow` Example - The `Flow` is a superior iteratioin of the `LLMChain` from Langchain, our intent with `Flow` is to create the most reliable loop structure that gives the agents their "autonomy" through 3 main methods of interaction, one through user specified loops, then dynamic where the agent parses a token, and or an interactive human input verison, or a mix of all 3. + ```python from swarms.models import OpenAIChat @@ -47,22 +48,37 @@ from swarms.structs import Flow api_key = "" - -# Initialize the language model, -# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC +# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( + # model_name="gpt-4" openai_api_key=api_key, temperature=0.5, + # max_tokens=100, ) -# Initialize the flow +## Initialize the workflow flow = Flow( llm=llm, - max_loops=5, + max_loops=2, + dashboard=True, + # stopping_condition=None, # You can define a stopping condition as needed. + # loop_interval=1, + # retry_attempts=3, + # retry_interval=1, + # interactive=False, # Set to 'True' for interactive mode. + # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. ) -out = flow.run("Generate a 10,000 word blog, say Stop when done") -print(out) +# out = flow.load_state("flow_state.json") +# temp = flow.dynamic_temperature() +# filter = flow.add_response_filter("Trump") +out = flow.run("Generate a 10,000 word blog on health and wellness.") +# out = flow.validate_response(out) +# out = flow.analyze_feedback(out) +# out = flow.print_history_and_memory() +# # out = flow.save_state("flow_state.json") +# print(out) + ``` diff --git a/pyproject.toml b/pyproject.toml index e3a29e78..d8a561bd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "1.9.5" +version = "1.9.6" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] @@ -28,7 +28,6 @@ openai = "*" langchain = "*" asyncio = "*" nest_asyncio = "*" -pegasusx = "*" einops = "*" google-generativeai = "*" torch = "*" @@ -48,10 +47,8 @@ beautifulsoup4 = "*" huggingface-hub = "*" pydantic = "*" tenacity = "*" -redis = "*" Pillow = "*" chromadb = "*" -agent-protocol = "*" open-interpreter = "*" tabulate = "*" termcolor = "*" diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index a0bec07f..b2a2b433 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -16,6 +16,8 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA +from swarms.models.gpt4v import GPT4Vision +from swarms.models.dalle3 import Dalle3 # from swarms.models.distilled_whisperx import DistilWhisperModel @@ -43,4 +45,6 @@ __all__ = [ "HuggingfaceLLM", "MPT7B", "WizardLLMStoryTeller", + "GPT4Vision", + "Dalle3", ] diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py index f22b11e0..73edf502 100644 --- a/swarms/models/dalle3.py +++ b/swarms/models/dalle3.py @@ -1,15 +1,14 @@ -import openai import logging import os from dataclasses import dataclass -from functools import lru_cache -from termcolor import colored -from openai import OpenAI -from dotenv import load_dotenv -from pydantic import BaseModel, validator -from PIL import Image from io import BytesIO +import openai +from dotenv import load_dotenv +from openai import OpenAI +from PIL import Image +from pydantic import validator +from termcolor import colored load_dotenv() @@ -111,10 +110,10 @@ class Dalle3: try: # Making a call to the the Dalle3 API response = self.client.images.generate( - # model=self.model, + model=self.model, prompt=task, - # size=self.size, - # quality=self.quality, + size=self.size, + quality=self.quality, n=self.n, ) # Extracting the image url from the response From 690433d39f7acdae0e1c5b9d227d8ba5e9d82fd6 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 17:43:50 -0500 Subject: [PATCH 36/63] playground + flow docs fix Former-commit-id: 336bffea19feff85db6e6b9035d687cdc09a4b0c --- docs/swarms/structs/flow.md | 11 ++- .../agents/simple_agent.py | 0 playground/models/multitemp.py | 56 ------------- playground/models/openai_model.py | 4 +- playground/structs/flow.py | 35 ++++++++ playground/structs/sequential_workflow.py | 31 +++++++ playground/swarms/godmode.py | 39 ++------- playground/swarms/groupchat.py | 84 ++++++++----------- 8 files changed, 120 insertions(+), 140 deletions(-) rename simple_agent.py => playground/agents/simple_agent.py (100%) delete mode 100644 playground/models/multitemp.py create mode 100644 playground/structs/sequential_workflow.py diff --git a/docs/swarms/structs/flow.md b/docs/swarms/structs/flow.md index 9300c632..13f0541c 100644 --- a/docs/swarms/structs/flow.md +++ b/docs/swarms/structs/flow.md @@ -108,8 +108,13 @@ Here are three usage examples: ```python from swarms.structs import Flow +# Select any Language model from the models folder +from swarms.models import Mistral, OpenAIChat -flow = Flow(llm=my_language_model, max_loops=5) +llm = Mistral() +# llm = OpenAIChat() + +flow = Flow(llm=llm, max_loops=5) # Define a starting task or message initial_task = "Generate an long form analysis on the transformer model architecture." @@ -126,7 +131,7 @@ from swarms.structs import Flow def stop_when_repeats(response: str) -> bool: return "Stop" in response.lower() -flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats) +flow = Flow(llm=llm, max_loops=5, stopping_condition=stop_when_repeats) ``` ### Example 3: Interactive Conversation @@ -134,7 +139,7 @@ flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_rep ```python from swarms.structs import Flow -flow = Flow(llm=my_language_model, max_loops=5, interactive=True) +flow = Flow(llm=llm, max_loops=5, interactive=True) # Provide initial task initial_task = "Rank and prioritize the following financial documents and cut out 30% of our expenses" diff --git a/simple_agent.py b/playground/agents/simple_agent.py similarity index 100% rename from simple_agent.py rename to playground/agents/simple_agent.py diff --git a/playground/models/multitemp.py b/playground/models/multitemp.py deleted file mode 100644 index f4146390..00000000 --- a/playground/models/multitemp.py +++ /dev/null @@ -1,56 +0,0 @@ -from swarms.models import OpenAIChat # Replace with your actual OpenAIChat import - -if __name__ == "__main__": - api_key = "" # Your OpenAI API key here - agent = MultiTempAgent(api_key) - - prompt = "Write a blog post about health and wellness" - final_output = agent.run(prompt) - - print("Final chosen output:") - print(final_output) - - -class MultiTempAgent: - def __init__(self, api_key, default_temp=0.5, alt_temps=[0.2, 0.7, 0.9]): - self.api_key = api_key - self.default_temp = default_temp - self.alt_temps = alt_temps - - def ask_user_feedback(self, text): - print(f"Generated text: {text}") - feedback = input("Are you satisfied with this output? (yes/no): ") - return feedback.lower() == "yes" - - def present_options_to_user(self, outputs): - print("Alternative outputs:") - for temp, output in outputs.items(): - print(f"Temperature {temp}: {output}") - chosen_temp = float(input("Choose the temperature of the output you like: ")) - return outputs.get(chosen_temp, "Invalid temperature chosen.") - - def run(self, prompt): - try: - llm = OpenAIChat(openai_api_key=self.api_key, temperature=self.default_temp) - initial_output = llm(prompt) # Using llm as a callable - except Exception as e: - print(f"Error generating initial output: {e}") - initial_output = None - - user_satisfied = self.ask_user_feedback(initial_output) - - if user_satisfied: - return initial_output - else: - outputs = {} - for temp in self.alt_temps: - try: - llm = OpenAIChat( - openai_api_key=self.api_key, temperature=temp - ) # Re-initializing - outputs[temp] = llm(prompt) # Using llm as a callable - except Exception as e: - print(f"Error generating text at temperature {temp}: {e}") - outputs[temp] = None - chosen_output = self.present_options_to_user(outputs) - return chosen_output diff --git a/playground/models/openai_model.py b/playground/models/openai_model.py index eccbb8cc..e3b01715 100644 --- a/playground/models/openai_model.py +++ b/playground/models/openai_model.py @@ -1,6 +1,6 @@ from swarms.models.openai_models import OpenAIChat -openai = OpenAIChat(openai_api_key="", verbose=False) +openai = OpenAIChat(openai_api_key="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", verbose=False) -chat = openai("Are quantum fields everywhere?") +chat = openai("What are quantum fields?") print(chat) diff --git a/playground/structs/flow.py b/playground/structs/flow.py index e69de29b..8e34cce3 100644 --- a/playground/structs/flow.py +++ b/playground/structs/flow.py @@ -0,0 +1,35 @@ +from swarms.models import OpenAIChat +from swarms.structs import Flow + +api_key = "" + +# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC +llm = OpenAIChat( + # model_name="gpt-4" + openai_api_key=api_key, + temperature=0.5, + # max_tokens=100, +) + +## Initialize the workflow +flow = Flow( + llm=llm, + max_loops=2, + dashboard=True, + # stopping_condition=None, # You can define a stopping condition as needed. + # loop_interval=1, + # retry_attempts=3, + # retry_interval=1, + # interactive=False, # Set to 'True' for interactive mode. + # dynamic_temperature=False, # Set to 'True' for dynamic temperature handling. +) + +# out = flow.load_state("flow_state.json") +# temp = flow.dynamic_temperature() +# filter = flow.add_response_filter("Trump") +out = flow.run("Generate a 10,000 word blog on health and wellness.") +# out = flow.validate_response(out) +# out = flow.analyze_feedback(out) +# out = flow.print_history_and_memory() +# # out = flow.save_state("flow_state.json") +# print(out) diff --git a/playground/structs/sequential_workflow.py b/playground/structs/sequential_workflow.py new file mode 100644 index 00000000..b8e5a10b --- /dev/null +++ b/playground/structs/sequential_workflow.py @@ -0,0 +1,31 @@ +from swarms.models import OpenAIChat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# Example usage +llm = OpenAIChat( + temperature=0.5, + max_tokens=3000, +) + +# Initialize the Flow with the language flow +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create another Flow for a different task +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add("Generate a 10,000 word blog on health and wellness.", flow1) + +# Suppose the next task takes the output of the first task as input +workflow.add("Summarize the generated blog", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") diff --git a/playground/swarms/godmode.py b/playground/swarms/godmode.py index 66aec1fa..f1269d98 100644 --- a/playground/swarms/godmode.py +++ b/playground/swarms/godmode.py @@ -1,39 +1,16 @@ +from swarms.swarms import GodMode from swarms.models import OpenAIChat -from swarms.swarms import GodMode -from swarms.workers.worker import Worker +api_key = "" + +llm = OpenAIChat(openai_api_key=api_key) -llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5) -worker1 = Worker( - llm=llm, - ai_name="Bumble Bee", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) -worker2 = Worker( - llm=llm, - ai_name="Optimus Prime", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) -worker3 = Worker( - llm=llm, - ai_name="Megatron", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) -# Usage -agents = [worker1, worker2, worker3] +llms = [llm, llm, llm] -god_mode = GodMode(agents) +god_mode = GodMode(llms) -task = "What are the biggest risks facing humanity?" +task = "Generate a 10,000 word blog on health and wellness." +out = god_mode.run(task) god_mode.print_responses(task) diff --git a/playground/swarms/groupchat.py b/playground/swarms/groupchat.py index a5e8dd0d..739181d1 100644 --- a/playground/swarms/groupchat.py +++ b/playground/swarms/groupchat.py @@ -1,61 +1,49 @@ -from swarms.models import OpenAIChat -from swarms.swarms import GroupChat, GroupChatManager -from swarms.workers import Worker +from swarms import OpenAI, Flow +from swarms.swarms.groupchat import GroupChatManager, GroupChat -llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5) -node = Worker( - llm=llm, - ai_name="Optimus Prime", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, +api_key = "" + +llm = OpenAI( + openai_api_key=api_key, temperature=0.5, + max_tokens=3000, ) -node2 = Worker( +# Initialize the flow +flow1 = Flow( llm=llm, - ai_name="Optimus Prime", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, + max_loops=1, + system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE", + name="silly", + dashboard=True, ) - -node3 = Worker( +flow2 = Flow( llm=llm, - ai_name="Optimus Prime", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, + max_loops=1, + system_message="YOU ARE VERY SMART AND ANSWER RIDDLES", + name="detective", + dashboard=True, ) - -nodes = [node, node2, node3] - -messages = [ - { - "role": "system", - "context": "Create an a small feedforward in pytorch", - } -] - -group = GroupChat( - workers=nodes, - messages=messages, - max_rounds=3, +flow3 = Flow( + llm=llm, + max_loops=1, + system_message="YOU MAKE RIDDLES", + name="riddler", + dashboard=True, ) - - -manager = GroupChatManager( - groupchat=group, - max_consecutive_auto_reply=3, +manager = Flow( + llm=llm, + max_loops=1, + system_message="YOU ARE A GROUP CHAT MANAGER", + name="manager", + dashboard=True, ) -output = group.run( - messages, - sender=node, - config=group, -) -print(output) +# Example usage: +agents = [flow1, flow2, flow3] + +group_chat = GroupChat(agents=agents, messages=[], max_round=10) +chat_manager = GroupChatManager(groupchat=group_chat, selector=manager) +chat_history = chat_manager("Write me a riddle") From 0f81159e683f078543c0aba7efbe9b36744454dd Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 17:53:02 -0500 Subject: [PATCH 37/63] saved state in dashboard error Former-commit-id: 97aa8bc3a02fd6c66f104d62fd6c33e1dc856228 --- playground/models/openai_model.py | 2 +- pyproject.toml | 2 +- swarms/models/__init__.py | 8 ++++---- swarms/models/dalle3.py | 3 ++- swarms/models/gpt4v.py | 2 +- swarms/structs/flow.py | 2 +- 6 files changed, 10 insertions(+), 9 deletions(-) diff --git a/playground/models/openai_model.py b/playground/models/openai_model.py index e3b01715..3b9cb967 100644 --- a/playground/models/openai_model.py +++ b/playground/models/openai_model.py @@ -1,6 +1,6 @@ from swarms.models.openai_models import OpenAIChat -openai = OpenAIChat(openai_api_key="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", verbose=False) +openai = OpenAIChat(openai_api_key="", verbose=False) chat = openai("What are quantum fields?") print(chat) diff --git a/pyproject.toml b/pyproject.toml index d8a561bd..6aa8585d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "1.9.6" +version = "1.9.9" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index b2a2b433..dd21ba80 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -16,8 +16,8 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA -from swarms.models.gpt4v import GPT4Vision -from swarms.models.dalle3 import Dalle3 +# from swarms.models.gpt4v import GPT4Vision +# from swarms.models.dalle3 import Dalle3 # from swarms.models.distilled_whisperx import DistilWhisperModel @@ -45,6 +45,6 @@ __all__ = [ "HuggingfaceLLM", "MPT7B", "WizardLLMStoryTeller", - "GPT4Vision", - "Dalle3", + # "GPT4Vision", + # "Dalle3", ] diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py index 73edf502..2ac5d403 100644 --- a/swarms/models/dalle3.py +++ b/swarms/models/dalle3.py @@ -12,7 +12,7 @@ from termcolor import colored load_dotenv() -api_key = os.getenv("OPENAI_API_KEY") +# api_key = os.getenv("OPENAI_API_KEY") # Configure Logging logging.basicConfig(level=logging.INFO) @@ -49,6 +49,7 @@ class Dalle3: size: str = "1024x1024" max_retries: int = 3 quality: str = "standard" + api_key: str = None n: int = 4 client = OpenAI( api_key=api_key, diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py index a7f8f1c1..99580d82 100644 --- a/swarms/models/gpt4v.py +++ b/swarms/models/gpt4v.py @@ -73,7 +73,7 @@ class GPT4Vision: model: str = "gpt-4-vision-preview" backoff_factor: float = 2.0 timeout_seconds: int = 10 - api_key: Optional[str] = None or os.getenv("OPENAI_API_KEY") + api_key: Optional[str] = None # 'Low' or 'High' for respesctively fast or high quality, but high more token usage quality: str = "low" # Max tokens to use for the API request, the maximum might be 3,000 but we don't know diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 117172ea..4e21c3df 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -217,7 +217,7 @@ class Flow: Dashboard: {self.dashboard} Dynamic Temperature: {self.dynamic_temperature} Autosave: {self.autosave} - Saved State: {self.saved_state} + Saved State: {self.saved_state_path} ---------------------------------------- """, From f079e099af2b1b0ad88b992fadb2c3270c627938 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 18:18:57 -0500 Subject: [PATCH 38/63] anthropic docs Former-commit-id: 4c6bbad49b67603460ef80f4744192c2011e4e23 --- docs/prompt.txt | 93 ------------------- docs/swarms/models/anthropic.md | 11 ++- godmode.py | 16 ---- dalle3.py => playground/models/dalle3.py | 0 .../models/gpt4vision_example.py | 0 swarms/agents/idea_to_image_agent.py | 2 +- swarms/models/huggingface.py | 17 ++-- tests/models/dalle3.py | 2 +- 8 files changed, 19 insertions(+), 122 deletions(-) delete mode 100644 docs/prompt.txt delete mode 100644 godmode.py rename dalle3.py => playground/models/dalle3.py (100%) rename gpt4vision_example.py => playground/models/gpt4vision_example.py (100%) diff --git a/docs/prompt.txt b/docs/prompt.txt deleted file mode 100644 index 3644be4a..00000000 --- a/docs/prompt.txt +++ /dev/null @@ -1,93 +0,0 @@ -Create multi-page long and explicit professional pytorch-like documentation for the swarms code below follow the outline for the swarms library, provide many examples and teach the user about the code, provide examples for every function, make the documentation 10,000 words, provide many usage examples and note this is markdown docs, create the documentation for the code to document. - -Now make the professional documentation for this code, provide the architecture and how the class works and why it works that way, it's purpose, provide args, their types, 3 ways of usage examples, in examples use from shapeless import x - -BE VERY EXPLICIT AND THOROUGH, MAKE IT DEEP AND USEFUL - -######## -Step 1: Understand the purpose and functionality of the module or framework - -Read and analyze the description provided in the documentation to understand the purpose and functionality of the module or framework. -Identify the key features, parameters, and operations performed by the module or framework. -Step 2: Provide an overview and introduction - -Start the documentation by providing a brief overview and introduction to the module or framework. -Explain the importance and relevance of the module or framework in the context of the problem it solves. -Highlight any key concepts or terminology that will be used throughout the documentation. -Step 3: Provide a class or function definition - -Provide the class or function definition for the module or framework. -Include the parameters that need to be passed to the class or function and provide a brief description of each parameter. -Specify the data types and default values for each parameter. -Step 4: Explain the functionality and usage - -Provide a detailed explanation of how the module or framework works and what it does. -Describe the steps involved in using the module or framework, including any specific requirements or considerations. -Provide code examples to demonstrate the usage of the module or framework. -Explain the expected inputs and outputs for each operation or function. -Step 5: Provide additional information and tips - -Provide any additional information or tips that may be useful for using the module or framework effectively. -Address any common issues or challenges that developers may encounter and provide recommendations or workarounds. -Step 6: Include references and resources - -Include references to any external resources or research papers that provide further information or background on the module or framework. -Provide links to relevant documentation or websites for further exploration. -Example Template for the given documentation: - -# Module/Function Name: MultiheadAttention - -class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None): - """ - Creates a multi-head attention module for joint information representation from the different subspaces. - - Parameters: - - embed_dim (int): Total dimension of the model. - - num_heads (int): Number of parallel attention heads. The embed_dim will be split across num_heads. - - dropout (float): Dropout probability on attn_output_weights. Default: 0.0 (no dropout). - - bias (bool): If specified, adds bias to input/output projection layers. Default: True. - - add_bias_kv (bool): If specified, adds bias to the key and value sequences at dim=0. Default: False. - - add_zero_attn (bool): If specified, adds a new batch of zeros to the key and value sequences at dim=1. Default: False. - - kdim (int): Total number of features for keys. Default: None (uses kdim=embed_dim). - - vdim (int): Total number of features for values. Default: None (uses vdim=embed_dim). - - batch_first (bool): If True, the input and output tensors are provided as (batch, seq, feature). Default: False. - - device (torch.device): If specified, the tensors will be moved to the specified device. - - dtype (torch.dtype): If specified, the tensors will have the specified dtype. - """ - - def forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True, is_causal=False): - """ - Forward pass of the multi-head attention module. - - Parameters: - - query (Tensor): Query embeddings of shape (L, E_q) for unbatched input, (L, N, E_q) when batch_first=False, or (N, L, E_q) when batch_first=True. - - key (Tensor): Key embeddings of shape (S, E_k) for unbatched input, (S, N, E_k) when batch_first=False, or (N, S, E_k) when batch_first=True. - - value (Tensor): Value embeddings of shape (S, E_v) for unbatched input, (S, N, E_v) when batch_first=False, or (N, S, E_v) when batch_first=True. - - key_padding_mask (Optional[Tensor]): If specified, a mask indicating elements to be ignored in key for attention computation. - - need_weights (bool): If specified, returns attention weights in addition to attention outputs. Default: True. - - attn_mask (Optional[Tensor]): If specified, a mask preventing attention to certain positions. - - average_attn_weights (bool): If true, returns averaged attention weights per head. Otherwise, returns attention weights separately per head. Note that this flag only has an effect when need_weights=True. Default: True. - - is_causal (bool): If specified, applies a causal mask as the attention mask. Default: False. - - Returns: - Tuple[Tensor, Optional[Tensor]]: - - attn_output (Tensor): Attention outputs of shape (L, E) for unbatched input, (L, N, E) when batch_first=False, or (N, L, E) when batch_first=True. - - attn_output_weights (Optional[Tensor]): Attention weights of shape (L, S) when unbatched or (N, L, S) when batched. Optional, only returned when need_weights=True. - """ - - # Implementation of the forward pass of the attention module goes here - - return attn_output, attn_output_weights - - -# Usage example: - -multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) -attn_output, attn_output_weights = multihead_attn(query, key, value) -Note: - -The above template includes the class or function definition, parameters, description, and usage example. -To replicate the documentation for any other module or framework, follow the same structure and provide the specific details for that module or framework. - - -############# CODE TO DOCUMENT, DOCUMENT THE diff --git a/docs/swarms/models/anthropic.md b/docs/swarms/models/anthropic.md index 4d5f1fcd..cf139f76 100644 --- a/docs/swarms/models/anthropic.md +++ b/docs/swarms/models/anthropic.md @@ -70,17 +70,18 @@ class Anthropic: ```python # Import necessary modules and classes from swarms.models import Anthropic -import torch # Initialize an instance of the Anthropic class -anthropic_instance = Anthropic() +model = Anthropic( + anthropic_api_key="sk-" +) -# Using the generate method -completion_1 = anthropic_instance.generate("What is the capital of France?") +# Using the run method +completion_1 = model.run("What is the capital of France?") print(completion_1) # Using the __call__ method -completion_2 = anthropic_instance("How far is the moon from the earth?", stop=["miles", "km"]) +completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"]) print(completion_2) ``` diff --git a/godmode.py b/godmode.py deleted file mode 100644 index f1269d98..00000000 --- a/godmode.py +++ /dev/null @@ -1,16 +0,0 @@ -from swarms.swarms import GodMode -from swarms.models import OpenAIChat - -api_key = "" - -llm = OpenAIChat(openai_api_key=api_key) - - -llms = [llm, llm, llm] - -god_mode = GodMode(llms) - -task = "Generate a 10,000 word blog on health and wellness." - -out = god_mode.run(task) -god_mode.print_responses(task) diff --git a/dalle3.py b/playground/models/dalle3.py similarity index 100% rename from dalle3.py rename to playground/models/dalle3.py diff --git a/gpt4vision_example.py b/playground/models/gpt4vision_example.py similarity index 100% rename from gpt4vision_example.py rename to playground/models/gpt4vision_example.py diff --git a/swarms/agents/idea_to_image_agent.py b/swarms/agents/idea_to_image_agent.py index e2a06691..f7e5ec0c 100644 --- a/swarms/agents/idea_to_image_agent.py +++ b/swarms/agents/idea_to_image_agent.py @@ -1,7 +1,7 @@ import os import logging from dataclasses import dataclass -from dalle3 import Dalle +from playground.models.dalle3 import Dalle from swarms.models import OpenAIChat diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index d18b1b9d..0c5bf2c7 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -23,7 +23,7 @@ class HuggingfaceLLM: ``` from swarms.models import HuggingfaceLLM - model_id = "gpt2-small" + model_id = "NousResearch/Yarn-Mistral-7b-128k" inference = HuggingfaceLLM(model_id=model_id) task = "Once upon a time" @@ -74,15 +74,20 @@ class HuggingfaceLLM: bnb_config = BitsAndBytesConfig(**quantization_config) try: - self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) + self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, *args, **kwargs) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config + self.model_id, quantization_config=bnb_config, *args, **kwargs ) self.model # .to(self.device) except Exception as e: - self.logger.error(f"Failed to load the model or the tokenizer: {e}") - raise + # self.logger.error(f"Failed to load the model or the tokenizer: {e}") + # raise + print(colored(f"Failed to load the model and or the tokenizer: {e}", "red")) + + def print_error(self, error: str): + """Print error""" + print(colored(f"Error: {error}", "red")) def load_model(self): """Load the model""" @@ -157,7 +162,7 @@ class HuggingfaceLLM: del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) except Exception as e: - self.logger.error(f"Failed to generate the text: {e}") + print(colored(f"HuggingfaceLLM could not generate text because of error: {e}, try optimizing your arguments", "red")) raise async def run_async(self, task: str, *args, **kwargs) -> str: diff --git a/tests/models/dalle3.py b/tests/models/dalle3.py index ff1489ea..42b851b7 100644 --- a/tests/models/dalle3.py +++ b/tests/models/dalle3.py @@ -6,7 +6,7 @@ from openai import OpenAIError from PIL import Image from termcolor import colored -from dalle3 import Dalle3 +from playground.models.dalle3 import Dalle3 # Mocking the OpenAI client to avoid making actual API calls during testing From e01f25a9ff433002b14b0410887672820893a327 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 19:08:13 -0500 Subject: [PATCH 39/63] clean up Former-commit-id: 8dc90648199f222582ef490a74931dbb45eb1cf2 --- docs/swarms/models/anthropic.md | 2 +- example.py | 6 ++++-- demos/positive_med.py => positive_med.py | 0 pyproject.toml | 2 +- swarms/agents/__init__.py | 4 ++-- swarms/agents/idea_to_image_agent.py | 2 +- swarms/models/anthropic.py | 5 ++++- 7 files changed, 13 insertions(+), 8 deletions(-) rename demos/positive_med.py => positive_med.py (100%) diff --git a/docs/swarms/models/anthropic.md b/docs/swarms/models/anthropic.md index cf139f76..85e7a428 100644 --- a/docs/swarms/models/anthropic.md +++ b/docs/swarms/models/anthropic.md @@ -73,7 +73,7 @@ from swarms.models import Anthropic # Initialize an instance of the Anthropic class model = Anthropic( - anthropic_api_key="sk-" + anthropic_api_key="" ) # Using the run method diff --git a/example.py b/example.py index 8e34cce3..b3740aa2 100644 --- a/example.py +++ b/example.py @@ -11,11 +11,13 @@ llm = OpenAIChat( # max_tokens=100, ) + ## Initialize the workflow flow = Flow( llm=llm, - max_loops=2, + max_loops=5, dashboard=True, + # tools = [search_api, slack, ] # stopping_condition=None, # You can define a stopping condition as needed. # loop_interval=1, # retry_attempts=3, @@ -27,7 +29,7 @@ flow = Flow( # out = flow.load_state("flow_state.json") # temp = flow.dynamic_temperature() # filter = flow.add_response_filter("Trump") -out = flow.run("Generate a 10,000 word blog on health and wellness.") +out = flow.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.") # out = flow.validate_response(out) # out = flow.analyze_feedback(out) # out = flow.print_history_and_memory() diff --git a/demos/positive_med.py b/positive_med.py similarity index 100% rename from demos/positive_med.py rename to positive_med.py diff --git a/pyproject.toml b/pyproject.toml index 6aa8585d..a80b6389 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "1.9.9" +version = "2.0.1" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index 597c8c76..34dc0f1d 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -5,7 +5,7 @@ from swarms.agents.message import Message # from swarms.agents.stream_response import stream from swarms.agents.base import AbstractAgent from swarms.agents.registry import Registry -from swarms.agents.idea_to_image_agent import Idea2Image +# from swarms.agents.idea_to_image_agent import Idea2Image from swarms.agents.simple_agent import SimpleAgent @@ -17,6 +17,6 @@ __all__ = [ "Message", "AbstractAgent", "Registry", - "Idea2Image", + # "Idea2Image", "SimpleAgent", ] diff --git a/swarms/agents/idea_to_image_agent.py b/swarms/agents/idea_to_image_agent.py index f7e5ec0c..ce3654e0 100644 --- a/swarms/agents/idea_to_image_agent.py +++ b/swarms/agents/idea_to_image_agent.py @@ -1,7 +1,7 @@ import os import logging from dataclasses import dataclass -from playground.models.dalle3 import Dalle +from swarms.models.dalle3 import Dalle from swarms.models import OpenAIChat diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index e2066637..9914fce9 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -44,6 +44,7 @@ class Anthropic: top_p=None, streaming=False, default_request_timeout=None, + api_key: str = None ): self.model = model self.max_tokens_to_sample = max_tokens_to_sample @@ -56,6 +57,7 @@ class Anthropic: "ANTHROPIC_API_URL", "https://api.anthropic.com" ) self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY") + self.api_key = api_key def _default_params(self): """Get the default parameters for calling Anthropic API.""" @@ -73,9 +75,10 @@ class Anthropic: def run(self, task: str, stop=None): """Call out to Anthropic's completion endpoint.""" + api_key = self.api_key or self.anthropic_api_key stop = stop or [] params = self._default_params() - headers = {"Authorization": f"Bearer {self.anthropic_api_key}"} + headers = {"Authorization": f"Bearer {api_key}"} data = {"prompt": task, "stop_sequences": stop, **params} response = requests.post( f"{self.anthropic_api_url}/completions", From 427c5af0e133a00a55d548480b4310442c17c228 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 19:18:13 -0500 Subject: [PATCH 40/63] swarms Former-commit-id: 62a413579cedbc8e7a0378ed6850454ee42cebe5 --- README.md | 48 +----------------------- positive_med.py => demos/positive_med.py | 0 2 files changed, 1 insertion(+), 47 deletions(-) rename positive_med.py => demos/positive_med.py (100%) diff --git a/README.md b/README.md index a80a307a..289a4c22 100644 --- a/README.md +++ b/README.md @@ -81,35 +81,6 @@ out = flow.run("Generate a 10,000 word blog on health and wellness.") -``` - - -## `GodMode` -- A powerful tool for concurrent execution of tasks using multiple Language Model (LLM) instances. - -```python -from swarms.swarms import GodMode -from swarms.models import OpenAIChat - -api_key = "" - -llm = OpenAIChat( - openai_api_key=api_key -) - - -llms = [ - llm, - llm, - llm -] - -god_mode = GodMode(llms) - -task = 'Generate a 10,000 word blog on health and wellness.' - -out = god_mode.run(task) -god_mode.print_responses(task) ``` ------ @@ -159,22 +130,6 @@ for task in workflow.tasks: ``` -### `OmniModalAgent` -- OmniModal Agent is an LLM that access to 10+ multi-modal encoders and diffusers! It can generate images, videos, speech, music and so much more, get started with: - -```python -from swarms.models import OpenAIChat -from swarms.agents import OmniModalAgent - -api_key = "SK-" - -llm = OpenAIChat(model_name="gpt-4", openai_api_key=api_key) - -agent = OmniModalAgent(llm) - -agent.run("Create a video of a swarm of fish") - -``` --- @@ -183,8 +138,7 @@ agent.run("Create a video of a swarm of fish") ## Contribute - -We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) +- We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) # License diff --git a/positive_med.py b/demos/positive_med.py similarity index 100% rename from positive_med.py rename to demos/positive_med.py From 86362cdaa09907e63a6ba04bf286d7a904240c88 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 6 Nov 2023 21:28:20 -0500 Subject: [PATCH 41/63] removed open interpreter, clean uped docs, added add messages to flow + utils Former-commit-id: 16176e8cad15d3609ffd4567115c3f1835d69a5d --- docs/swarms/chunkers/basechunker.md | 2 +- docs/swarms/chunkers/pdf_chunker.md | 2 +- example.py | 4 +- pyproject.toml | 4 +- requirements.txt | 1 + swarms/agents/__init__.py | 1 + swarms/agents/companion.py | 4 + swarms/agents/profitpilot.py | 8 +- swarms/chunkers/base.py | 35 +++++-- swarms/chunkers/markdown.py | 7 ++ swarms/chunkers/omni_chunker.py | 124 ++++++++++++++++++++++ swarms/chunkers/pdf.py | 7 ++ swarms/models/__init__.py | 1 + swarms/models/anthropic.py | 2 +- swarms/models/dalle3.py | 11 +- swarms/models/huggingface.py | 11 +- swarms/models/openai_assistant.py | 74 +++++++++++++ swarms/models/openai_tokenizer.py | 150 +++++++++++++++++++++++++++ swarms/structs/flow.py | 21 ++++ swarms/tools/interpreter_tool.py | 24 ----- swarms/workers/__init__.py | 2 +- tests/chunkers/basechunker.py | 4 +- tests/models/dalle3.py | 74 +++++++++---- tests/models/gpt4v.py | 155 ++++++++++++++++++++-------- 24 files changed, 608 insertions(+), 120 deletions(-) create mode 100644 swarms/agents/companion.py create mode 100644 swarms/chunkers/omni_chunker.py create mode 100644 swarms/models/openai_assistant.py create mode 100644 swarms/models/openai_tokenizer.py delete mode 100644 swarms/tools/interpreter_tool.py diff --git a/docs/swarms/chunkers/basechunker.md b/docs/swarms/chunkers/basechunker.md index fed03277..33b03312 100644 --- a/docs/swarms/chunkers/basechunker.md +++ b/docs/swarms/chunkers/basechunker.md @@ -53,7 +53,7 @@ The `BaseChunker` class is the core component of the `BaseChunker` module. It is #### Parameters: - `separators` (list[ChunkSeparator]): Specifies a list of `ChunkSeparator` objects used to split the text into chunks. -- `tokenizer` (OpenAiTokenizer): Defines the tokenizer to be used for counting tokens in the text. +- `tokenizer` (OpenAITokenizer): Defines the tokenizer to be used for counting tokens in the text. - `max_tokens` (int): Sets the maximum token limit for each chunk. ### 4.2. Examples diff --git a/docs/swarms/chunkers/pdf_chunker.md b/docs/swarms/chunkers/pdf_chunker.md index 5b97a551..8c92060d 100644 --- a/docs/swarms/chunkers/pdf_chunker.md +++ b/docs/swarms/chunkers/pdf_chunker.md @@ -52,7 +52,7 @@ The `PdfChunker` class is the core component of the `PdfChunker` module. It is u #### Parameters: - `separators` (list[ChunkSeparator]): Specifies a list of `ChunkSeparator` objects used to split the PDF text content into chunks. -- `tokenizer` (OpenAiTokenizer): Defines the tokenizer used for counting tokens in the text. +- `tokenizer` (OpenAITokenizer): Defines the tokenizer used for counting tokens in the text. - `max_tokens` (int): Sets the maximum token limit for each chunk. ### 4.2. Examples diff --git a/example.py b/example.py index b3740aa2..6c27bceb 100644 --- a/example.py +++ b/example.py @@ -29,7 +29,9 @@ flow = Flow( # out = flow.load_state("flow_state.json") # temp = flow.dynamic_temperature() # filter = flow.add_response_filter("Trump") -out = flow.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.") +out = flow.run( + "Generate a 10,000 word blog on mental clarity and the benefits of meditation." +) # out = flow.validate_response(out) # out = flow.analyze_feedback(out) # out = flow.print_history_and_memory() diff --git a/pyproject.toml b/pyproject.toml index a80b6389..3cb153c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.1" +version = "2.0.2" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] @@ -41,6 +41,7 @@ sentencepiece = "*" wget = "*" griptape = "*" httpx = "*" +tiktoken = "*" attrs = "*" ggl = "*" beautifulsoup4 = "*" @@ -49,7 +50,6 @@ pydantic = "*" tenacity = "*" Pillow = "*" chromadb = "*" -open-interpreter = "*" tabulate = "*" termcolor = "*" black = "*" diff --git a/requirements.txt b/requirements.txt index 7ff9d362..cb0c65b8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -29,6 +29,7 @@ sentencepiece duckduckgo-search agent-protocol chromadb +tiktoken open-interpreter tabulate colored diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index 34dc0f1d..355f0ad1 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -5,6 +5,7 @@ from swarms.agents.message import Message # from swarms.agents.stream_response import stream from swarms.agents.base import AbstractAgent from swarms.agents.registry import Registry + # from swarms.agents.idea_to_image_agent import Idea2Image from swarms.agents.simple_agent import SimpleAgent diff --git a/swarms/agents/companion.py b/swarms/agents/companion.py new file mode 100644 index 00000000..a630895e --- /dev/null +++ b/swarms/agents/companion.py @@ -0,0 +1,4 @@ +""" +Companion agents converse with the user about the agent the user wants to create then creates the agent with the desired attributes and traits and tools and configurations + +""" diff --git a/swarms/agents/profitpilot.py b/swarms/agents/profitpilot.py index 8f6927c4..ac1d0b44 100644 --- a/swarms/agents/profitpilot.py +++ b/swarms/agents/profitpilot.py @@ -16,7 +16,6 @@ from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Chroma from pydantic import BaseModel, Field from swarms.prompts.sales import SALES_AGENT_TOOLS_PROMPT, conversation_stages -from swarms.tools.interpreter_tool import compile # classes @@ -166,12 +165,7 @@ def get_tools(product_catalog): func=knowledge_base.run, description="useful for when you need to answer questions about product information", ), - # Interpreter - Tool( - name="Code Interepeter", - func=compile, - description="Useful when you need to run code locally, such as Python, Javascript, Shell, and more.", - ) + # omnimodal agent ] diff --git a/swarms/chunkers/base.py b/swarms/chunkers/base.py index 464f51e4..0fabdcef 100644 --- a/swarms/chunkers/base.py +++ b/swarms/chunkers/base.py @@ -1,10 +1,13 @@ from __future__ import annotations + from abc import ABC from typing import Optional -from attr import define, field, Factory + +from attr import Factory, define, field from griptape.artifacts import TextArtifact -from swarms.chunkers.chunk_seperators import ChunkSeparator -from griptape.tokenizers import OpenAiTokenizer + +from swarms.chunkers.chunk_seperator import ChunkSeparator +from swarms.models.openai_tokenizer import OpenAITokenizer @define @@ -16,6 +19,24 @@ class BaseChunker(ABC): Usage: -------------- + from swarms.chunkers.base import BaseChunker + from swarms.chunkers.chunk_seperator import ChunkSeparator + + class PdfChunker(BaseChunker): + DEFAULT_SEPARATORS = [ + ChunkSeparator("\n\n"), + ChunkSeparator(". "), + ChunkSeparator("! "), + ChunkSeparator("? "), + ChunkSeparator(" "), + ] + + # Example + pdf = "swarmdeck.pdf" + chunker = PdfChunker() + chunks = chunker.chunk(pdf) + print(chunks) + """ @@ -26,10 +47,10 @@ class BaseChunker(ABC): default=Factory(lambda self: self.DEFAULT_SEPARATORS, takes_self=True), kw_only=True, ) - tokenizer: OpenAiTokenizer = field( + tokenizer: OpenAITokenizer = field( default=Factory( - lambda: OpenAiTokenizer( - model=OpenAiTokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL + lambda: OpenAITokenizer( + model=OpenAITokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL ) ), kw_only=True, @@ -47,7 +68,7 @@ class BaseChunker(ABC): def _chunk_recursively( self, chunk: str, current_separator: Optional[ChunkSeparator] = None ) -> list[str]: - token_count = self.tokenizer.token_count(chunk) + token_count = self.tokenizer.count_tokens(chunk) if token_count <= self.max_tokens: return [chunk] diff --git a/swarms/chunkers/markdown.py b/swarms/chunkers/markdown.py index 6c0e755f..7836b0a7 100644 --- a/swarms/chunkers/markdown.py +++ b/swarms/chunkers/markdown.py @@ -15,3 +15,10 @@ class MarkdownChunker(BaseChunker): ChunkSeparator("? "), ChunkSeparator(" "), ] + + +# # Example using chunker to chunk a markdown file +# file = open("README.md", "r") +# text = file.read() +# chunker = MarkdownChunker() +# chunks = chunker.chunk(text) diff --git a/swarms/chunkers/omni_chunker.py b/swarms/chunkers/omni_chunker.py new file mode 100644 index 00000000..dca569ea --- /dev/null +++ b/swarms/chunkers/omni_chunker.py @@ -0,0 +1,124 @@ +""" +Omni Chunker is a chunker that chunks all files into select chunks of size x strings + +Usage: +-------------- +from swarms.chunkers.omni_chunker import OmniChunker + +# Example +pdf = "swarmdeck.pdf" +chunker = OmniChunker(chunk_size=1000, beautify=True) +chunks = chunker(pdf) +print(chunks) + + +""" +from dataclasses import dataclass +from typing import List, Optional, Callable +from termcolor import colored +import os +import sys + + + + +@dataclass +class OmniChunker: + """ + + + """ + chunk_size: int = 1000 + beautify: bool = False + use_tokenizer: bool = False + tokenizer: Optional[Callable[[str], List[str]]] = None + + + + def __call__(self, file_path: str) -> List[str]: + """ + Chunk the given file into parts of size `chunk_size`. + + Args: + file_path (str): The path to the file to chunk. + + Returns: + List[str]: A list of string chunks from the file. + """ + if not os.path.isfile(file_path): + print(colored("The file does not exist.", "red")) + return [] + + file_extension = os.path.splitext(file_path)[1] + try: + with open(file_path, "rb") as file: + content = file.read() + # Decode content based on MIME type or file extension + decoded_content = self.decode_content(content, file_extension) + chunks = self.chunk_content(decoded_content) + return chunks + + except Exception as e: + print(colored(f"Error reading file: {e}", "red")) + return [] + + def decode_content(self, content: bytes, file_extension: str) -> str: + """ + Decode the content of the file based on its MIME type or file extension. + + Args: + content (bytes): The content of the file. + file_extension (str): The file extension of the file. + + Returns: + str: The decoded content of the file. + """ + # Add logic to handle different file types based on the extension + # For simplicity, this example assumes text files encoded in utf-8 + try: + return content.decode("utf-8") + except UnicodeDecodeError as e: + print( + colored( + f"Could not decode file with extension {file_extension}: {e}", + "yellow", + ) + ) + return "" + + def chunk_content(self, content: str) -> List[str]: + """ + Split the content into chunks of size `chunk_size`. + + Args: + content (str): The content to chunk. + + Returns: + List[str]: The list of chunks. + """ + return [ + content[i : i + self.chunk_size] + for i in range(0, len(content), self.chunk_size) + ] + + def __str__(self): + return f"OmniChunker(chunk_size={self.chunk_size}, beautify={self.beautify})" + + def metrics(self): + return { + "chunk_size": self.chunk_size, + "beautify": self.beautify, + } + + def print_dashboard(self): + print( + colored( + f""" + Omni Chunker + ------------ + {self.metrics()} + """, + "cyan", + ) + ) + diff --git a/swarms/chunkers/pdf.py b/swarms/chunkers/pdf.py index 206c74f3..710134a0 100644 --- a/swarms/chunkers/pdf.py +++ b/swarms/chunkers/pdf.py @@ -10,3 +10,10 @@ class PdfChunker(BaseChunker): ChunkSeparator("? "), ChunkSeparator(" "), ] + + +# # Example +# pdf = "swarmdeck.pdf" +# chunker = PdfChunker() +# chunks = chunker.chunk(pdf) +# print(chunks) diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index dd21ba80..26c06066 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -16,6 +16,7 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA + # from swarms.models.gpt4v import GPT4Vision # from swarms.models.dalle3 import Dalle3 diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 9914fce9..cc3931bb 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -44,7 +44,7 @@ class Anthropic: top_p=None, streaming=False, default_request_timeout=None, - api_key: str = None + api_key: str = None, ): self.model = model self.max_tokens_to_sample = max_tokens_to_sample diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py index 2ac5d403..899564fc 100644 --- a/swarms/models/dalle3.py +++ b/swarms/models/dalle3.py @@ -129,7 +129,7 @@ class Dalle3: ) ) raise error - + def create_variations(self, img: str): """ Create variations of an image using the Dalle3 API @@ -151,14 +151,11 @@ class Dalle3: >>> img = dalle3.create_variations(img) >>> print(img) - + """ try: - response = self.client.images.create_variation( - img = open(img, "rb"), - n=self.n, - size=self.size + img=open(img, "rb"), n=self.n, size=self.size ) img = response.data[0].url @@ -172,4 +169,4 @@ class Dalle3: ) print(colored(f"Error running Dalle3: {error.http_status}", "red")) print(colored(f"Error running Dalle3: {error.error}", "red")) - raise error \ No newline at end of file + raise error diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index 0c5bf2c7..f11bf3df 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -74,7 +74,9 @@ class HuggingfaceLLM: bnb_config = BitsAndBytesConfig(**quantization_config) try: - self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, *args, **kwargs) + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_id, *args, **kwargs + ) self.model = AutoModelForCausalLM.from_pretrained( self.model_id, quantization_config=bnb_config, *args, **kwargs ) @@ -162,7 +164,12 @@ class HuggingfaceLLM: del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) except Exception as e: - print(colored(f"HuggingfaceLLM could not generate text because of error: {e}, try optimizing your arguments", "red")) + print( + colored( + f"HuggingfaceLLM could not generate text because of error: {e}, try optimizing your arguments", + "red", + ) + ) raise async def run_async(self, task: str, *args, **kwargs) -> str: diff --git a/swarms/models/openai_assistant.py b/swarms/models/openai_assistant.py new file mode 100644 index 00000000..6d0c518f --- /dev/null +++ b/swarms/models/openai_assistant.py @@ -0,0 +1,74 @@ +from typing import Dict, List, Optional +from dataclass import dataclass + +from swarms.models import OpenAI + + +@dataclass +class OpenAIAssistant: + name: str = "OpenAI Assistant" + instructions: str = None + tools: List[Dict] = None + model: str = None + openai_api_key: str = None + temperature: float = 0.5 + max_tokens: int = 100 + stop: List[str] = None + echo: bool = False + stream: bool = False + log: bool = False + presence: bool = False + dashboard: bool = False + debug: bool = False + max_loops: int = 5 + stopping_condition: Optional[str] = None + loop_interval: int = 1 + retry_attempts: int = 3 + retry_interval: int = 1 + interactive: bool = False + dynamic_temperature: bool = False + state: Dict = None + response_filters: List = None + response_filter: Dict = None + response_filter_name: str = None + response_filter_value: str = None + response_filter_type: str = None + response_filter_action: str = None + response_filter_action_value: str = None + response_filter_action_type: str = None + response_filter_action_name: str = None + client = OpenAI() + role: str = "user" + instructions: str = None + + def create_assistant(self, task: str): + assistant = self.client.create_assistant( + name=self.name, + instructions=self.instructions, + tools=self.tools, + model=self.model, + ) + return assistant + + def create_thread(self): + thread = self.client.beta.threads.create() + return thread + + def add_message_to_thread(self, thread_id: str, message: str): + message = self.client.beta.threads.add_message( + thread_id=thread_id, role=self.user, content=message + ) + return message + + def run(self, task: str): + run = self.client.beta.threads.runs.create( + thread_id=self.create_thread().id, + assistant_id=self.create_assistant().id, + instructions=self.instructions, + ) + + out = self.client.beta.threads.runs.retrieve( + thread_id=run.thread_id, run_id=run.id + ) + + return out diff --git a/swarms/models/openai_tokenizer.py b/swarms/models/openai_tokenizer.py new file mode 100644 index 00000000..b4e375cc --- /dev/null +++ b/swarms/models/openai_tokenizer.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from typing import Optional + +import tiktoken +from attr import Factory, define, field + + +@define(frozen=True) +class BaseTokenizer(ABC): + DEFAULT_STOP_SEQUENCES = ["Observation:"] + + stop_sequences: list[str] = field( + default=Factory(lambda: BaseTokenizer.DEFAULT_STOP_SEQUENCES), + kw_only=True, + ) + + @property + @abstractmethod + def max_tokens(self) -> int: + ... + + def count_tokens_left(self, text: str) -> int: + diff = self.max_tokens - self.count_tokens(text) + + if diff > 0: + return diff + else: + return 0 + + @abstractmethod + def count_tokens(self, text: str) -> int: + ... + + +@define(frozen=True) +class OpenAITokenizer(BaseTokenizer): + DEFAULT_OPENAI_GPT_3_COMPLETION_MODEL = "text-davinci-003" + DEFAULT_OPENAI_GPT_3_CHAT_MODEL = "gpt-3.5-turbo" + DEFAULT_OPENAI_GPT_4_MODEL = "gpt-4" + DEFAULT_ENCODING = "cl100k_base" + DEFAULT_MAX_TOKENS = 2049 + TOKEN_OFFSET = 8 + + MODEL_PREFIXES_TO_MAX_TOKENS = { + "gpt-4-32k": 32768, + "gpt-4": 8192, + "gpt-3.5-turbo-16k": 16384, + "gpt-3.5-turbo": 4096, + "gpt-35-turbo-16k": 16384, + "gpt-35-turbo": 4096, + "text-davinci-003": 4097, + "text-davinci-002": 4097, + "code-davinci-002": 8001, + "text-embedding-ada-002": 8191, + "text-embedding-ada-001": 2046, + } + + EMBEDDING_MODELS = ["text-embedding-ada-002", "text-embedding-ada-001"] + + model: str = field(kw_only=True) + + @property + def encoding(self) -> tiktoken.Encoding: + try: + return tiktoken.encoding_for_model(self.model) + except KeyError: + return tiktoken.get_encoding(self.DEFAULT_ENCODING) + + @property + def max_tokens(self) -> int: + tokens = next( + v + for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() + if self.model.startswith(k) + ) + offset = 0 if self.model in self.EMBEDDING_MODELS else self.TOKEN_OFFSET + + return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset + + def count_tokens( + self, text: str | list, model: Optional[str] = None + ) -> int: + """ + Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook: + https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb + """ + if isinstance(text, list): + model = model if model else self.model + + try: + encoding = tiktoken.encoding_for_model(model) + except KeyError: + logging.warning("model not found. Using cl100k_base encoding.") + + encoding = tiktoken.get_encoding("cl100k_base") + + if model in { + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", + }: + tokens_per_message = 3 + tokens_per_name = 1 + elif model == "gpt-3.5-turbo-0301": + # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_message = 4 + # if there's a name, the role is omitted + tokens_per_name = -1 + elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: + logging.info( + "gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613." + ) + return self.count_tokens(text, model="gpt-3.5-turbo-0613") + elif "gpt-4" in model: + logging.info( + "gpt-4 may update over time. Returning num tokens assuming gpt-4-0613." + ) + return self.count_tokens(text, model="gpt-4-0613") + else: + raise NotImplementedError( + f"""token_count() is not implemented for model {model}. + See https://github.com/openai/openai-python/blob/main/chatml.md for + information on how messages are converted to tokens.""" + ) + + num_tokens = 0 + + for message in text: + num_tokens += tokens_per_message + for key, value in message.items(): + num_tokens += len(encoding.encode(value)) + if key == "name": + num_tokens += tokens_per_name + + # every reply is primed with <|start|>assistant<|message|> + num_tokens += 3 + + return num_tokens + else: + return len( + self.encoding.encode( + text, allowed_special=set(self.stop_sequences) + ) + ) \ No newline at end of file diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 4e21c3df..9ff021f4 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -116,6 +116,7 @@ class Flow: dynamic_temperature: bool = False, saved_state_path: Optional[str] = "flow_state.json", autosave: bool = False, + context_length: int = 8192, **kwargs: Any, ): self.llm = llm @@ -188,6 +189,26 @@ class Flow: return "\n".join(params_str_list) + def truncate_history(self): + """ + Take the history and truncate it to fit into the model context length + """ + truncated_history = self.memory[-1][-self.context_length :] + self.memory[-1] = truncated_history + + def add_task_to_memory(self, task: str): + """Add the task to the memory""" + self.memory.append([f"Human: {task}"]) + + def add_message_to_memory(self, message: str): + """Add the message to the memory""" + self.memory[-1].append(message) + + def add_message_to_memory_and_truncate(self, message: str): + """Add the message to the memory and truncate""" + self.memory[-1].append(message) + self.truncate_history() + def print_dashboard(self, task: str): """Print dashboard""" model_config = self.get_llm_init_params() diff --git a/swarms/tools/interpreter_tool.py b/swarms/tools/interpreter_tool.py deleted file mode 100644 index 22758de6..00000000 --- a/swarms/tools/interpreter_tool.py +++ /dev/null @@ -1,24 +0,0 @@ -import os -import interpreter - - -def compile(task: str): - """ - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing. - - This provides a natural-language interface to your computer's general-purpose capabilities: - - Create and edit photos, videos, PDFs, etc. - Control a Chrome browser to perform research - Plot, clean, and analyze large datasets - ...etc. - โš ๏ธ Note: You'll be asked to approve code before it's run. - """ - - task = interpreter.chat(task, return_messages=True) - interpreter.chat() - interpreter.reset(task) - - os.environ["INTERPRETER_CLI_AUTO_RUN"] = True - os.environ["INTERPRETER_CLI_FAST_MODE"] = True - os.environ["INTERPRETER_CLI_DEBUG"] = True diff --git a/swarms/workers/__init__.py b/swarms/workers/__init__.py index 2a7cc4f1..9dabe94d 100644 --- a/swarms/workers/__init__.py +++ b/swarms/workers/__init__.py @@ -1,2 +1,2 @@ -from swarms.workers.worker import Worker +# from swarms.workers.worker import Worker from swarms.workers.base import AbstractWorker diff --git a/tests/chunkers/basechunker.py b/tests/chunkers/basechunker.py index f70705bc..4fd92da1 100644 --- a/tests/chunkers/basechunker.py +++ b/tests/chunkers/basechunker.py @@ -3,7 +3,7 @@ from swarms.chunkers.base import ( BaseChunker, TextArtifact, ChunkSeparator, - OpenAiTokenizer, + OpenAITokenizer, ) # adjust the import paths accordingly @@ -21,7 +21,7 @@ def test_default_separators(): def test_default_tokenizer(): chunker = BaseChunker() - assert isinstance(chunker.tokenizer, OpenAiTokenizer) + assert isinstance(chunker.tokenizer, OpenAITokenizer) # 2. Test Basic Chunking diff --git a/tests/models/dalle3.py b/tests/models/dalle3.py index 42b851b7..f9a2f8cf 100644 --- a/tests/models/dalle3.py +++ b/tests/models/dalle3.py @@ -23,8 +23,12 @@ def dalle3(mock_openai_client): def test_dalle3_call_success(dalle3, mock_openai_client): # Arrange task = "A painting of a dog" - expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - mock_openai_client.images.generate.return_value = Mock(data=[Mock(url=expected_img_url)]) + expected_img_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + ) + mock_openai_client.images.generate.return_value = Mock( + data=[Mock(url=expected_img_url)] + ) # Act img_url = dalle3(task) @@ -40,7 +44,9 @@ def test_dalle3_call_failure(dalle3, mock_openai_client, capsys): expected_error_message = "Error running Dalle3: API Error" # Mocking OpenAIError - mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + mock_openai_client.images.generate.side_effect = OpenAIError( + expected_error_message, http_status=500, error="Internal Server Error" + ) # Act and assert with pytest.raises(OpenAIError) as excinfo: @@ -57,8 +63,12 @@ def test_dalle3_call_failure(dalle3, mock_openai_client, capsys): def test_dalle3_create_variations_success(dalle3, mock_openai_client): # Arrange img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" - mock_openai_client.images.create_variation.return_value = Mock(data=[Mock(url=expected_variation_url)]) + expected_variation_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + ) + mock_openai_client.images.create_variation.return_value = Mock( + data=[Mock(url=expected_variation_url)] + ) # Act variation_img_url = dalle3.create_variations(img_url) @@ -78,7 +88,9 @@ def test_dalle3_create_variations_failure(dalle3, mock_openai_client, capsys): expected_error_message = "Error running Dalle3: API Error" # Mocking OpenAIError - mock_openai_client.images.create_variation.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + mock_openai_client.images.create_variation.side_effect = OpenAIError( + expected_error_message, http_status=500, error="Internal Server Error" + ) # Act and assert with pytest.raises(OpenAIError) as excinfo: @@ -86,7 +98,7 @@ def test_dalle3_create_variations_failure(dalle3, mock_openai_client, capsys): assert str(excinfo.value) == expected_error_message mock_openai_client.images.create_variation.assert_called_once() - + # Ensure the error message is printed in red captured = capsys.readouterr() assert colored(expected_error_message, "red") in captured.out @@ -142,8 +154,12 @@ def test_dalle3_convert_to_bytesio(): def test_dalle3_call_multiple_times(dalle3, mock_openai_client): # Arrange task = "A painting of a dog" - expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - mock_openai_client.images.generate.return_value = Mock(data=[Mock(url=expected_img_url)]) + expected_img_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + ) + mock_openai_client.images.generate.return_value = Mock( + data=[Mock(url=expected_img_url)] + ) # Act img_url1 = dalle3(task) @@ -159,7 +175,9 @@ def test_dalle3_call_with_large_input(dalle3, mock_openai_client): # Arrange task = "A" * 2048 # Input longer than API's limit expected_error_message = "Error running Dalle3: API Error" - mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + mock_openai_client.images.generate.side_effect = OpenAIError( + expected_error_message, http_status=500, error="Internal Server Error" + ) # Act and assert with pytest.raises(OpenAIError) as excinfo: @@ -204,7 +222,9 @@ def test_dalle3_convert_to_bytesio_invalid_format(dalle3): def test_dalle3_call_with_retry(dalle3, mock_openai_client): # Arrange task = "A painting of a dog" - expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_img_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + ) # Simulate a retry scenario mock_openai_client.images.generate.side_effect = [ @@ -223,7 +243,9 @@ def test_dalle3_call_with_retry(dalle3, mock_openai_client): def test_dalle3_create_variations_with_retry(dalle3, mock_openai_client): # Arrange img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + expected_variation_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + ) # Simulate a retry scenario mock_openai_client.images.create_variation.side_effect = [ @@ -245,7 +267,9 @@ def test_dalle3_call_exception_logging(dalle3, mock_openai_client, capsys): expected_error_message = "Error running Dalle3: API Error" # Mocking OpenAIError - mock_openai_client.images.generate.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + mock_openai_client.images.generate.side_effect = OpenAIError( + expected_error_message, http_status=500, error="Internal Server Error" + ) # Act with pytest.raises(OpenAIError): @@ -262,7 +286,9 @@ def test_dalle3_create_variations_exception_logging(dalle3, mock_openai_client, expected_error_message = "Error running Dalle3: API Error" # Mocking OpenAIError - mock_openai_client.images.create_variation.side_effect = OpenAIError(expected_error_message, http_status=500, error="Internal Server Error") + mock_openai_client.images.create_variation.side_effect = OpenAIError( + expected_error_message, http_status=500, error="Internal Server Error" + ) # Act with pytest.raises(OpenAIError): @@ -313,7 +339,9 @@ def test_dalle3_call_with_retry_max_retries_exceeded(dalle3, mock_openai_client) task = "A painting of a dog" # Simulate max retries exceeded - mock_openai_client.images.generate.side_effect = OpenAIError("Temporary error", http_status=500, error="Internal Server Error") + mock_openai_client.images.generate.side_effect = OpenAIError( + "Temporary error", http_status=500, error="Internal Server Error" + ) # Act and assert with pytest.raises(OpenAIError) as excinfo: @@ -322,12 +350,16 @@ def test_dalle3_call_with_retry_max_retries_exceeded(dalle3, mock_openai_client) assert "Retry limit exceeded" in str(excinfo.value) -def test_dalle3_create_variations_with_retry_max_retries_exceeded(dalle3, mock_openai_client): +def test_dalle3_create_variations_with_retry_max_retries_exceeded( + dalle3, mock_openai_client +): # Arrange img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" # Simulate max retries exceeded - mock_openai_client.images.create_variation.side_effect = OpenAIError("Temporary error", http_status=500, error="Internal Server Error") + mock_openai_client.images.create_variation.side_effect = OpenAIError( + "Temporary error", http_status=500, error="Internal Server Error" + ) # Act and assert with pytest.raises(OpenAIError) as excinfo: @@ -339,7 +371,9 @@ def test_dalle3_create_variations_with_retry_max_retries_exceeded(dalle3, mock_o def test_dalle3_call_retry_with_success(dalle3, mock_openai_client): # Arrange task = "A painting of a dog" - expected_img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + expected_img_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" + ) # Simulate success after a retry mock_openai_client.images.generate.side_effect = [ @@ -358,7 +392,9 @@ def test_dalle3_call_retry_with_success(dalle3, mock_openai_client): def test_dalle3_create_variations_retry_with_success(dalle3, mock_openai_client): # Arrange img_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - expected_variation_url = "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + expected_variation_url = ( + "https://cdn.openai.com/dall-e/encoded/feats/feats_02ABCDE.png" + ) # Simulate success after a retry mock_openai_client.images.create_variation.side_effect = [ diff --git a/tests/models/gpt4v.py b/tests/models/gpt4v.py index 40ccc7f5..23e97d03 100644 --- a/tests/models/gpt4v.py +++ b/tests/models/gpt4v.py @@ -12,19 +12,22 @@ load_dotenv api_key = os.getenv("OPENAI_API_KEY") + # Mock the OpenAI client @pytest.fixture def mock_openai_client(): return Mock() + @pytest.fixture def gpt4vision(mock_openai_client): return GPT4Vision(client=mock_openai_client) + def test_gpt4vision_default_values(): # Arrange and Act gpt4vision = GPT4Vision() - + # Assert assert gpt4vision.max_retries == 3 assert gpt4vision.model == "gpt-4-vision-preview" @@ -34,59 +37,68 @@ def test_gpt4vision_default_values(): assert gpt4vision.quality == "low" assert gpt4vision.max_tokens == 200 + def test_gpt4vision_api_key_from_env_variable(): # Arrange - api_key = os.environ["OPENAI_API_KEY"] - + api_key = os.environ["OPENAI_API_KEY"] + # Act gpt4vision = GPT4Vision() - + # Assert assert gpt4vision.api_key == api_key + def test_gpt4vision_set_api_key(): # Arrange gpt4vision = GPT4Vision(api_key=api_key) - + # Assert assert gpt4vision.api_key == api_key + def test_gpt4vision_invalid_max_retries(): # Arrange and Act with pytest.raises(ValueError): GPT4Vision(max_retries=-1) + def test_gpt4vision_invalid_backoff_factor(): # Arrange and Act with pytest.raises(ValueError): GPT4Vision(backoff_factor=-1) + def test_gpt4vision_invalid_timeout_seconds(): # Arrange and Act with pytest.raises(ValueError): GPT4Vision(timeout_seconds=-1) + def test_gpt4vision_invalid_max_tokens(): # Arrange and Act with pytest.raises(ValueError): GPT4Vision(max_tokens=-1) + def test_gpt4vision_logger_initialized(): # Arrange gpt4vision = GPT4Vision() - + # Assert assert isinstance(gpt4vision.logger, logging.Logger) + def test_gpt4vision_process_img_nonexistent_file(): # Arrange gpt4vision = GPT4Vision() img_path = "nonexistent_image.jpg" - + # Act and Assert with pytest.raises(FileNotFoundError): gpt4vision.process_img(img_path) + def test_gpt4vision_call_single_task_single_image_no_openai_client(gpt4vision): # Arrange img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" @@ -96,7 +108,10 @@ def test_gpt4vision_call_single_task_single_image_no_openai_client(gpt4vision): with pytest.raises(AttributeError): gpt4vision(img_url, [task]) -def test_gpt4vision_call_single_task_single_image_empty_response(gpt4vision, mock_openai_client): + +def test_gpt4vision_call_single_task_single_image_empty_response( + gpt4vision, mock_openai_client +): # Arrange img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" task = "Describe this image." @@ -110,7 +125,10 @@ def test_gpt4vision_call_single_task_single_image_empty_response(gpt4vision, moc assert response.answer == "" mock_openai_client.chat.completions.create.assert_called_once() -def test_gpt4vision_call_multiple_tasks_single_image_empty_responses(gpt4vision, mock_openai_client): + +def test_gpt4vision_call_multiple_tasks_single_image_empty_responses( + gpt4vision, mock_openai_client +): # Arrange img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" tasks = ["Describe this image.", "What's in this picture?"] @@ -122,20 +140,30 @@ def test_gpt4vision_call_multiple_tasks_single_image_empty_responses(gpt4vision, # Assert assert all(response.answer == "" for response in responses) - assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + assert ( + mock_openai_client.chat.completions.create.call_count == 1 + ) # Should be called only once + -def test_gpt4vision_call_single_task_single_image_timeout(gpt4vision, mock_openai_client): +def test_gpt4vision_call_single_task_single_image_timeout( + gpt4vision, mock_openai_client +): # Arrange img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" task = "Describe this image." - mock_openai_client.chat.completions.create.side_effect = Timeout("Request timed out") + mock_openai_client.chat.completions.create.side_effect = Timeout( + "Request timed out" + ) # Act and Assert with pytest.raises(Timeout): gpt4vision(img_url, [task]) -def test_gpt4vision_call_retry_with_success_after_timeout(gpt4vision, mock_openai_client): + +def test_gpt4vision_call_retry_with_success_after_timeout( + gpt4vision, mock_openai_client +): # Arrange img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" task = "Describe this image." @@ -143,7 +171,11 @@ def test_gpt4vision_call_retry_with_success_after_timeout(gpt4vision, mock_opena # Simulate success after a timeout and retry mock_openai_client.chat.completions.create.side_effect = [ Timeout("Request timed out"), - {"choices": [{"message": {"content": {"text": "A description of the image."}}}],} + { + "choices": [ + {"message": {"content": {"text": "A description of the image."}}} + ], + }, ] # Act @@ -151,7 +183,9 @@ def test_gpt4vision_call_retry_with_success_after_timeout(gpt4vision, mock_opena # Assert assert response.answer == "A description of the image." - assert mock_openai_client.chat.completions.create.call_count == 2 # Should be called twice + assert ( + mock_openai_client.chat.completions.create.call_count == 2 + ) # Should be called twice def test_gpt4vision_process_img(): @@ -173,7 +207,9 @@ def test_gpt4vision_call_single_task_single_image(gpt4vision, mock_openai_client expected_response = GPT4VisionResponse(answer="A description of the image.") - mock_openai_client.chat.completions.create.return_value.choices[0].text = expected_response.answer + mock_openai_client.chat.completions.create.return_value.choices[ + 0 + ].text = expected_response.answer # Act response = gpt4vision(img_url, [task]) @@ -190,7 +226,9 @@ def test_gpt4vision_call_single_task_multiple_images(gpt4vision, mock_openai_cli expected_response = GPT4VisionResponse(answer="Descriptions of the images.") - mock_openai_client.chat.completions.create.return_value.choices[0].text = expected_response.answer + mock_openai_client.chat.completions.create.return_value.choices[ + 0 + ].text = expected_response.answer # Act response = gpt4vision(img_urls, [task]) @@ -213,57 +251,76 @@ def test_gpt4vision_call_multiple_tasks_single_image(gpt4vision, mock_openai_cli def create_mock_response(response): return {"choices": [{"message": {"content": {"text": response.answer}}}]} - mock_openai_client.chat.completions.create.side_effect = [create_mock_response(response) for response in expected_responses] + mock_openai_client.chat.completions.create.side_effect = [ + create_mock_response(response) for response in expected_responses + ] # Act responses = gpt4vision(img_url, tasks) # Assert assert responses == expected_responses - assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once - def test_gpt4vision_call_multiple_tasks_single_image(gpt4vision, mock_openai_client): - # Arrange - img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" - tasks = ["Describe this image.", "What's in this picture?"] - - expected_responses = [ - GPT4VisionResponse(answer="A description of the image."), - GPT4VisionResponse(answer="It contains various objects."), + assert ( + mock_openai_client.chat.completions.create.call_count == 1 + ) # Should be called only once + + def test_gpt4vision_call_multiple_tasks_single_image( + gpt4vision, mock_openai_client + ): + # Arrange + img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" + tasks = ["Describe this image.", "What's in this picture?"] + + expected_responses = [ + GPT4VisionResponse(answer="A description of the image."), + GPT4VisionResponse(answer="It contains various objects."), + ] + + mock_openai_client.chat.completions.create.side_effect = [ + { + "choices": [ + {"message": {"content": {"text": expected_responses[i].answer}}} ] + } + for i in range(len(expected_responses)) + ] - mock_openai_client.chat.completions.create.side_effect = [ - {"choices": [{"message": {"content": {"text": expected_responses[i].answer}}}] } for i in range(len(expected_responses)) - ] + # Act + responses = gpt4vision(img_url, tasks) - # Act - responses = gpt4vision(img_url, tasks) - - # Assert - assert responses == expected_responses - assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + # Assert + assert responses == expected_responses + assert ( + mock_openai_client.chat.completions.create.call_count == 1 + ) # Should be called only once def test_gpt4vision_call_multiple_tasks_multiple_images(gpt4vision, mock_openai_client): # Arrange - img_urls = ["https://images.unsplash.com/photo-1694734479857-626882b6db37?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D", "https://images.unsplash.com/photo-1694734479898-6ac4633158ac?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"] + img_urls = [ + "https://images.unsplash.com/photo-1694734479857-626882b6db37?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D", + "https://images.unsplash.com/photo-1694734479898-6ac4633158ac?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D", + ] tasks = ["Describe these images.", "What's in these pictures?"] expected_responses = [ GPT4VisionResponse(answer="Descriptions of the images."), - GPT4VisionResponse(answer="They contain various objects.") + GPT4VisionResponse(answer="They contain various objects."), ] mock_openai_client.chat.completions.create.side_effect = [ - {"choices": [{"message": {"content": {"text": response.answer}}}] } for response in expected_responses + {"choices": [{"message": {"content": {"text": response.answer}}}]} + for response in expected_responses ] # Act responses = gpt4vision(img_urls, tasks) - # Assert assert responses == expected_responses - assert mock_openai_client.chat.completions.create.call_count == 1 # Should be called only once + assert ( + mock_openai_client.chat.completions.create.call_count == 1 + ) # Should be called only once def test_gpt4vision_call_http_error(gpt4vision, mock_openai_client): @@ -283,7 +340,9 @@ def test_gpt4vision_call_request_error(gpt4vision, mock_openai_client): img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" task = "Describe this image." - mock_openai_client.chat.completions.create.side_effect = RequestException("Request Error") + mock_openai_client.chat.completions.create.side_effect = RequestException( + "Request Error" + ) # Act and Assert with pytest.raises(RequestException): @@ -295,7 +354,9 @@ def test_gpt4vision_call_connection_error(gpt4vision, mock_openai_client): img_url = "https://images.unsplash.com/photo-1694734479942-8cc7f4660578?q=80&w=1287&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" task = "Describe this image." - mock_openai_client.chat.completions.create.side_effect = ConnectionError("Connection Error") + mock_openai_client.chat.completions.create.side_effect = ConnectionError( + "Connection Error" + ) # Act and Assert with pytest.raises(ConnectionError): @@ -310,7 +371,9 @@ def test_gpt4vision_call_retry_with_success(gpt4vision, mock_openai_client): # Simulate success after a retry mock_openai_client.chat.completions.create.side_effect = [ RequestException("Temporary error"), - {"choices": [{"text": "A description of the image."}]} # fixed dictionary syntax + { + "choices": [{"text": "A description of the image."}] + }, # fixed dictionary syntax ] # Act @@ -318,4 +381,6 @@ def test_gpt4vision_call_retry_with_success(gpt4vision, mock_openai_client): # Assert assert response.answer == "A description of the image." - assert mock_openai_client.chat.completions.create.call_count == 2 # Should be called twice + assert ( + mock_openai_client.chat.completions.create.call_count == 2 + ) # Should be called twice From 6b0d5d4a4ff923ecc0337f5f643684a967e58d77 Mon Sep 17 00:00:00 2001 From: vyomakesh09 Date: Tue, 7 Nov 2023 10:48:08 +0000 Subject: [PATCH 42/63] fix: typo bfloat16 Former-commit-id: b61fcf3983dd0f8efa1e245ea4c7b7db461f5926 --- swarms/models/zephyr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index 582bc740..f4052d82 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -39,7 +39,7 @@ class Zephyr: self.pipe = pipeline( "text-generation", model="HuggingFaceH4/zephyr-7b-alpha", - torch_dtype=torch.bfloa16, + torch_dtype=torch.bfloat16, device_map="auto", ) self.messages = [ From 7bd21aac332dbaf07a36a5994329ae9c47892f24 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 09:23:08 -0500 Subject: [PATCH 43/63] docs for misral and groupchat Former-commit-id: 55a4e9acd8ea758f21b2a667130bccdab305611a --- docs/swarms/models/mistral.md | 8 +- docs/swarms/swarms/groupchat.md | 167 ++++++++++++++++++++++++++++++++ mkdocs.yml | 3 +- 3 files changed, 172 insertions(+), 6 deletions(-) diff --git a/docs/swarms/models/mistral.md b/docs/swarms/models/mistral.md index 19b7b43a..c8dc179c 100644 --- a/docs/swarms/models/mistral.md +++ b/docs/swarms/models/mistral.md @@ -1,4 +1,4 @@ -# Swarms Documentation +# `Mistral` Documentation ## Table of Contents @@ -133,9 +133,7 @@ Mistral provides two methods for running the model: The `run` method is used to generate text-based responses to a given task or input. It takes a single string parameter, `task`, and returns the generated text as a string. ```python -def run - -(self, task: str) -> str: +def run(self, task: str) -> str: """ Run the model on a given task. @@ -236,6 +234,8 @@ In this section, we provide practical examples to illustrate how to use Mistral In this example, we initialize the Mistral AI agent with custom settings: ```python +from swarms.models import Mistral + model = Mistral( ai_name="My AI Assistant", device="cpu", diff --git a/docs/swarms/swarms/groupchat.md b/docs/swarms/swarms/groupchat.md index e69de29b..b881513f 100644 --- a/docs/swarms/swarms/groupchat.md +++ b/docs/swarms/swarms/groupchat.md @@ -0,0 +1,167 @@ +# Swarms Framework Documentation + +--- + +## Overview + +The Swarms framework is a Python library designed to facilitate the creation and management of a simulated group chat environment. This environment can be used for a variety of purposes, such as training conversational agents, role-playing games, or simulating dialogues for machine learning purposes. The core functionality revolves around managing the flow of messages between different agents within the chat, as well as handling the selection and responses of these agents based on the conversation's context. + +### Purpose + +The purpose of the Swarms framework, and specifically the `GroupChat` and `GroupChatManager` classes, is to simulate a dynamic and interactive conversation between multiple agents. This simulates a real-time chat environment where each participant is represented by an agent with a specific role and behavioral patterns. These agents interact within the rules of the group chat, controlled by the `GroupChatManager`. + +### Key Features + +- **Agent Interaction**: Allows multiple agents to communicate within a group chat scenario. +- **Message Management**: Handles the storage and flow of messages within the group chat. +- **Role Play**: Enables agents to assume specific roles and interact accordingly. +- **Conversation Context**: Maintains the context of the conversation for appropriate responses by agents. + +--- + +## GroupChat Class + +The `GroupChat` class is the backbone of the Swarms framework's chat simulation. It maintains the list of agents participating in the chat, the messages that have been exchanged, and the logic to reset the chat and determine the next speaker. + +### Class Definition + +#### Parameters + +| Parameter | Type | Description | Default Value | +|------------|---------------------|--------------------------------------------------------------|---------------| +| agents | List[Flow] | List of agent flows participating in the group chat. | None | +| messages | List[Dict] | List of message dictionaries exchanged in the group chat. | None | +| max_round | int | Maximum number of rounds/messages allowed in the group chat. | 10 | +| admin_name | str | The name of the admin agent in the group chat. | "Admin" | + +#### Class Properties and Methods + +- `agent_names`: Returns a list of the names of the agents in the group chat. +- `reset()`: Clears all messages from the group chat. +- `agent_by_name(name: str) -> Flow`: Finds and returns an agent by name. +- `next_agent(agent: Flow) -> Flow`: Returns the next agent in the list. +- `select_speaker_msg() -> str`: Returns the message for selecting the next speaker. +- `select_speaker(last_speaker: Flow, selector: Flow) -> Flow`: Logic to select the next speaker based on the last speaker and the selector agent. +- `_participant_roles() -> str`: Returns a string listing all participant roles. +- `format_history(messages: List[Dict]) -> str`: Formats the history of messages for display or processing. + +### Usage Examples + +#### Example 1: Initializing a GroupChat + +```python +from swarms.structs.flow import Flow +from swarms.groupchat import GroupChat + +# Assuming Flow objects (flow1, flow2, flow3) are initialized and configured +agents = [flow1, flow2, flow3] +group_chat = GroupChat(agents=agents, messages=[], max_round=10) +``` + +#### Example 2: Resetting a GroupChat + +```python +group_chat.reset() +``` + +#### Example 3: Selecting a Speaker + +```python +last_speaker = agents[0] # Assuming this is a Flow object representing the last speaker +selector = agents[1] # Assuming this is a Flow object with the selector role + +next_speaker = group_chat.select_speaker(last_speaker, selector) +``` + +--- + +## GroupChatManager Class + +The `GroupChatManager` class acts as a controller for the `GroupChat` instance. It orchestrates the interaction between agents, prompts for tasks, and manages the rounds of conversation. + +### Class Definition + +#### Constructor Parameters + +| Parameter | Type | Description | +|------------|-------------|------------------------------------------------------| +| groupchat | GroupChat | The GroupChat instance that the manager will handle. | +| selector | Flow | The Flow object that selects the next speaker. | + +#### Methods + +- `__call__(task: str)`: Invokes the GroupChatManager with a given task string to start the conversation. + +### Usage Examples + +#### Example 1: Initializing GroupChatManager + +```python +from swarms.groupchat import GroupChat, GroupChatManager +from swarms.structs.flow import Flow + +# Initialize your agents and group chat as shown in previous examples +chat_manager = GroupChatManager(groupchat=group_chat, selector=manager) +``` + +#### Example 2: Starting a Conversation + +```python +# Start the group chat with a task +chat_history = chat_manager("Start a conversation about space exploration.") +``` + +#### Example 3: Using the Call Method + +```python +# The call method is the same as starting a conversation +chat_history = chat_manager.__call__("Discuss recent advances in AI.") +``` + +--- + +## Conclusion + +In summary, the Swarms framework offers a unique and effective solution for simulating group chat environments. Its `GroupChat` and `GroupChatManager` classes provide the necessary infrastructure to create dynamic conversations between agents, manage messages, and maintain the context of the dialogue. This framework can be instrumental in developing more sophisticated conversational agents, experimenting with social dynamics in chat environments, and providing a rich dataset for machine learning applications. + +By leveraging the framework's features, users can create complex interaction scenarios that closely mimic real-world group communication. This can prove to be a valuable asset in the fields of artificial intelligence, computational social science, and beyond. + +--- + +### Frequently Asked Questions (FAQ) + +**Q: Can the Swarms framework handle real-time interactions between agents?** + +A: The Swarms framework is designed to simulate group chat environments. While it does not handle real-time interactions as they would occur on a network, it can simulate the flow of conversation in a way that mimics real-time communication. + +**Q: Is the Swarms framework capable of natural language processing?** + +A: The framework itself is focused on the structure and management of group chats. It does not inherently include natural language processing (NLP) capabilities. However, it can be integrated with NLP tools to enhance the simulation with language understanding and generation features. + +**Q: Can I customize the roles and behaviors of agents within the framework?** + +A: Yes, the framework is designed to be flexible. You can define custom roles and behaviors for agents to fit the specific requirements of your simulation scenario. + +**Q: What are the limitations of the Swarms framework?** + +A: The framework is constrained by its design to simulate text-based group chats. It is not suitable for voice or video communication simulations. Additionally, its effectiveness depends on the sophistication of the agentsโ€™ decision-making logic, which is outside the framework itself. + +**Q: Is it possible to integrate the Swarms framework with other chat services?** + +A: The framework is can be integrated with any chat services. However, it could potentially be adapted to work with chat service APIs, where the agents could be used to simulate user behavior within a real chat application. + +**Q: How does the `GroupChatManager` select the next speaker?** + +A: The `GroupChatManager` uses a selection mechanism, which is typically based on the conversation's context and the roles of the agents, to determine the next speaker. The specifics of this mechanism can be customized to match the desired flow of the conversation. + +**Q: Can I contribute to the Swarms framework or suggest features?** + +A: As with many open-source projects, contributions and feature suggestions can usually be made through the project's repository on platforms like GitHub. It's best to check with the maintainers of the Swarms framework for their contribution guidelines. + +**Q: Are there any tutorials or community support for new users of the Swarms framework?** + +A: Documentation and usage examples are provided with the framework. Community support may be available through forums, chat groups, or the platform where the framework is hosted. Tutorials may also be available from third-party educators or in official documentation. + +**Q: What programming skills do I need to use the Swarms framework effectively?** + +A: You should have a good understanding of Python programming, including experience with classes and methods. Familiarity with the principles of agent-based modeling and conversational AI would also be beneficial. diff --git a/mkdocs.yml b/mkdocs.yml index 7413e809..3a212201 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -69,13 +69,11 @@ nav: - GodMode: "swarms/swarms/godmode.md" - Groupchat: "swarms/swarms/groupchat.md" - swarms.workers: - - AbstractWorker: "swarms/workers/base.md" - Overview: "swarms/workers/index.md" - AbstractWorker: "swarms/workers/abstract_worker.md" - swarms.agents: - AbstractAgent: "swarms/agents/abstract_agent.md" - OmniModalAgent: "swarms/agents/omni_agent.md" - - Idea2Image: "swarms/agents/idea_to_image.md" - swarms.models: - Language: - Overview: "swarms/models/index.md" @@ -85,6 +83,7 @@ nav: - Zephyr: "swarms/models/zephyr.md" - BioGPT: "swarms/models/biogpt.md" - MPT7B: "swarms/models/mpt.md" + - Mistral: "swarms/models/mistral.md" - MultiModal: - Fuyu: "swarms/models/fuyu.md" - Vilt: "swarms/models/vilt.md" From 70f5d343695b9c55fcec22e8b445c4bc17e8a0bf Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 09:23:59 -0500 Subject: [PATCH 44/63] verison Former-commit-id: 7bc6b9c5261b216476852487903fb5a538f7b512 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3cb153c4..81b197d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.2" +version = "2.0.3" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] From 517959f42407662e4341b55a66f3c8d3a59fbaf5 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:34:27 -0500 Subject: [PATCH 45/63] clean up of useless code, code, no more worker, etc Former-commit-id: bb496f4f61159e2fe8cd4c26923f4620f0031609 --- groupchat.py | 8 +++---- playground/workflow.py | 2 +- swarms/__init__.py | 1 - swarms/models/biogpt.py | 4 +++- swarms/tools/exit_conversation.py | 22 ------------------- swarms/tools/requests.py | 36 ------------------------------- 6 files changed, 8 insertions(+), 65 deletions(-) delete mode 100644 swarms/tools/exit_conversation.py delete mode 100644 swarms/tools/requests.py diff --git a/groupchat.py b/groupchat.py index 739181d1..71d40a03 100644 --- a/groupchat.py +++ b/groupchat.py @@ -14,28 +14,28 @@ llm = OpenAI( flow1 = Flow( llm=llm, max_loops=1, - system_message="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE", + system_prompt="YOU ARE SILLY, YOU OFFER NOTHING OF VALUE", name="silly", dashboard=True, ) flow2 = Flow( llm=llm, max_loops=1, - system_message="YOU ARE VERY SMART AND ANSWER RIDDLES", + system_prompt="YOU ARE VERY SMART AND ANSWER RIDDLES", name="detective", dashboard=True, ) flow3 = Flow( llm=llm, max_loops=1, - system_message="YOU MAKE RIDDLES", + system_prompt="YOU MAKE RIDDLES", name="riddler", dashboard=True, ) manager = Flow( llm=llm, max_loops=1, - system_message="YOU ARE A GROUP CHAT MANAGER", + system_prompt="YOU ARE A GROUP CHAT MANAGER", name="manager", dashboard=True, ) diff --git a/playground/workflow.py b/playground/workflow.py index a5d0ea03..78909dc7 100644 --- a/playground/workflow.py +++ b/playground/workflow.py @@ -1,5 +1,5 @@ from swarms import Workflow -from swarms.tools.autogpt import ChatOpenAI +from swarms.models import ChatOpenAI workflow = Workflow(ChatOpenAI) diff --git a/swarms/__init__.py b/swarms/__init__.py index dda0aff2..f1225d81 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -9,7 +9,6 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" from swarms.workers import * -from swarms.workers.worker import Worker from swarms.chunkers import * from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py from swarms.structs import * diff --git a/swarms/models/biogpt.py b/swarms/models/biogpt.py index f5abdf95..d8afcebd 100644 --- a/swarms/models/biogpt.py +++ b/swarms/models/biogpt.py @@ -105,12 +105,14 @@ class BioGPT: generator = pipeline( "text-generation", model=self.model, tokenizer=self.tokenizer ) - return generator( + out = generator( text, max_length=self.max_length, num_return_sequences=self.num_return_sequences, do_sample=self.do_sample, ) + + return out[0]['generated_text'] def get_features(self, text): """ diff --git a/swarms/tools/exit_conversation.py b/swarms/tools/exit_conversation.py deleted file mode 100644 index d1543e14..00000000 --- a/swarms/tools/exit_conversation.py +++ /dev/null @@ -1,22 +0,0 @@ -from langchain.tools import tool - -from swarms.tools.base import BaseToolSet, SessionGetter, ToolScope -from swarms.utils.logger import logger - - -class ExitConversation(BaseToolSet): - @tool( - name="Exit Conversation", - description="A tool to exit the conversation. " - "Use this when you want to exit the conversation. " - "The input should be a message that the conversation is over.", - scope=ToolScope.SESSION, - ) - def exit(self, message: str, get_session: SessionGetter) -> str: - """Run the tool.""" - _, executor = get_session() - del executor - - logger.debug("\nProcessed ExitConversation.") - - return message diff --git a/swarms/tools/requests.py b/swarms/tools/requests.py deleted file mode 100644 index fa60e8e4..00000000 --- a/swarms/tools/requests.py +++ /dev/null @@ -1,36 +0,0 @@ -import requests -from bs4 import BeautifulSoup - -from swarms.tools.base import BaseToolSet, tool -from swarms.utils.logger import logger - - -class RequestsGet(BaseToolSet): - @tool( - name="Requests Get", - description="A portal to the internet. " - "Use this when you need to get specific content from a website." - "Input should be a url (i.e. https://www.google.com)." - "The output will be the text response of the GET request.", - ) - def get(self, url: str) -> str: - """Run the tool.""" - html = requests.get(url).text - soup = BeautifulSoup(html) - non_readable_tags = soup.find_all( - ["script", "style", "header", "footer", "form"] - ) - - for non_readable_tag in non_readable_tags: - non_readable_tag.extract() - - content = soup.get_text("\n", strip=True) - - if len(content) > 300: - content = content[:300] + "..." - - logger.debug( - f"\nProcessed RequestsGet, Input Url: {url} " f"Output Contents: {content}" - ) - - return content From 62acf2b3c70f6ee0ded5a0221a95c89e813ff9a1 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:37:56 -0500 Subject: [PATCH 46/63] clean up unused code Former-commit-id: 7a5e82b8955d13acf5ec8229346426e124d41a4d --- pyproject.toml | 2 +- swarms/tools/mm_models.py | 13 ++++++------- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 81b197d4..f829bf41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.3" +version = "2.0.4" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/tools/mm_models.py b/swarms/tools/mm_models.py index 0b1cd281..e8da2e5c 100644 --- a/swarms/tools/mm_models.py +++ b/swarms/tools/mm_models.py @@ -19,13 +19,12 @@ from transformers import ( ) from swarms.prompts.prebuild.multi_modal_prompts import IMAGE_PROMPT -from swarms.tools.base import tool -from swarms.tools.main import BaseToolSet +from swarms.tools.tool import tool from swarms.utils.logger import logger from swarms.utils.main import BaseHandler, get_new_image_name -class MaskFormer(BaseToolSet): +class MaskFormer: def __init__(self, device): print("Initializing MaskFormer to %s" % device) self.device = device @@ -61,7 +60,7 @@ class MaskFormer(BaseToolSet): return image_mask.resize(original_image.size) -class ImageEditing(BaseToolSet): +class ImageEditing: def __init__(self, device): print("Initializing ImageEditing to %s" % device) self.device = device @@ -116,7 +115,7 @@ class ImageEditing(BaseToolSet): return updated_image_path -class InstructPix2Pix(BaseToolSet): +class InstructPix2Pix: def __init__(self, device): print("Initializing InstructPix2Pix to %s" % device) self.device = device @@ -156,7 +155,7 @@ class InstructPix2Pix(BaseToolSet): return updated_image_path -class Text2Image(BaseToolSet): +class Text2Image: def __init__(self, device): print("Initializing Text2Image to %s" % device) self.device = device @@ -190,7 +189,7 @@ class Text2Image(BaseToolSet): return image_filename -class VisualQuestionAnswering(BaseToolSet): +class VisualQuestionAnswering: def __init__(self, device): print("Initializing VisualQuestionAnswering to %s" % device) self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 From 187123854b32632b1e92b5ef7f5feb7b10461d8d Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:39:04 -0500 Subject: [PATCH 47/63] 2.0.5 Former-commit-id: 570ed6a22982c57b5fd01eff4243b3338270d2f6 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f829bf41..9b79360a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.4" +version = "2.0.5" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] From c5184cb69d1feba6d992b6a11fe2c4327ea47d9c Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Tue, 7 Nov 2023 16:46:11 -0500 Subject: [PATCH 48/63] Create SECURITY.md Former-commit-id: 60d8f25ff112c84c1cab95a6eb20a123bb96ad20 --- SECURITY.md | 150 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 SECURITY.md diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000..7755a851 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,150 @@ +# Security Policy +=============== + +## Supported Versions +------------------ + +* * * * * + +| Version | Supported | +| --- | --- | +| 2.0.5 | :white_check_mark: | +| 2.0.4 | :white_check_mark: | +| 2.0.3 | :white_check_mark: | +| 2.0.2 | :white_check_mark: | +| 2.0.1 | :white_check_mark: | +| 2.0.0 | :white_check_mark: | + +## Reporting a Vulnerability +------------------------- + +* * * * * + +If you discover a security vulnerability in any of the above versions, please report it immediately to our security team by sending an email to security@example.com. We take security vulnerabilities seriously and appreciate your efforts in disclosing them responsibly. + +Please provide detailed information on the vulnerability, including steps to reproduce, potential impact, and any known mitigations. Our security team will acknowledge receipt of your report within 24 hours and will provide regular updates on the progress of the investigation. + +Once the vulnerability has been thoroughly assessed, we will take the necessary steps to address it. This may include releasing a security patch, issuing a security advisory, or implementing other appropriate mitigations. + +We aim to respond to all vulnerability reports in a timely manner and work towards resolving them as quickly as possible. We thank you for your contribution to the security of our software. + +Please note that any vulnerability reports that are not related to the specified versions or do not provide sufficient information may be declined. + +# Security and Encryption Pathway for Swarms Framework +==================================================== + + +## Introduction +------------ + +* * * * * + +This document outlines the security and encryption pathway for the Swarms Framework. It provides guidelines and best practices to ensure the ultra security and encryption of the framework. + +## Table of Contents +----------------- + +* * * * * + +1. Authentication and Authorization +2. Secure Communication +3. Data Encryption +4. Secure Storage +5. Vulnerability Management +6. Incident Response +7. Compliance and Audit +8. Security Awareness Training +9. Third-Party Dependencies +10. Continuous Monitoring + +1\. Authentication and Authorization +------------------------------------ + +* * * * * + +- Implement strong authentication mechanisms such as two-factor authentication (2FA) or multi-factor authentication (MFA) to prevent unauthorized access. +- Use secure protocols, such as Transport Layer Security (TLS), for authentication. +- Regularly review and update access controls and permissions to ensure only authorized individuals have access to the framework. + +2\. Secure Communication +------------------------ + +* * * * * + +- Ensure that all communication between components of the framework, as well as with external systems, is encrypted using industry-standard encryption algorithms. +- Use secure communication protocols, such as HTTPS, to protect data in transit. +- Regularly update and patch communication libraries and protocols to address security vulnerabilities. + +3\. Data Encryption +------------------- + +* * * * * + +- Implement strong encryption algorithms to protect sensitive data at rest. +- Use industry-standard encryption protocols, such as AES, to encrypt data. +- Regularly rotate encryption keys and ensure the secure storage of these keys. + +4\. Secure Storage +------------------ + +* * * * * + +- Use secure storage mechanisms to protect sensitive data stored within the framework. +- Implement access controls and permissions to restrict access to sensitive data. +- Regularly review and update storage configurations to address potential vulnerabilities. + +5\. Vulnerability Management +---------------------------- + +* * * * * + +- Implement a vulnerability management process to identify and remediate security vulnerabilities in the framework. +- Regularly scan the framework for known vulnerabilities using vulnerability scanning tools. +- Establish a patch management process to apply security patches and updates in a timely manner. + +6\. Incident Response +--------------------- + +* * * * * + +- Develop and maintain an incident response plan to effectively respond to security incidents. +- Establish communication channels and escalation procedures for reporting and addressing security incidents. +- Conduct regular incident response exercises and drills to test the effectiveness of the plan. + +7\. Compliance and Audit +------------------------ + +* * * * * + +- Implement controls to ensure compliance with applicable security regulations and standards. +- Conduct regular security audits and assessments to identify and address security gaps. +- Maintain documentation of security controls and audit findings. + +8\. Security Awareness Training +------------------------------- + +* * * * * + +- Provide security awareness training to all personnel involved in the development and maintenance of the framework. +- Ensure that all personnel are aware of security best practices and their roles and responsibilities in maintaining the security of the framework. +- Regularly update and reinforce security training to address emerging threats and vulnerabilities. + +9\. Third-Party Dependencies +---------------------------- + +* * * * * + +- Regularly assess and evaluate the security of third-party libraries and dependencies used in the framework. +- Ensure that third-party dependencies are up to date and free from known vulnerabilities. +- Implement controls and monitoring to detect and mitigate risks associated with third-party dependencies. + +10\. Continuous Monitoring +-------------------------- + +* * * * * + +- Implement a continuous monitoring program to detect and respond to security events in real-time. +- Monitor system logs and network traffic to identify anomalies and potential security threats. +- Regularly review and update monitoring configurations to stay ahead of emerging threats. + +By following the guidelines outlined in this document, the ultra security and encryption of the Swarms Framework can be ensured. From 079697a91de7c9fd731fc13f15bb6eeb351add41 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:46:58 -0500 Subject: [PATCH 49/63] security Former-commit-id: f16515af6d66de5b0f0202698269a39526aaa480 --- SECURITY.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SECURITY.md b/SECURITY.md index 7755a851..476e769f 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -20,7 +20,7 @@ * * * * * -If you discover a security vulnerability in any of the above versions, please report it immediately to our security team by sending an email to security@example.com. We take security vulnerabilities seriously and appreciate your efforts in disclosing them responsibly. +If you discover a security vulnerability in any of the above versions, please report it immediately to our security team by sending an email to kye@apac.ai. We take security vulnerabilities seriously and appreciate your efforts in disclosing them responsibly. Please provide detailed information on the vulnerability, including steps to reproduce, potential impact, and any known mitigations. Our security team will acknowledge receipt of your report within 24 hours and will provide regular updates on the progress of the investigation. From 698feb32553512a318c1e3cc845b71cc3bf0acb6 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:47:28 -0500 Subject: [PATCH 50/63] security md Former-commit-id: 217dbf8cbac7c6313e038548d334699c8760a6da --- SECURITY.md | 150 ---------------------------------------------------- 1 file changed, 150 deletions(-) diff --git a/SECURITY.md b/SECURITY.md index 476e769f..e69de29b 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -1,150 +0,0 @@ -# Security Policy -=============== - -## Supported Versions ------------------- - -* * * * * - -| Version | Supported | -| --- | --- | -| 2.0.5 | :white_check_mark: | -| 2.0.4 | :white_check_mark: | -| 2.0.3 | :white_check_mark: | -| 2.0.2 | :white_check_mark: | -| 2.0.1 | :white_check_mark: | -| 2.0.0 | :white_check_mark: | - -## Reporting a Vulnerability -------------------------- - -* * * * * - -If you discover a security vulnerability in any of the above versions, please report it immediately to our security team by sending an email to kye@apac.ai. We take security vulnerabilities seriously and appreciate your efforts in disclosing them responsibly. - -Please provide detailed information on the vulnerability, including steps to reproduce, potential impact, and any known mitigations. Our security team will acknowledge receipt of your report within 24 hours and will provide regular updates on the progress of the investigation. - -Once the vulnerability has been thoroughly assessed, we will take the necessary steps to address it. This may include releasing a security patch, issuing a security advisory, or implementing other appropriate mitigations. - -We aim to respond to all vulnerability reports in a timely manner and work towards resolving them as quickly as possible. We thank you for your contribution to the security of our software. - -Please note that any vulnerability reports that are not related to the specified versions or do not provide sufficient information may be declined. - -# Security and Encryption Pathway for Swarms Framework -==================================================== - - -## Introduction ------------- - -* * * * * - -This document outlines the security and encryption pathway for the Swarms Framework. It provides guidelines and best practices to ensure the ultra security and encryption of the framework. - -## Table of Contents ------------------ - -* * * * * - -1. Authentication and Authorization -2. Secure Communication -3. Data Encryption -4. Secure Storage -5. Vulnerability Management -6. Incident Response -7. Compliance and Audit -8. Security Awareness Training -9. Third-Party Dependencies -10. Continuous Monitoring - -1\. Authentication and Authorization ------------------------------------- - -* * * * * - -- Implement strong authentication mechanisms such as two-factor authentication (2FA) or multi-factor authentication (MFA) to prevent unauthorized access. -- Use secure protocols, such as Transport Layer Security (TLS), for authentication. -- Regularly review and update access controls and permissions to ensure only authorized individuals have access to the framework. - -2\. Secure Communication ------------------------- - -* * * * * - -- Ensure that all communication between components of the framework, as well as with external systems, is encrypted using industry-standard encryption algorithms. -- Use secure communication protocols, such as HTTPS, to protect data in transit. -- Regularly update and patch communication libraries and protocols to address security vulnerabilities. - -3\. Data Encryption -------------------- - -* * * * * - -- Implement strong encryption algorithms to protect sensitive data at rest. -- Use industry-standard encryption protocols, such as AES, to encrypt data. -- Regularly rotate encryption keys and ensure the secure storage of these keys. - -4\. Secure Storage ------------------- - -* * * * * - -- Use secure storage mechanisms to protect sensitive data stored within the framework. -- Implement access controls and permissions to restrict access to sensitive data. -- Regularly review and update storage configurations to address potential vulnerabilities. - -5\. Vulnerability Management ----------------------------- - -* * * * * - -- Implement a vulnerability management process to identify and remediate security vulnerabilities in the framework. -- Regularly scan the framework for known vulnerabilities using vulnerability scanning tools. -- Establish a patch management process to apply security patches and updates in a timely manner. - -6\. Incident Response ---------------------- - -* * * * * - -- Develop and maintain an incident response plan to effectively respond to security incidents. -- Establish communication channels and escalation procedures for reporting and addressing security incidents. -- Conduct regular incident response exercises and drills to test the effectiveness of the plan. - -7\. Compliance and Audit ------------------------- - -* * * * * - -- Implement controls to ensure compliance with applicable security regulations and standards. -- Conduct regular security audits and assessments to identify and address security gaps. -- Maintain documentation of security controls and audit findings. - -8\. Security Awareness Training -------------------------------- - -* * * * * - -- Provide security awareness training to all personnel involved in the development and maintenance of the framework. -- Ensure that all personnel are aware of security best practices and their roles and responsibilities in maintaining the security of the framework. -- Regularly update and reinforce security training to address emerging threats and vulnerabilities. - -9\. Third-Party Dependencies ----------------------------- - -* * * * * - -- Regularly assess and evaluate the security of third-party libraries and dependencies used in the framework. -- Ensure that third-party dependencies are up to date and free from known vulnerabilities. -- Implement controls and monitoring to detect and mitigate risks associated with third-party dependencies. - -10\. Continuous Monitoring --------------------------- - -* * * * * - -- Implement a continuous monitoring program to detect and respond to security events in real-time. -- Monitor system logs and network traffic to identify anomalies and potential security threats. -- Regularly review and update monitoring configurations to stay ahead of emerging threats. - -By following the guidelines outlined in this document, the ultra security and encryption of the Swarms Framework can be ensured. From ba4a7046acf953b8b45fbf3c0af8397c2d3f01ea Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Tue, 7 Nov 2023 16:48:50 -0500 Subject: [PATCH 51/63] Create CODE_OF_CONDUCT.md Former-commit-id: 3d186fdf60fe130e0b56eb305c1647bc9ab1c37b --- CODE_OF_CONDUCT.md | 128 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..afbec392 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +kye@apac.ai. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. From 121795203ad6d1dd433eb16824dd733f13c77bb3 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 7 Nov 2023 16:59:16 -0500 Subject: [PATCH 52/63] security md Former-commit-id: e9c712f8f3e4a0ccae08c504336adbd15693ab69 --- SECURITY.md | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/SECURITY.md b/SECURITY.md index e69de29b..2de3c275 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -0,0 +1,32 @@ +# Security Policy +=============== + +## Supported Versions +------------------ + +* * * * * + +| Version | Supported | +| --- | --- | +| 2.0.5 | :white_check_mark: | +| 2.0.4 | :white_check_mark: | +| 2.0.3 | :white_check_mark: | +| 2.0.2 | :white_check_mark: | +| 2.0.1 | :white_check_mark: | +| 2.0.0 | :white_check_mark: | + +# Reporting a Vulnerability +------------------------- + +* * * * * + +If you discover a security vulnerability in any of the above versions, please report it immediately to our security team by sending an email to kye@apac.ai. We take security vulnerabilities seriously and appreciate your efforts in disclosing them responsibly. + +Please provide detailed information on the vulnerability, including steps to reproduce, potential impact, and any known mitigations. Our security team will acknowledge receipt of your report within 24 hours and will provide regular updates on the progress of the investigation. + +Once the vulnerability has been thoroughly assessed, we will take the necessary steps to address it. This may include releasing a security patch, issuing a security advisory, or implementing other appropriate mitigations. + +We aim to respond to all vulnerability reports in a timely manner and work towards resolving them as quickly as possible. We thank you for your contribution to the security of our software. + +Please note that any vulnerability reports that are not related to the specified versions or do not provide sufficient information may be declined. + From faf2c63fd4a42829ffa675b75088f99160024932 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 02:29:56 -0500 Subject: [PATCH 53/63] accountant swarm + autotemp agent Former-commit-id: 11b02a5d13596d7dfdb6f5e139467663f982d96b --- accountant_team.py | 61 ++++++++++++++++++ bank_statement_2.jpg | Bin 0 -> 550804 bytes demos/autotemp.py | 101 ++++++++++++++++++++++++++++++ pyproject.toml | 2 +- swarms/agents/profitpilot.py | 1 - swarms/chunkers/omni_chunker.py | 11 +--- swarms/models/biogpt.py | 4 +- swarms/models/nougat.py | 5 +- swarms/models/openai_tokenizer.py | 10 +-- swarms/swarms/autoscaler.py | 6 +- 10 files changed, 176 insertions(+), 25 deletions(-) create mode 100644 accountant_team.py create mode 100644 bank_statement_2.jpg create mode 100644 demos/autotemp.py diff --git a/accountant_team.py b/accountant_team.py new file mode 100644 index 00000000..20cd5feb --- /dev/null +++ b/accountant_team.py @@ -0,0 +1,61 @@ +# !pip install --upgrade swarms==2.0.6 + + + +from swarms.models import OpenAIChat +from swarms.models.nougat import Nougat +from swarms.structs import Flow +from swarms.structs.sequential_workflow import SequentialWorkflow + +# # URL of the image of the financial document +IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg" + +# Example usage +api_key = "" # Your actual API key here + +# Initialize the OCR model +def ocr_model(img: str): + ocr = Nougat() + analyze_finance_docs = ocr(img) + return str(analyze_finance_docs) + +# Initialize the language flow +llm = OpenAIChat( + model_name="gpt-4-turbo", + openai_api_key=api_key, + temperature=0.5, + max_tokens=3000, +) + +# Create a prompt for the language model +def summary_agent_prompt(analyzed_doc: str): + analyzed_doc = ocr_model(img=analyzed_doc) + return f""" + Generate an actionable summary of this financial document, provide bulletpoints: + + Here is the Analyzed Document: + --- + {analyzed_doc} + """ + +# Initialize the Flow with the language flow +flow1 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create another Flow for a different task +flow2 = Flow(llm=llm, max_loops=1, dashboard=False) + +# Create the workflow +workflow = SequentialWorkflow(max_loops=1) + +# Add tasks to the workflow +workflow.add(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL), flow1) + +# Suppose the next task takes the output of the first task as input +workflow.add("Provide an actionable step by step plan on how to cut costs from the analyzed financial document.", flow2) + +# Run the workflow +workflow.run() + +# Output the results +for task in workflow.tasks: + print(f"Task: {task.description}, Result: {task.result}") \ No newline at end of file diff --git a/bank_statement_2.jpg b/bank_statement_2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..dbc8a4e9e9fcc694700fac175ab999fdcfd5b3f5 GIT binary patch literal 550804 zcmeFa2Urx%(kML35+zBNj07=}a|V|vAWF_@S%R=EunQ;%sHmu*C=w(qQ9wX)5>Qc4 zKyuECl7qqmvas;ag8J$?=YHRJ-h1xzKliyd?o4%6b#--hnC_mMc6VU+1Ltv#3vSK; zpsx=c2LON^AcN2YP!NKEe*lCHAlZWfz!Ad!6Fv_)`kjXeXa62g)^NA(@xZk`0pF+f~Y zTwGpMR$fe$M^sK;N>*M<43q{Q*<(fP92N3W0v-CH=K55Z=Ga z0y+L`S%@G%WI)7;e#&P5vM&zM2JfI%_V2p`yNdwTUMcKg`T%5i382}F0Zx|*e7nQI zHGqnooScH3ih_cQj`9E{9TP1T6)n>t1_mYuhC_5zd*SiGbY~03A8-2)GM{@B&115GWmFcN8on#qMi}3aBLsWG{dU69N=~ z5D}A*l95v!padJh??ebd1pPrg2tbG-P$FU?5;9V95-61fNTh=jAEp;0Q9I|r!0UZQ zoRsnYz+^8%(6GVpVv&C{cgl&l{fZHt;=Ycx%h|0Wmh!yPjAoxL`0yr z#CtlCk&;O5sc=|~o*2}?o0ow^{K|bsf(9=QJHJTqJAUxF8U-_Af}MAUh)U zstZ&ioyAGRL>hzm_)~@dwFZKJsjxc)P(kL)asQ-;)q#T;+rWvd>%^6SvEQ|-PKxM;!fqk{xvj+ zrBpf3Kj`t;oNG2tD`&>cr+;>H)52oCLh#r?zl1<*VRo#Eqwu)pxcg1>gYV~vBAzE&UE2kUp)s?4)TrV@yUwzdxZ>wEO68wI zhU}(7)aGz&n9V9=L!%$;;XG7V&7HVrWa9mBh=TLtPJ35Kx|WW#4}ESVHO;MSdY+%( z6q@k|AztubvH5iK{;Wc38)~Vhdt`JM*c{TD=b}U2Ey!EI4kGT)#=gxjC|cyo8V{Ml zE7jMPMd{goR+(pJ7sB2#W1)VzWv)$9U$qk#Sl_t|^h*cd5e#x@6v}N45P6sTaVALb zi1hQ0O;k4yxeH{Q;WTpN^WD`Y0Y%cJjBMGptOE-nY13?P5B7_O1xK}B$k{5d9>wkg zQrL}w=5C1sQyjANoXbpZ@o2i6H+I!lZP_Sr=IAoqKPJN4Q$njAa8rPwN9)?I^}+Ow|wX4Q96`r=A9pC_KgpV$S|$2&tB`Ll&b!YH|hS$l6e z1X{bplllV_Y4TgC-G`=Xup)3)9Jf&~UcBad=z7A;drwcRhxqc_PrVC#$7cs^`)X4N zZ3yqaStik2ebXUiA>ttkBt#yr> zhQ?rRKF`F@#a#_pYueS6)Y*_pp&g`7vT&bdsk4(T{VSPzZ+4D;{L#_k_lX|6KxlFM zHx&ale&cDGOv{0Yk&JUoYaym_K-}9W>0h_K!Un z(jb;ypfQ9whq->YYe~M3uUPy%TFm_&+I9*TS=xnbNQ|uNk*^Tj8tdCy4++Jyd^35t zl;{=672vDyMMcse^kVUecrM3c71dg;bcM^T%3_O>Nq+AJJ}WttD%JR9>z(fMn_I%m zUs7>*&U(G2DRh^2Rk-!(%s1x^ld?s;@MvB3HrMOu(Grz;sBK^Wjp2l;VqK5?%tD^( z#+r-Djf;5pUEtMWnY^_YXt`rY|Bb7!wr__!Rpq;eWG=fajqBH^T#50s5nUXW%Y2&K zS3FI6Wwt!XZ?I<2Mrg-jOC@o~SG_{KsW{0QQZK%A7e0uZ7q949}XmQC8w+nb1EQVN;yB(6cMdl@4aZ1n$iK_9Tkyh!` zJFky97j_rXhIS`|Czqwwx49=SIk#}{+%XTVD-GwIt>Em{p?W}j)2~TkRszqRvkO@3 z=mqz$rLoJNjOwr@xg$EeAyuH@9~g3f7cj%|FBIkFdiG%AL=Kj|7Q!45WH^qLZ

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Generate outputs at a range of temperature settings. + 2. Evaluate each output using the default temperature setting. + 3. Select the best output based on the evaluation score. + 4. Return the best output. + + + Args: + temperature (float, optional): The default temperature setting to use. Defaults to 0.5. + api_key (str, optional): Your OpenAI API key. Defaults to None. + alt_temps ([type], optional): A list of alternative temperature settings to try. Defaults to None. + auto_select (bool, optional): If True, the best temperature setting will be automatically selected. Defaults to True. + max_workers (int, optional): The maximum number of workers to use when generating outputs. Defaults to 6. + + Returns: + [type]: [description] + + Examples: + >>> from swarms.demos.autotemp import AutoTemp + >>> autotemp = AutoTemp() + >>> autotemp.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.", "0.4,0.6,0.8,1.0,1.2,1.4") + Best AutoTemp Output (Temp 0.4 | Score: 100.0): + Generate a 10,000 word blog on mental clarity and the benefits of meditation. + + """ + + def __init__( + self, + temperature: float = 0.5, + api_key: str = None, + alt_temps=None, + auto_select=True, + max_workers=6, + ): + self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4] + self.auto_select = auto_select + self.max_workers = max_workers + self.temperature = temperature + self.alt_temps = alt_temps + self.llm = OpenAIChat( + openai_api_key=api_key, + temperature=temperature, + ) + + def evaluate_output(self, output: str): + """Evaluate the output using the default temperature setting.""" + eval_prompt = f""" + Evaluate the following output which was generated at a temperature setting of {self.temperature}. + Provide a precise score from 0.0 to 100.0, considering the criteria of relevance, clarity, utility, pride, and delight. + + Output to evaluate: + --- + {output} + --- + """ + score_text = self.llm(prompt=eval_prompt) + score_match = re.search(r"\b\d+(\.\d)?\b", score_text) + return round(float(score_match.group()), 1) if score_match else 0.0 + + def run(self, task: str, temperature_string): + """Run the AutoTemp agent.""" + temperature_list = [ + float(temp.strip()) for temp in temperature_string.split(",") + ] + outputs = {} + scores = {} + with ThreadPoolExecutor(max_workers=self.max_workers) as executor: + future_to_temp = { + executor.submit(self.llm.generate, task, temp): temp + for temp in temperature_list + } + for future in as_completed(future_to_temp): + temp = future_to_temp[future] + output_text = future.result() + outputs[temp] = output_text + scores[temp] = self.evaluate_output(output_text, temp) + + if not scores: + return "No valid outputs generated.", None + + sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True) + best_temp, best_score = sorted_scores[0] + best_output = outputs[best_temp] + + return ( + f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}" + if self.auto_select + else "\n".join( + f"Temp {temp} | Score: {score}:\n{outputs[temp]}" + for temp, score in sorted_scores + ) + ) diff --git a/pyproject.toml b/pyproject.toml index 9b79360a..f6369d6a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.5" +version = "2.0.7" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/agents/profitpilot.py b/swarms/agents/profitpilot.py index ac1d0b44..8de7dbf0 100644 --- a/swarms/agents/profitpilot.py +++ b/swarms/agents/profitpilot.py @@ -165,7 +165,6 @@ def get_tools(product_catalog): func=knowledge_base.run, description="useful for when you need to answer questions about product information", ), - # omnimodal agent ] diff --git a/swarms/chunkers/omni_chunker.py b/swarms/chunkers/omni_chunker.py index dca569ea..70a11380 100644 --- a/swarms/chunkers/omni_chunker.py +++ b/swarms/chunkers/omni_chunker.py @@ -20,21 +20,15 @@ import os import sys - - @dataclass class OmniChunker: - """ - - - """ + """ """ + chunk_size: int = 1000 beautify: bool = False use_tokenizer: bool = False tokenizer: Optional[Callable[[str], List[str]]] = None - - def __call__(self, file_path: str) -> List[str]: """ Chunk the given file into parts of size `chunk_size`. @@ -121,4 +115,3 @@ class OmniChunker: "cyan", ) ) - diff --git a/swarms/models/biogpt.py b/swarms/models/biogpt.py index d8afcebd..0bca8f30 100644 --- a/swarms/models/biogpt.py +++ b/swarms/models/biogpt.py @@ -111,8 +111,8 @@ class BioGPT: num_return_sequences=self.num_return_sequences, do_sample=self.do_sample, ) - - return out[0]['generated_text'] + + return out[0]["generated_text"] def get_features(self, text): """ diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index cc154283..34465c73 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -61,9 +61,10 @@ class Nougat: pixel_values.to(self.device), min_length=self.min_length, max_new_tokens=self.max_new_tokens, - bad_words_ids=[[self.processor.unk_token - id]], ) sequence = self.processor.batch_decode(outputs, skip_special_tokens=True)[0] sequence = self.processor.post_process_generation(sequence, fix_markdown=False) - return sequence + + out = print(repr(sequence)) + return out diff --git a/swarms/models/openai_tokenizer.py b/swarms/models/openai_tokenizer.py index b4e375cc..ee0ea363 100644 --- a/swarms/models/openai_tokenizer.py +++ b/swarms/models/openai_tokenizer.py @@ -80,9 +80,7 @@ class OpenAITokenizer(BaseTokenizer): return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset - def count_tokens( - self, text: str | list, model: Optional[str] = None - ) -> int: + def count_tokens(self, text: str | list, model: Optional[str] = None) -> int: """ Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb @@ -144,7 +142,5 @@ class OpenAITokenizer(BaseTokenizer): return num_tokens else: return len( - self.encoding.encode( - text, allowed_special=set(self.stop_sequences) - ) - ) \ No newline at end of file + self.encoding.encode(text, allowed_special=set(self.stop_sequences)) + ) diff --git a/swarms/swarms/autoscaler.py b/swarms/swarms/autoscaler.py index 55870112..5f6bedde 100644 --- a/swarms/swarms/autoscaler.py +++ b/swarms/swarms/autoscaler.py @@ -2,7 +2,7 @@ import queue import threading from time import sleep from swarms.utils.decorators import error_decorator, log_decorator, timing_decorator -from swarms.workers.worker import Worker +from swarms.structs.flow import Flow class AutoScaler: @@ -52,7 +52,7 @@ class AutoScaler: busy_threshold=0.7, agent=None, ): - self.agent = agent or Worker + self.agent = agent or Flow self.agents_pool = [self.agent() for _ in range(initial_agents)] self.task_queue = queue.Queue() self.scale_up_factor = scale_up_factor @@ -71,7 +71,7 @@ class AutoScaler: with self.lock: new_agents_counts = len(self.agents_pool) * self.scale_up_factor for _ in range(new_agents_counts): - self.agents_pool.append(Worker()) + self.agents_pool.append(Flow()) def scale_down(self): """scale down""" From da120e1aef8fdd2053a8ec3de3c0cbdc30e6b945 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 13:49:02 -0500 Subject: [PATCH 54/63] anthropic working Former-commit-id: f74a4da9340619a12f7ec237769828ba2a0aae12 --- .../accountant_team/accountant_team.py | 4 +- .../accountant_team/bank_statement_2.jpg | Bin playground/models/anthropic_example.py | 11 + pyproject.toml | 2 +- swarms/models/__init__.py | 3 +- swarms/models/anthropic.py | 581 +++++++++++++++--- swarms/structs/flow.py | 1 + swarms/structs/sequential_workflow.py | 26 + 8 files changed, 543 insertions(+), 85 deletions(-) rename accountant_team.py => demos/accountant_team/accountant_team.py (90%) rename bank_statement_2.jpg => demos/accountant_team/bank_statement_2.jpg (100%) create mode 100644 playground/models/anthropic_example.py diff --git a/accountant_team.py b/demos/accountant_team/accountant_team.py similarity index 90% rename from accountant_team.py rename to demos/accountant_team/accountant_team.py index 20cd5feb..459da830 100644 --- a/accountant_team.py +++ b/demos/accountant_team/accountant_team.py @@ -11,7 +11,7 @@ from swarms.structs.sequential_workflow import SequentialWorkflow IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg" # Example usage -api_key = "" # Your actual API key here +api_key = "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" # Your actual API key here # Initialize the OCR model def ocr_model(img: str): @@ -21,10 +21,8 @@ def ocr_model(img: str): # Initialize the language flow llm = OpenAIChat( - model_name="gpt-4-turbo", openai_api_key=api_key, temperature=0.5, - max_tokens=3000, ) # Create a prompt for the language model diff --git a/bank_statement_2.jpg b/demos/accountant_team/bank_statement_2.jpg similarity index 100% rename from bank_statement_2.jpg rename to demos/accountant_team/bank_statement_2.jpg diff --git a/playground/models/anthropic_example.py b/playground/models/anthropic_example.py new file mode 100644 index 00000000..695dfe62 --- /dev/null +++ b/playground/models/anthropic_example.py @@ -0,0 +1,11 @@ +from swarms.models.anthropic import Anthropic + + +model = Anthropic( + anthropic_api_key="" +) + + +task = "Say hello to" + +print(model(task)) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f6369d6a..3709ebc0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.7" +version = "2.0.8" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 26c06066..c7868196 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -19,10 +19,9 @@ from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA # from swarms.models.gpt4v import GPT4Vision # from swarms.models.dalle3 import Dalle3 - # from swarms.models.distilled_whisperx import DistilWhisperModel - # from swarms.models.fuyu import Fuyu # Not working, wait until they update + import sys log_file = open("errors.txt", "w") diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index cc3931bb..24c3c126 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -1,65 +1,292 @@ -import requests -import os +import contextlib +import datetime +import functools +import importlib +import re +import warnings +from importlib.metadata import version +from typing import ( + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Mapping, + Optional, + Set, + Tuple, + Union, +) +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.llms.base import LLM +from langchain.pydantic_v1 import Field, SecretStr, root_validator +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.output import GenerationChunk +from langchain.schema.prompt import PromptValue +from langchain.utils import ( + check_package_version, + get_from_dict_or_env, + get_pydantic_field_names, +) +from packaging.version import parse +from requests import HTTPError, Response -class Anthropic: + +def xor_args(*arg_groups: Tuple[str, ...]) -> Callable: + """Validate specified keyword args are mutually exclusive.""" + + def decorator(func: Callable) -> Callable: + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + """Validate exactly one arg in each group is not None.""" + counts = [ + sum(1 for arg in arg_group if kwargs.get(arg) is not None) + for arg_group in arg_groups + ] + invalid_groups = [i for i, count in enumerate(counts) if count != 1] + if invalid_groups: + invalid_group_names = [", ".join(arg_groups[i]) for i in invalid_groups] + raise ValueError( + "Exactly one argument in each of the following" + " groups must be defined:" + f" {', '.join(invalid_group_names)}" + ) + return func(*args, **kwargs) + + return wrapper + + return decorator + + +def raise_for_status_with_text(response: Response) -> None: + """Raise an error with the response text.""" + try: + response.raise_for_status() + except HTTPError as e: + raise ValueError(response.text) from e + + +@contextlib.contextmanager +def mock_now(dt_value): # type: ignore + """Context manager for mocking out datetime.now() in unit tests. + + Example: + with mock_now(datetime.datetime(2011, 2, 3, 10, 11)): + assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11) """ - Anthropic large language models. + class MockDateTime(datetime.datetime): + """Mock datetime.datetime.now() with a fixed datetime.""" + + @classmethod + def now(cls): # type: ignore + # Create a copy of dt_value. + return datetime.datetime( + dt_value.year, + dt_value.month, + dt_value.day, + dt_value.hour, + dt_value.minute, + dt_value.second, + dt_value.microsecond, + dt_value.tzinfo, + ) + + real_datetime = datetime.datetime + datetime.datetime = MockDateTime + try: + yield datetime.datetime + finally: + datetime.datetime = real_datetime + + +def guard_import( + module_name: str, *, pip_name: Optional[str] = None, package: Optional[str] = None +) -> Any: + """Dynamically imports a module and raises a helpful exception if the module is not + installed.""" + try: + module = importlib.import_module(module_name, package) + except ImportError: + raise ImportError( + f"Could not import {module_name} python package. " + f"Please install it with `pip install {pip_name or module_name}`." + ) + return module + + +def check_package_version( + package: str, + lt_version: Optional[str] = None, + lte_version: Optional[str] = None, + gt_version: Optional[str] = None, + gte_version: Optional[str] = None, +) -> None: + """Check the version of a package.""" + imported_version = parse(version(package)) + if lt_version is not None and imported_version >= parse(lt_version): + raise ValueError( + f"Expected {package} version to be < {lt_version}. Received " + f"{imported_version}." + ) + if lte_version is not None and imported_version > parse(lte_version): + raise ValueError( + f"Expected {package} version to be <= {lte_version}. Received " + f"{imported_version}." + ) + if gt_version is not None and imported_version <= parse(gt_version): + raise ValueError( + f"Expected {package} version to be > {gt_version}. Received " + f"{imported_version}." + ) + if gte_version is not None and imported_version < parse(gte_version): + raise ValueError( + f"Expected {package} version to be >= {gte_version}. Received " + f"{imported_version}." + ) + + +def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]: + """Get field names, including aliases, for a pydantic class. Args: - model: The model to use. Defaults to "claude-2". - max_tokens_to_sample: The maximum number of tokens to sample. - temperature: The temperature to use for sampling. - top_k: The top_k to use for sampling. - top_p: The top_p to use for sampling. - streaming: Whether to stream the response or not. - default_request_timeout: The default request timeout to use. - - - Attributes: - model: The model to use. - max_tokens_to_sample: The maximum number of tokens to sample. - temperature: The temperature to use for sampling. - top_k: The top_k to use for sampling. - top_p: The top_p to use for sampling. - streaming: Whether to stream the response or not. - default_request_timeout: The default request timeout to use. - anthropic_api_url: The API URL to use. - anthropic_api_key: The API key to use. - - Usage: - model_wrapper = Anthropic() - completion = model_wrapper("Hello, my name is") - print(completion) + pydantic_cls: Pydantic class.""" + all_required_field_names = set() + for field in pydantic_cls.__fields__.values(): + all_required_field_names.add(field.name) + if field.has_alias: + all_required_field_names.add(field.alias) + return all_required_field_names + + +def build_extra_kwargs( + extra_kwargs: Dict[str, Any], + values: Dict[str, Any], + all_required_field_names: Set[str], +) -> Dict[str, Any]: + """Build extra kwargs from values and extra_kwargs. + Args: + extra_kwargs: Extra kwargs passed in by user. + values: Values passed in by user. + all_required_field_names: All required field names for the pydantic class. """ + for field_name in list(values): + if field_name in extra_kwargs: + raise ValueError(f"Found {field_name} supplied twice.") + if field_name not in all_required_field_names: + warnings.warn( + f"""WARNING! {field_name} is not default parameter. + {field_name} was transferred to model_kwargs. + Please confirm that {field_name} is what you intended.""" + ) + extra_kwargs[field_name] = values.pop(field_name) - def __init__( - self, - model="claude-2", - max_tokens_to_sample=256, - temperature=None, - top_k=None, - top_p=None, - streaming=False, - default_request_timeout=None, - api_key: str = None, - ): - self.model = model - self.max_tokens_to_sample = max_tokens_to_sample - self.temperature = temperature - self.top_k = top_k - self.top_p = top_p - self.streaming = streaming - self.default_request_timeout = default_request_timeout or 600 - self.anthropic_api_url = os.getenv( - "ANTHROPIC_API_URL", "https://api.anthropic.com" + invalid_model_kwargs = all_required_field_names.intersection(extra_kwargs.keys()) + if invalid_model_kwargs: + raise ValueError( + f"Parameters {invalid_model_kwargs} should be specified explicitly. " + f"Instead they were passed in as part of `model_kwargs` parameter." ) - self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY") - self.api_key = api_key - def _default_params(self): + return extra_kwargs + + +def convert_to_secret_str(value: Union[SecretStr, str]) -> SecretStr: + """Convert a string to a SecretStr if needed.""" + if isinstance(value, SecretStr): + return value + return SecretStr(value) + + +class _AnthropicCommon(BaseLanguageModel): + client: Any = None #: :meta private: + async_client: Any = None #: :meta private: + model: str = Field(default="claude-2", alias="model_name") + """Model name to use.""" + + max_tokens_to_sample: int = Field(default=256, alias="max_tokens") + """Denotes the number of tokens to predict per generation.""" + + temperature: Optional[float] = None + """A non-negative float that tunes the degree of randomness in generation.""" + + top_k: Optional[int] = None + """Number of most likely tokens to consider at each step.""" + + top_p: Optional[float] = None + """Total probability mass of tokens to consider at each step.""" + + streaming: bool = False + """Whether to stream the results.""" + + default_request_timeout: Optional[float] = None + """Timeout for requests to Anthropic Completion API. Default is 600 seconds.""" + + anthropic_api_url: Optional[str] = None + + anthropic_api_key: Optional[SecretStr] = None + + HUMAN_PROMPT: Optional[str] = None + AI_PROMPT: Optional[str] = None + count_tokens: Optional[Callable[[str], int]] = None + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + + @root_validator(pre=True) + def build_extra(cls, values: Dict) -> Dict: + extra = values.get("model_kwargs", {}) + all_required_field_names = get_pydantic_field_names(cls) + values["model_kwargs"] = build_extra_kwargs( + extra, values, all_required_field_names + ) + return values + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + values["anthropic_api_key"] = convert_to_secret_str( + get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY") + ) + # Get custom api url from environment. + values["anthropic_api_url"] = get_from_dict_or_env( + values, + "anthropic_api_url", + "ANTHROPIC_API_URL", + default="https://api.anthropic.com", + ) + + try: + import anthropic + + check_package_version("anthropic", gte_version="0.3") + values["client"] = anthropic.Anthropic( + base_url=values["anthropic_api_url"], + api_key=values["anthropic_api_key"].get_secret_value(), + timeout=values["default_request_timeout"], + ) + values["async_client"] = anthropic.AsyncAnthropic( + base_url=values["anthropic_api_url"], + api_key=values["anthropic_api_key"].get_secret_value(), + timeout=values["default_request_timeout"], + ) + values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT + values["AI_PROMPT"] = anthropic.AI_PROMPT + values["count_tokens"] = values["client"].count_tokens + + except ImportError: + raise ImportError( + "Could not import anthropic python package. " + "Please it install it with `pip install anthropic`." + ) + return values + + @property + def _default_params(self) -> Mapping[str, Any]: """Get the default parameters for calling Anthropic API.""" d = { "max_tokens_to_sample": self.max_tokens_to_sample, @@ -71,33 +298,229 @@ class Anthropic: d["top_k"] = self.top_k if self.top_p is not None: d["top_p"] = self.top_p - return d - - def run(self, task: str, stop=None): - """Call out to Anthropic's completion endpoint.""" - api_key = self.api_key or self.anthropic_api_key - stop = stop or [] - params = self._default_params() - headers = {"Authorization": f"Bearer {api_key}"} - data = {"prompt": task, "stop_sequences": stop, **params} - response = requests.post( - f"{self.anthropic_api_url}/completions", - headers=headers, - json=data, - timeout=self.default_request_timeout, + return {**d, **self.model_kwargs} + + @property + def _identifying_params(self) -> Mapping[str, Any]: + """Get the identifying parameters.""" + return {**{}, **self._default_params} + + def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]: + if not self.HUMAN_PROMPT or not self.AI_PROMPT: + raise NameError("Please ensure the anthropic package is loaded") + + if stop is None: + stop = [] + + # Never want model to invent new turns of Human / Assistant dialog. + stop.extend([self.HUMAN_PROMPT]) + + return stop + + +class Anthropic(LLM, _AnthropicCommon): + """Anthropic large language models. + + To use, you should have the ``anthropic`` python package installed, and the + environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass + it as a named parameter to the constructor. + + Example: + .. code-block:: python + + import anthropic + from langchain.llms import Anthropic + + model = Anthropic(model="", anthropic_api_key="my-api-key") + + # Simplest invocation, automatically wrapped with HUMAN_PROMPT + # and AI_PROMPT. + response = model("What are the biggest risks facing humanity?") + + # Or if you want to use the chat mode, build a few-shot-prompt, or + # put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT: + raw_prompt = "What are the biggest risks facing humanity?" + prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}" + response = model(prompt) + """ + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + arbitrary_types_allowed = True + + @root_validator() + def raise_warning(cls, values: Dict) -> Dict: + """Raise warning that this class is deprecated.""" + warnings.warn( + "This Anthropic LLM is deprecated. " + "Please use `from langchain.chat_models import ChatAnthropic` instead" ) - return response.json().get("completion") - - def __call__(self, task: str, stop=None): - """Call out to Anthropic's completion endpoint.""" - stop = stop or [] - params = self._default_params() - headers = {"Authorization": f"Bearer {self.anthropic_api_key}"} - data = {"prompt": task, "stop_sequences": stop, **params} - response = requests.post( - f"{self.anthropic_api_url}/completions", - headers=headers, - json=data, - timeout=self.default_request_timeout, + return values + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "anthropic-llm" + + def _wrap_prompt(self, prompt: str) -> str: + if not self.HUMAN_PROMPT or not self.AI_PROMPT: + raise NameError("Please ensure the anthropic package is loaded") + + if prompt.startswith(self.HUMAN_PROMPT): + return prompt # Already wrapped. + + # Guard against common errors in specifying wrong number of newlines. + corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt) + if n_subs == 1: + return corrected_prompt + + # As a last resort, wrap the prompt ourselves to emulate instruct-style. + return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n" + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + r"""Call out to Anthropic's completion endpoint. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + + Returns: + The string generated by the model. + + Example: + .. code-block:: python + + prompt = "What are the biggest risks facing humanity?" + prompt = f"\n\nHuman: {prompt}\n\nAssistant:" + response = model(prompt) + + """ + if self.streaming: + completion = "" + for chunk in self._stream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + completion += chunk.text + return completion + + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + response = self.client.completions.create( + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + **params, ) - return response.json().get("completion") + return response.completion + + def convert_prompt(self, prompt: PromptValue) -> str: + return self._wrap_prompt(prompt.to_string()) + + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call out to Anthropic's completion endpoint asynchronously.""" + if self.streaming: + completion = "" + async for chunk in self._astream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + completion += chunk.text + return completion + + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + + response = await self.async_client.completions.create( + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + **params, + ) + return response.completion + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + r"""Call Anthropic completion_stream and return the resulting generator. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + Returns: + A generator representing the stream of tokens from Anthropic. + Example: + .. code-block:: python + + prompt = "Write a poem about a stream." + prompt = f"\n\nHuman: {prompt}\n\nAssistant:" + generator = anthropic.stream(prompt) + for token in generator: + yield token + """ + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + + for token in self.client.completions.create( + prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params + ): + chunk = GenerationChunk(text=token.completion) + yield chunk + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) + + async def _astream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[GenerationChunk]: + r"""Call Anthropic completion_stream and return the resulting generator. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + Returns: + A generator representing the stream of tokens from Anthropic. + Example: + .. code-block:: python + prompt = "Write a poem about a stream." + prompt = f"\n\nHuman: {prompt}\n\nAssistant:" + generator = anthropic.stream(prompt) + for token in generator: + yield token + """ + stop = self._get_anthropic_stop(stop) + params = {**self._default_params, **kwargs} + + async for token in await self.async_client.completions.create( + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + stream=True, + **params, + ): + chunk = GenerationChunk(text=token.completion) + yield chunk + if run_manager: + await run_manager.on_llm_new_token(chunk.text, chunk=chunk) + + def get_num_tokens(self, text: str) -> int: + """Calculate number of tokens.""" + if not self.count_tokens: + raise NameError("Please ensure the anthropic package is loaded") + return self.count_tokens(text) \ No newline at end of file diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 9ff021f4..4ba0ca4a 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -544,6 +544,7 @@ class Flow: def graceful_shutdown(self): """Gracefully shutdown the system saving the state""" + print(colored("Shutting down the system...", "red")) return self.save_state("flow_state.json") def run_with_timeout(self, task: str, timeout: int = 60) -> str: diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index 802e5442..251447ba 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -265,6 +265,32 @@ class SequentialWorkflow: attrs=["bold", "underline"], ) ) + + def workflow_shutdown(self, **kwargs) -> None: + print( + colored( + """ + Sequential Workflow Shutdown...""", + "red", + attrs=["bold", "underline"], + ) + ) + + def add_objective_to_workflow(self, task: str, **kwargs) -> None: + print( + colored( + """ + Adding Objective to Workflow...""", + "green", + attrs=["bold", "underline"], + ) + ) + + task = Task(description=task, flow=kwargs["flow"], args=list(kwargs["args"]), kwargs=kwargs["kwargs"]) + self.tasks.append(task) + + + def load_workflow_state(self, filepath: str = None, **kwargs) -> None: """ From 0c4dd88f988ac5b4e0d2ef644d9138991052f0d4 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 14:17:12 -0500 Subject: [PATCH 55/63] anthropic + kosmo2 + fastvit Former-commit-id: 6b4c2d45d3534f69b1ed9445ecfca9f18699e588 --- demos/accountant_team/accountant_team.py | 16 +- playground/models/anthropic_example.py | 6 +- swarms/models/anthropic.py | 2 +- swarms/models/fast_vit_classes.json | 1000 ++++++++++++++++++++++ swarms/models/fastvit.py | 80 ++ swarms/models/kosmos2.py | 100 +++ swarms/structs/sequential_workflow.py | 12 +- 7 files changed, 1202 insertions(+), 14 deletions(-) create mode 100644 swarms/models/fast_vit_classes.json create mode 100644 swarms/models/fastvit.py create mode 100644 swarms/models/kosmos2.py diff --git a/demos/accountant_team/accountant_team.py b/demos/accountant_team/accountant_team.py index 459da830..18bda007 100644 --- a/demos/accountant_team/accountant_team.py +++ b/demos/accountant_team/accountant_team.py @@ -1,7 +1,6 @@ # !pip install --upgrade swarms==2.0.6 - from swarms.models import OpenAIChat from swarms.models.nougat import Nougat from swarms.structs import Flow @@ -11,7 +10,10 @@ from swarms.structs.sequential_workflow import SequentialWorkflow IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg" # Example usage -api_key = "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" # Your actual API key here +api_key = ( + "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" # Your actual API key here +) + # Initialize the OCR model def ocr_model(img: str): @@ -19,12 +21,14 @@ def ocr_model(img: str): analyze_finance_docs = ocr(img) return str(analyze_finance_docs) + # Initialize the language flow llm = OpenAIChat( openai_api_key=api_key, temperature=0.5, ) + # Create a prompt for the language model def summary_agent_prompt(analyzed_doc: str): analyzed_doc = ocr_model(img=analyzed_doc) @@ -36,6 +40,7 @@ def summary_agent_prompt(analyzed_doc: str): {analyzed_doc} """ + # Initialize the Flow with the language flow flow1 = Flow(llm=llm, max_loops=1, dashboard=False) @@ -49,11 +54,14 @@ workflow = SequentialWorkflow(max_loops=1) workflow.add(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL), flow1) # Suppose the next task takes the output of the first task as input -workflow.add("Provide an actionable step by step plan on how to cut costs from the analyzed financial document.", flow2) +workflow.add( + "Provide an actionable step by step plan on how to cut costs from the analyzed financial document.", + flow2, +) # Run the workflow workflow.run() # Output the results for task in workflow.tasks: - print(f"Task: {task.description}, Result: {task.result}") \ No newline at end of file + print(f"Task: {task.description}, Result: {task.result}") diff --git a/playground/models/anthropic_example.py b/playground/models/anthropic_example.py index 695dfe62..3354d0cc 100644 --- a/playground/models/anthropic_example.py +++ b/playground/models/anthropic_example.py @@ -1,11 +1,9 @@ from swarms.models.anthropic import Anthropic -model = Anthropic( - anthropic_api_key="" -) +model = Anthropic(anthropic_api_key="") task = "Say hello to" -print(model(task)) \ No newline at end of file +print(model(task)) diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 24c3c126..c8ba2329 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -523,4 +523,4 @@ class Anthropic(LLM, _AnthropicCommon): """Calculate number of tokens.""" if not self.count_tokens: raise NameError("Please ensure the anthropic package is loaded") - return self.count_tokens(text) \ No newline at end of file + return self.count_tokens(text) diff --git a/swarms/models/fast_vit_classes.json b/swarms/models/fast_vit_classes.json new file mode 100644 index 00000000..57434253 --- /dev/null +++ b/swarms/models/fast_vit_classes.json @@ -0,0 +1,1000 @@ +["tench", +"goldfish", +"great white shark", +"tiger shark", +"hammerhead shark", +"electric ray", +"stingray", +"cock", +"hen", +"ostrich", +"brambling", +"goldfinch", +"house finch", +"junco", +"indigo bunting", +"American robin", +"bulbul", +"jay", +"magpie", +"chickadee", +"American dipper", +"kite", +"bald eagle", +"vulture", +"great grey owl", +"fire salamander", +"smooth newt", +"newt", +"spotted salamander", +"axolotl", +"American bullfrog", +"tree frog", +"tailed frog", +"loggerhead sea turtle", +"leatherback sea turtle", +"mud turtle", +"terrapin", +"box turtle", +"banded gecko", +"green iguana", +"Carolina anole", +"desert grassland whiptail lizard", +"agama", +"frilled-necked lizard", +"alligator lizard", +"Gila monster", +"European green lizard", +"chameleon", +"Komodo dragon", +"Nile crocodile", +"American alligator", +"triceratops", +"worm snake", +"ring-necked snake", +"eastern hog-nosed snake", +"smooth green snake", +"kingsnake", +"garter snake", +"water snake", +"vine snake", +"night snake", +"boa constrictor", +"African rock python", +"Indian cobra", +"green mamba", +"sea snake", +"Saharan horned viper", +"eastern diamondback rattlesnake", +"sidewinder", +"trilobite", +"harvestman", +"scorpion", +"yellow garden spider", +"barn spider", +"European garden spider", +"southern black widow", +"tarantula", +"wolf spider", +"tick", +"centipede", +"black grouse", +"ptarmigan", +"ruffed grouse", +"prairie grouse", +"peacock", +"quail", +"partridge", +"grey parrot", +"macaw", +"sulphur-crested cockatoo", +"lorikeet", +"coucal", +"bee eater", +"hornbill", +"hummingbird", +"jacamar", +"toucan", +"duck", +"red-breasted merganser", +"goose", +"black swan", +"tusker", +"echidna", +"platypus", +"wallaby", +"koala", +"wombat", +"jellyfish", +"sea anemone", +"brain coral", +"flatworm", +"nematode", +"conch", +"snail", +"slug", +"sea slug", +"chiton", +"chambered nautilus", +"Dungeness crab", +"rock crab", +"fiddler crab", +"red king crab", +"American lobster", +"spiny lobster", +"crayfish", +"hermit crab", +"isopod", +"white stork", +"black stork", +"spoonbill", +"flamingo", +"little blue heron", +"great egret", +"bittern", +"crane (bird)", +"limpkin", +"common gallinule", +"American coot", +"bustard", +"ruddy turnstone", +"dunlin", +"common redshank", +"dowitcher", +"oystercatcher", +"pelican", +"king penguin", +"albatross", +"grey whale", +"killer whale", +"dugong", +"sea lion", +"Chihuahua", +"Japanese Chin", +"Maltese", +"Pekingese", +"Shih Tzu", +"King Charles Spaniel", +"Papillon", +"toy terrier", +"Rhodesian Ridgeback", +"Afghan Hound", +"Basset Hound", +"Beagle", +"Bloodhound", +"Bluetick Coonhound", +"Black and Tan Coonhound", +"Treeing Walker Coonhound", +"English foxhound", +"Redbone Coonhound", +"borzoi", +"Irish Wolfhound", +"Italian Greyhound", +"Whippet", +"Ibizan Hound", +"Norwegian Elkhound", +"Otterhound", +"Saluki", +"Scottish Deerhound", +"Weimaraner", +"Staffordshire Bull Terrier", +"American Staffordshire Terrier", +"Bedlington Terrier", +"Border Terrier", +"Kerry Blue Terrier", +"Irish Terrier", +"Norfolk Terrier", +"Norwich Terrier", +"Yorkshire Terrier", +"Wire Fox Terrier", +"Lakeland Terrier", +"Sealyham Terrier", +"Airedale Terrier", +"Cairn Terrier", +"Australian Terrier", +"Dandie Dinmont Terrier", +"Boston Terrier", +"Miniature Schnauzer", +"Giant Schnauzer", +"Standard Schnauzer", +"Scottish Terrier", +"Tibetan Terrier", +"Australian Silky Terrier", +"Soft-coated Wheaten Terrier", +"West Highland White Terrier", +"Lhasa Apso", +"Flat-Coated Retriever", +"Curly-coated Retriever", +"Golden Retriever", +"Labrador Retriever", +"Chesapeake Bay Retriever", +"German Shorthaired Pointer", +"Vizsla", +"English Setter", +"Irish Setter", +"Gordon Setter", +"Brittany Spaniel", +"Clumber Spaniel", +"English Springer Spaniel", +"Welsh Springer Spaniel", +"Cocker Spaniels", +"Sussex Spaniel", +"Irish Water Spaniel", +"Kuvasz", +"Schipperke", +"Groenendael", +"Malinois", +"Briard", +"Australian Kelpie", +"Komondor", +"Old English Sheepdog", +"Shetland Sheepdog", +"collie", +"Border Collie", +"Bouvier des Flandres", +"Rottweiler", +"German Shepherd Dog", +"Dobermann", +"Miniature Pinscher", +"Greater Swiss Mountain Dog", +"Bernese Mountain Dog", +"Appenzeller Sennenhund", +"Entlebucher Sennenhund", +"Boxer", +"Bullmastiff", +"Tibetan Mastiff", +"French Bulldog", +"Great Dane", +"St. Bernard", +"husky", +"Alaskan Malamute", +"Siberian Husky", +"Dalmatian", +"Affenpinscher", +"Basenji", +"pug", +"Leonberger", +"Newfoundland", +"Pyrenean Mountain Dog", +"Samoyed", +"Pomeranian", +"Chow Chow", +"Keeshond", +"Griffon Bruxellois", +"Pembroke Welsh Corgi", +"Cardigan Welsh Corgi", +"Toy Poodle", +"Miniature Poodle", +"Standard Poodle", +"Mexican hairless dog", +"grey wolf", +"Alaskan tundra wolf", +"red wolf", +"coyote", +"dingo", +"dhole", +"African wild dog", +"hyena", +"red fox", +"kit fox", +"Arctic fox", +"grey fox", +"tabby cat", +"tiger cat", +"Persian cat", +"Siamese cat", +"Egyptian Mau", +"cougar", +"lynx", +"leopard", +"snow leopard", +"jaguar", +"lion", +"tiger", +"cheetah", +"brown bear", +"American black bear", +"polar bear", +"sloth bear", +"mongoose", +"meerkat", +"tiger beetle", +"ladybug", +"ground beetle", +"longhorn beetle", +"leaf beetle", +"dung beetle", +"rhinoceros beetle", +"weevil", +"fly", +"bee", +"ant", +"grasshopper", +"cricket", +"stick insect", +"cockroach", +"mantis", +"cicada", +"leafhopper", +"lacewing", +"dragonfly", +"damselfly", +"red admiral", +"ringlet", +"monarch butterfly", +"small white", +"sulphur butterfly", +"gossamer-winged butterfly", +"starfish", +"sea urchin", +"sea cucumber", +"cottontail rabbit", +"hare", +"Angora rabbit", +"hamster", +"porcupine", +"fox squirrel", +"marmot", +"beaver", +"guinea pig", +"common sorrel", +"zebra", +"pig", +"wild boar", +"warthog", +"hippopotamus", +"ox", +"water buffalo", +"bison", +"ram", +"bighorn sheep", +"Alpine ibex", +"hartebeest", +"impala", +"gazelle", +"dromedary", +"llama", +"weasel", +"mink", +"European polecat", +"black-footed ferret", +"otter", +"skunk", +"badger", +"armadillo", +"three-toed sloth", +"orangutan", +"gorilla", +"chimpanzee", +"gibbon", +"siamang", +"guenon", +"patas monkey", +"baboon", +"macaque", +"langur", +"black-and-white colobus", +"proboscis monkey", +"marmoset", +"white-headed capuchin", +"howler monkey", +"titi", +"Geoffroy's spider monkey", +"common squirrel monkey", +"ring-tailed lemur", +"indri", +"Asian elephant", +"African bush elephant", +"red panda", +"giant panda", +"snoek", +"eel", +"coho salmon", +"rock beauty", +"clownfish", +"sturgeon", +"garfish", +"lionfish", +"pufferfish", +"abacus", +"abaya", +"academic gown", +"accordion", +"acoustic guitar", +"aircraft carrier", +"airliner", +"airship", +"altar", +"ambulance", +"amphibious vehicle", +"analog clock", +"apiary", +"apron", +"waste container", +"assault rifle", +"backpack", +"bakery", +"balance beam", +"balloon", +"ballpoint pen", +"Band-Aid", +"banjo", +"baluster", +"barbell", +"barber chair", +"barbershop", +"barn", +"barometer", +"barrel", +"wheelbarrow", +"baseball", +"basketball", +"bassinet", +"bassoon", +"swimming cap", +"bath towel", +"bathtub", +"station wagon", +"lighthouse", +"beaker", +"military cap", +"beer bottle", +"beer glass", +"bell-cot", +"bib", +"tandem bicycle", +"bikini", +"ring binder", +"binoculars", +"birdhouse", +"boathouse", +"bobsleigh", +"bolo tie", +"poke bonnet", +"bookcase", +"bookstore", +"bottle cap", +"bow", +"bow tie", +"brass", +"bra", +"breakwater", +"breastplate", +"broom", +"bucket", +"buckle", +"bulletproof vest", +"high-speed train", +"butcher shop", +"taxicab", +"cauldron", +"candle", +"cannon", +"canoe", +"can opener", +"cardigan", +"car mirror", +"carousel", +"tool kit", +"carton", +"car wheel", +"automated teller machine", +"cassette", +"cassette player", +"castle", +"catamaran", +"CD player", +"cello", +"mobile phone", +"chain", +"chain-link fence", +"chain mail", +"chainsaw", +"chest", +"chiffonier", +"chime", +"china cabinet", +"Christmas stocking", +"church", +"movie theater", +"cleaver", +"cliff dwelling", +"cloak", +"clogs", +"cocktail shaker", +"coffee mug", +"coffeemaker", +"coil", +"combination lock", +"computer keyboard", +"confectionery store", +"container ship", +"convertible", +"corkscrew", +"cornet", +"cowboy boot", +"cowboy hat", +"cradle", +"crane (machine)", +"crash helmet", +"crate", +"infant bed", +"Crock Pot", +"croquet ball", +"crutch", +"cuirass", +"dam", +"desk", +"desktop computer", +"rotary dial telephone", +"diaper", +"digital clock", +"digital watch", +"dining table", +"dishcloth", +"dishwasher", +"disc brake", +"dock", +"dog sled", +"dome", +"doormat", +"drilling rig", +"drum", +"drumstick", +"dumbbell", +"Dutch oven", +"electric fan", +"electric guitar", +"electric locomotive", +"entertainment center", +"envelope", +"espresso machine", +"face powder", +"feather boa", +"filing cabinet", +"fireboat", +"fire engine", +"fire screen sheet", +"flagpole", +"flute", +"folding chair", +"football helmet", +"forklift", +"fountain", +"fountain pen", +"four-poster bed", +"freight car", +"French horn", +"frying pan", +"fur coat", +"garbage truck", +"gas mask", +"gas pump", +"goblet", +"go-kart", +"golf ball", +"golf cart", +"gondola", +"gong", +"gown", +"grand piano", +"greenhouse", +"grille", +"grocery store", +"guillotine", +"barrette", +"hair spray", +"half-track", +"hammer", +"hamper", +"hair dryer", +"hand-held computer", +"handkerchief", +"hard disk drive", +"harmonica", +"harp", +"harvester", +"hatchet", +"holster", +"home theater", +"honeycomb", +"hook", +"hoop skirt", +"horizontal bar", +"horse-drawn vehicle", +"hourglass", +"iPod", +"clothes iron", +"jack-o'-lantern", +"jeans", +"jeep", +"T-shirt", +"jigsaw puzzle", +"pulled rickshaw", +"joystick", +"kimono", +"knee pad", +"knot", +"lab coat", +"ladle", +"lampshade", +"laptop computer", +"lawn mower", +"lens cap", +"paper knife", +"library", +"lifeboat", +"lighter", +"limousine", +"ocean liner", +"lipstick", +"slip-on shoe", +"lotion", +"speaker", +"loupe", +"sawmill", +"magnetic compass", +"mail bag", +"mailbox", +"tights", +"tank suit", +"manhole cover", +"maraca", +"marimba", +"mask", +"match", +"maypole", +"maze", +"measuring cup", +"medicine chest", +"megalith", +"microphone", +"microwave oven", +"military uniform", +"milk can", +"minibus", +"miniskirt", +"minivan", +"missile", +"mitten", +"mixing bowl", +"mobile home", +"Model T", +"modem", +"monastery", +"monitor", +"moped", +"mortar", +"square academic cap", +"mosque", +"mosquito net", +"scooter", +"mountain bike", +"tent", +"computer mouse", +"mousetrap", +"moving van", +"muzzle", +"nail", +"neck brace", +"necklace", +"nipple", +"notebook computer", +"obelisk", +"oboe", +"ocarina", +"odometer", +"oil filter", +"organ", +"oscilloscope", +"overskirt", +"bullock cart", +"oxygen mask", +"packet", +"paddle", +"paddle wheel", +"padlock", +"paintbrush", +"pajamas", +"palace", +"pan flute", +"paper towel", +"parachute", +"parallel bars", +"park bench", +"parking meter", +"passenger car", +"patio", +"payphone", +"pedestal", +"pencil case", +"pencil sharpener", +"perfume", +"Petri dish", +"photocopier", +"plectrum", +"Pickelhaube", +"picket fence", +"pickup truck", +"pier", +"piggy bank", +"pill bottle", +"pillow", +"ping-pong ball", +"pinwheel", +"pirate ship", +"pitcher", +"hand plane", +"planetarium", +"plastic bag", +"plate rack", +"plow", +"plunger", +"Polaroid camera", +"pole", +"police van", +"poncho", +"billiard table", +"soda bottle", +"pot", +"potter's wheel", +"power drill", +"prayer rug", +"printer", +"prison", +"projectile", +"projector", +"hockey puck", +"punching bag", +"purse", +"quill", +"quilt", +"race car", +"racket", +"radiator", +"radio", +"radio telescope", +"rain barrel", +"recreational vehicle", +"reel", +"reflex camera", +"refrigerator", +"remote control", +"restaurant", +"revolver", +"rifle", +"rocking chair", +"rotisserie", +"eraser", +"rugby ball", +"ruler", +"running shoe", +"safe", +"safety pin", +"salt shaker", +"sandal", +"sarong", +"saxophone", +"scabbard", +"weighing scale", +"school bus", +"schooner", +"scoreboard", +"CRT screen", +"screw", +"screwdriver", +"seat belt", +"sewing machine", +"shield", +"shoe store", +"shoji", +"shopping basket", +"shopping cart", +"shovel", +"shower cap", +"shower curtain", +"ski", +"ski mask", +"sleeping bag", +"slide rule", +"sliding door", +"slot machine", +"snorkel", +"snowmobile", +"snowplow", +"soap dispenser", +"soccer ball", +"sock", +"solar thermal collector", +"sombrero", +"soup bowl", +"space bar", +"space heater", +"space shuttle", +"spatula", +"motorboat", +"spider web", +"spindle", +"sports car", +"spotlight", +"stage", +"steam locomotive", +"through arch bridge", +"steel drum", +"stethoscope", +"scarf", +"stone wall", +"stopwatch", +"stove", +"strainer", +"tram", +"stretcher", +"couch", +"stupa", +"submarine", +"suit", +"sundial", +"sunglass", +"sunglasses", +"sunscreen", +"suspension bridge", +"mop", +"sweatshirt", +"swimsuit", +"swing", +"switch", +"syringe", +"table lamp", +"tank", +"tape player", +"teapot", +"teddy bear", +"television", +"tennis ball", +"thatched roof", +"front curtain", +"thimble", +"threshing machine", +"throne", +"tile roof", +"toaster", +"tobacco shop", +"toilet seat", +"torch", +"totem pole", +"tow truck", +"toy store", +"tractor", +"semi-trailer truck", +"tray", +"trench coat", +"tricycle", +"trimaran", +"tripod", +"triumphal arch", +"trolleybus", +"trombone", +"tub", +"turnstile", +"typewriter keyboard", +"umbrella", +"unicycle", +"upright piano", +"vacuum cleaner", +"vase", +"vault", +"velvet", +"vending machine", +"vestment", +"viaduct", +"violin", +"volleyball", +"waffle iron", +"wall clock", +"wallet", +"wardrobe", +"military aircraft", +"sink", +"washing machine", +"water bottle", +"water jug", +"water tower", +"whiskey jug", +"whistle", +"wig", +"window screen", +"window shade", +"Windsor tie", +"wine bottle", +"wing", +"wok", +"wooden spoon", +"wool", +"split-rail fence", +"shipwreck", +"yawl", +"yurt", +"website", +"comic book", +"crossword", +"traffic sign", +"traffic light", +"dust jacket", +"menu", +"plate", +"guacamole", +"consomme", +"hot pot", +"trifle", +"ice cream", +"ice pop", +"baguette", +"bagel", +"pretzel", +"cheeseburger", +"hot dog", +"mashed potato", +"cabbage", +"broccoli", +"cauliflower", +"zucchini", +"spaghetti squash", +"acorn squash", +"butternut squash", +"cucumber", +"artichoke", +"bell pepper", +"cardoon", +"mushroom", +"Granny Smith", +"strawberry", +"orange", +"lemon", +"fig", +"pineapple", +"banana", +"jackfruit", +"custard apple", +"pomegranate", +"hay", +"carbonara", +"chocolate syrup", +"dough", +"meatloaf", +"pizza", +"pot pie", +"burrito", +"red wine", +"espresso", +"cup", +"eggnog", +"alp", +"bubble", +"cliff", +"coral reef", +"geyser", +"lakeshore", +"promontory", +"shoal", +"seashore", +"valley", +"volcano", +"baseball player", +"bridegroom", +"scuba diver", +"rapeseed", +"daisy", +"yellow lady's slipper", +"corn", +"acorn", +"rose hip", +"horse chestnut seed", +"coral fungus", +"agaric", +"gyromitra", +"stinkhorn mushroom", +"earth star", +"hen-of-the-woods", +"bolete", +"ear of corn", +"toilet paper"] \ No newline at end of file diff --git a/swarms/models/fastvit.py b/swarms/models/fastvit.py new file mode 100644 index 00000000..a9a6abf7 --- /dev/null +++ b/swarms/models/fastvit.py @@ -0,0 +1,80 @@ +import json +import os +from typing import List + +import numpy as np +import timm +import torch +from PIL import Image +from pydantic import BaseModel, StrictFloat, StrictInt, validator + +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Load the classes for image classification +with open(os.path.join(os.path.dirname(__file__), "fast_vit_classes.json")) as f: + FASTVIT_IMAGENET_1K_CLASSES = json.load(f) + + +class ClassificationResult(BaseModel): + class_id: List[StrictInt] + confidence: List[StrictFloat] + + @validator("class_id", "confidence", pre=True, each_item=True) + def check_list_contents(cls, v): + assert isinstance(v, int) or isinstance(v, float), "must be integer or float" + return v + + +class FastViT: + """ + FastViT model for image classification + + Args: + img (str): path to the input image + confidence_threshold (float): confidence threshold for the model's predictions + + Returns: + ClassificationResult: a pydantic BaseModel containing the class ids and confidences of the model's predictions + + + Example: + >>> fastvit = FastViT() + >>> result = fastvit(img="path_to_image.jpg", confidence_threshold=0.5) + + + To use, create a json file called: fast_vit_classes.json + + """ + def __init__(self): + self.model = timm.create_model( + "hf_hub:timm/fastvit_s12.apple_in1k", pretrained=True + ).to(DEVICE) + data_config = timm.data.resolve_model_data_config(self.model) + self.transforms = timm.data.create_transform(**data_config, is_training=False) + self.model.eval() + + def __call__( + self, img: str, confidence_threshold: float = 0.5 + ) -> ClassificationResult: + """classifies the input image and returns the top k classes and their probabilities""" + img = Image.open(img).convert("RGB") + img_tensor = self.transforms(img).unsqueeze(0).to(DEVICE) + with torch.no_grad(): + output = self.model(img_tensor) + probabilities = torch.nn.functional.softmax(output, dim=1) + + # Get top k classes and their probabilities + top_probs, top_classes = torch.topk( + probabilities, k=FASTVIT_IMAGENET_1K_CLASSES + ) + + # Filter by confidence threshold + mask = top_probs > confidence_threshold + top_probs, top_classes = top_probs[mask], top_classes[mask] + + # Convert to Python lists and map class indices to labels if needed + top_probs = top_probs.cpu().numpy().tolist() + top_classes = top_classes.cpu().numpy().tolist() + # top_class_labels = [FASTVIT_IMAGENET_1K_CLASSES[i] for i in top_classes] # Uncomment if class labels are needed + + return ClassificationResult(class_id=top_classes, confidence=top_probs) diff --git a/swarms/models/kosmos2.py b/swarms/models/kosmos2.py new file mode 100644 index 00000000..12d5638a --- /dev/null +++ b/swarms/models/kosmos2.py @@ -0,0 +1,100 @@ +from typing import List, Tuple + +import numpy as np +from PIL import Image +from pydantic import BaseModel, root_validator, validator +from transformers import AutoModelForVision2Seq, AutoProcessor + + +# Assuming the Detections class represents the output of the model prediction +class Detections(BaseModel): + xyxy: List[Tuple[float, float, float, float]] + class_id: List[int] + confidence: List[float] + + @root_validator + def check_length(cls, values): + assert ( + len(values.get("xyxy")) + == len(values.get("class_id")) + == len(values.get("confidence")) + ), "All fields must have the same length." + return values + + @validator("xyxy", "class_id", "confidence", pre=True, each_item=True) + def check_not_empty(cls, v): + if isinstance(v, list) and len(v) == 0: + raise ValueError("List must not be empty") + return v + + @classmethod + def empty(cls): + return cls(xyxy=[], class_id=[], confidence=[]) + + +class Kosmos2(BaseModel): + model: AutoModelForVision2Seq + processor: AutoProcessor + + @classmethod + def initialize(cls): + model = AutoModelForVision2Seq.from_pretrained( + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True + ) + processor = AutoProcessor.from_pretrained( + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True + ) + return cls(model=model, processor=processor) + + def __call__(self, img: str) -> Detections: + image = Image.open(img) + prompt = "An image of" + + inputs = self.processor(text=prompt, images=image, return_tensors="pt") + outputs = self.model.generate(**inputs, use_cache=True, max_new_tokens=64) + + generated_text = self.processor.batch_decode(outputs, skip_special_tokens=True)[ + 0 + ] + + # The actual processing of generated_text to entities would go here + # For the purpose of this example, assume a mock function 'extract_entities' exists: + entities = self.extract_entities(generated_text) + + # Convert entities to detections format + detections = self.process_entities_to_detections(entities, image) + return detections + + def extract_entities( + self, text: str + ) -> List[Tuple[str, Tuple[float, float, float, float]]]: + # Placeholder function for entity extraction + # This should be replaced with the actual method of extracting entities + return [] + + def process_entities_to_detections( + self, + entities: List[Tuple[str, Tuple[float, float, float, float]]], + image: Image.Image, + ) -> Detections: + if not entities: + return Detections.empty() + + class_ids = [0] * len(entities) # Replace with actual class ID extraction logic + xyxys = [ + ( + e[1][0] * image.width, + e[1][1] * image.height, + e[1][2] * image.width, + e[1][3] * image.height, + ) + for e in entities + ] + confidences = [1.0] * len(entities) # Placeholder confidence + + return Detections(xyxy=xyxys, class_id=class_ids, confidence=confidences) + + +# Usage: +# kosmos2 = Kosmos2.initialize() +# detections = kosmos2(img="path_to_image.jpg") diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index 251447ba..a7e1cd63 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -265,7 +265,7 @@ class SequentialWorkflow: attrs=["bold", "underline"], ) ) - + def workflow_shutdown(self, **kwargs) -> None: print( colored( @@ -286,11 +286,13 @@ class SequentialWorkflow: ) ) - task = Task(description=task, flow=kwargs["flow"], args=list(kwargs["args"]), kwargs=kwargs["kwargs"]) + task = Task( + description=task, + flow=kwargs["flow"], + args=list(kwargs["args"]), + kwargs=kwargs["kwargs"], + ) self.tasks.append(task) - - - def load_workflow_state(self, filepath: str = None, **kwargs) -> None: """ From d4b12e0beacc1525820ec3eca51865642d4cd117 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 14:52:53 -0500 Subject: [PATCH 56/63] fuyu + zephyr fixes Former-commit-id: 9aa40842a63ca72b8229c9481b2dc6f24fec4abc --- demos/accountant_team/accountant_team.py | 43 +++++---------- playground/models/anthropic_example.py | 2 +- pyproject.toml | 2 +- swarms/models/fastvit.py | 3 +- swarms/models/fuyu.py | 7 +-- swarms/models/timm.py | 67 ++++++++++++++++++++++++ swarms/models/zephyr.py | 53 +++++++++++++++++-- 7 files changed, 136 insertions(+), 41 deletions(-) create mode 100644 swarms/models/timm.py diff --git a/demos/accountant_team/accountant_team.py b/demos/accountant_team/accountant_team.py index 18bda007..06f89684 100644 --- a/demos/accountant_team/accountant_team.py +++ b/demos/accountant_team/accountant_team.py @@ -1,43 +1,36 @@ # !pip install --upgrade swarms==2.0.6 - -from swarms.models import OpenAIChat +from swarms.models import BioGPT from swarms.models.nougat import Nougat from swarms.structs import Flow -from swarms.structs.sequential_workflow import SequentialWorkflow # # URL of the image of the financial document IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg" # Example usage -api_key = ( - "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" # Your actual API key here -) - +api_key = "" # Your actual API key here # Initialize the OCR model -def ocr_model(img: str): - ocr = Nougat() - analyze_finance_docs = ocr(img) - return str(analyze_finance_docs) # Initialize the language flow -llm = OpenAIChat( - openai_api_key=api_key, - temperature=0.5, -) +llm = BioGPT() # Create a prompt for the language model def summary_agent_prompt(analyzed_doc: str): - analyzed_doc = ocr_model(img=analyzed_doc) + model = Nougat( + max_new_tokens=5000, + ) + + out = model(analyzed_doc) + return f""" Generate an actionable summary of this financial document, provide bulletpoints: Here is the Analyzed Document: --- - {analyzed_doc} + {out} """ @@ -47,21 +40,11 @@ flow1 = Flow(llm=llm, max_loops=1, dashboard=False) # Create another Flow for a different task flow2 = Flow(llm=llm, max_loops=1, dashboard=False) -# Create the workflow -workflow = SequentialWorkflow(max_loops=1) # Add tasks to the workflow -workflow.add(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL), flow1) +summary_agent = flow1.run(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL)) # Suppose the next task takes the output of the first task as input -workflow.add( - "Provide an actionable step by step plan on how to cut costs from the analyzed financial document.", - flow2, +out = flow2.run( + f"Provide an actionable step by step plan on how to cut costs from the analyzed financial document. {summary_agent}" ) - -# Run the workflow -workflow.run() - -# Output the results -for task in workflow.tasks: - print(f"Task: {task.description}, Result: {task.result}") diff --git a/playground/models/anthropic_example.py b/playground/models/anthropic_example.py index 3354d0cc..940892ca 100644 --- a/playground/models/anthropic_example.py +++ b/playground/models/anthropic_example.py @@ -4,6 +4,6 @@ from swarms.models.anthropic import Anthropic model = Anthropic(anthropic_api_key="") -task = "Say hello to" +task = "What is quantum field theory? What are 3 books on the field?" print(model(task)) diff --git a/pyproject.toml b/pyproject.toml index 3709ebc0..75f0e7ca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.0.8" +version = "2.1.0" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/models/fastvit.py b/swarms/models/fastvit.py index a9a6abf7..a2d6bc0a 100644 --- a/swarms/models/fastvit.py +++ b/swarms/models/fastvit.py @@ -43,8 +43,9 @@ class FastViT: To use, create a json file called: fast_vit_classes.json - + """ + def __init__(self): self.model = timm.create_model( "hf_hub:timm/fastvit_s12.apple_in1k", pretrained=True diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index 0fd1fd85..edecc19c 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -1,8 +1,8 @@ """Fuyu model by Kye""" from transformers import ( + FuyuProcessor, FuyuForCausalLM, AutoTokenizer, - FuyuProcessor, FuyuImageProcessor, ) from PIL import Image @@ -50,9 +50,9 @@ class Fuyu: pretrained_path, device_map=device_map ) - def __call__(self, text: str, img_path: str): + def __call__(self, text: str, img: str): """Call the model with text and img paths""" - image_pil = Image.open(img_path) + image_pil = Image.open(img) model_inputs = self.processor( text=text, images=[image_pil], device=self.device_map ) @@ -62,3 +62,4 @@ class Fuyu: output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens) text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) + return print(str(text)) diff --git a/swarms/models/timm.py b/swarms/models/timm.py new file mode 100644 index 00000000..5d9b965a --- /dev/null +++ b/swarms/models/timm.py @@ -0,0 +1,67 @@ +from typing import List + +import timm +import torch +from pydantic import BaseModel, conlist + + +class TimmModelInfo(BaseModel): + model_name: str + pretrained: bool + in_chans: int + + class Config: + # Use strict typing for all fields + strict = True + + +class TimmModel: + """ + + # Usage + model_handler = TimmModelHandler() + model_info = TimmModelInfo(model_name='resnet34', pretrained=True, in_chans=1) + input_tensor = torch.randn(1, 1, 224, 224) + output_shape = model_handler(model_info=model_info, input_tensor=input_tensor) + print(output_shape) + + """ + + def __init__(self): + self.models = self._get_supported_models() + + def _get_supported_models(self) -> List[str]: + """Retrieve the list of supported models from timm.""" + return timm.list_models() + + def _create_model(self, model_info: TimmModelInfo) -> torch.nn.Module: + """ + Create a model instance from timm with specified parameters. + + Args: + model_info: An instance of TimmModelInfo containing model specifications. + + Returns: + An instance of a pytorch model. + """ + return timm.create_model( + model_info.model_name, + pretrained=model_info.pretrained, + in_chans=model_info.in_chans, + ) + + def __call__( + self, model_info: TimmModelInfo, input_tensor: torch.Tensor + ) -> torch.Size: + """ + Create and run a model specified by `model_info` on `input_tensor`. + + Args: + model_info: An instance of TimmModelInfo containing model specifications. + input_tensor: A torch tensor representing the input data. + + Returns: + The shape of the output from the model. + """ + model = self._create_model(model_info) + return model(input_tensor).shape diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index f4052d82..30d2bcd6 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -25,12 +25,22 @@ class Zephyr: def __init__( self, + model_name: str = "HuggingFaceH4/zephyr-7b-alpha", + tokenize: bool = False, + add_generation_prompt: bool = True, + system_prompt: str = "You are a friendly chatbot who always responds in the style of a pirate", max_new_tokens: int = 300, temperature: float = 0.5, top_k: float = 50, top_p: float = 0.95, + *args, + **kwargs, ): super().__init__() + self.model_name = model_name + self.tokenize = tokenize + self.add_generation_prompt = add_generation_prompt + self.system_prompt = system_prompt self.max_new_tokens = max_new_tokens self.temperature = temperature self.top_k = top_k @@ -38,14 +48,14 @@ class Zephyr: self.pipe = pipeline( "text-generation", - model="HuggingFaceH4/zephyr-7b-alpha", + model=self.model_name, torch_dtype=torch.bfloat16, device_map="auto", ) self.messages = [ { "role": "system", - "content": "You are a friendly chatbot who always responds in the style of a pirate", + "content": f"{self.systen_prompt}\n\nUser:", }, { "role": "user", @@ -53,10 +63,43 @@ class Zephyr: }, ] - def __call__(self, text: str): + def __call__(self, task: str): """Call the model""" prompt = self.pipe.tokenizer.apply_chat_template( - self.messages, tokenize=False, add_generation_prompt=True + self.messages, + tokenize=self.tokenize, + add_generation_prompt=self.add_generation_prompt, ) - outputs = self.pipe(prompt, max_new_token=self.max_new_tokens) + outputs = self.pipe(prompt) # max_new_token=self.max_new_tokens) print(outputs[0])["generated_text"] + + def chat(self, message: str): + """ + Adds a user message to the conversation and generates a chatbot response. + """ + # Add the user message to the conversation + self.messages.append({"role": "user", "content": message}) + + # Apply the chat template to format the messages + prompt = self.pipe.tokenizer.apply_chat_template( + self.messages, tokenize=False, add_generation_prompt=True + ) + + # Generate a response + outputs = self.pipe( + prompt, + max_new_tokens=self.max_new_tokens, + do_sample=True, + temperature=self.temperature, + top_k=self.top_k, + top_p=self.top_p, + ) + + # Extract the generated text + generated_text = outputs[0]["generated_text"] + + # Optionally, you could also add the chatbot's response to the messages list + # However, the below line should be adjusted to extract the chatbot's response only + # self.messages.append({"role": "bot", "content": generated_text}) + + return generated_text From f4a5918131473c51973dbdd5b96957195be63b8d Mon Sep 17 00:00:00 2001 From: Vyomakesh Dundigalla <54256947+vyomakesh09@users.noreply.github.com> Date: Wed, 8 Nov 2023 21:05:04 +0000 Subject: [PATCH 57/63] Update zephyr.py Typo: 58. "content": f"{self.system_prompt}\n\nUser:", Parenthesis: 74. print(outputs[0]["generated_text"]) Former-commit-id: 73b549b569e76e954ca8ed78423e9d62d434247b --- swarms/models/zephyr.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index 30d2bcd6..5a6467b7 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -55,7 +55,7 @@ class Zephyr: self.messages = [ { "role": "system", - "content": f"{self.systen_prompt}\n\nUser:", + "content": f"{self.system_prompt}\n\nUser:", }, { "role": "user", @@ -71,8 +71,8 @@ class Zephyr: add_generation_prompt=self.add_generation_prompt, ) outputs = self.pipe(prompt) # max_new_token=self.max_new_tokens) - print(outputs[0])["generated_text"] - + print(outputs[0]["generated_text"]) + def chat(self, message: str): """ Adds a user message to the conversation and generates a chatbot response. From 532aa8c9e5f2b9ace1c5ce6395087fd2bd4061eb Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 17:34:04 -0500 Subject: [PATCH 58/63] code quality + new verison + fuyu fixes Former-commit-id: c279784458912a7f2587f9ceba44436c24ee4fe0 --- playground/models/dalle3.jpeg | Bin 0 -> 228428 bytes playground/models/fuyu.py | 4 - playground/models/fuyu_example.py | 7 + pyproject.toml | 3 +- quality.sh | 18 + requirements.txt | 1 + swarms/__init__.py | 14 +- swarms/agents/agent.py | 19 +- swarms/agents/aot.py | 3 +- swarms/agents/browser_agent.py | 25 +- swarms/agents/hf_agents.py | 22 +- swarms/agents/multi_modal_visual_agent.py | 397 +++++++++++++--------- swarms/agents/profitpilot.py | 96 +++++- swarms/agents/registry.py | 3 +- swarms/memory/chroma.py | 2 +- swarms/memory/schemas.py | 5 +- swarms/models/__init__.py | 3 +- swarms/models/anthropic.py | 2 +- swarms/models/bioclip.py | 2 +- swarms/models/biogpt.py | 26 +- swarms/models/dalle3.py | 10 +- swarms/models/fuyu.py | 34 +- swarms/models/gpt4v.py | 13 +- swarms/models/huggingface.py | 7 +- swarms/models/kosmos_two.py | 5 +- swarms/models/openai_embeddings.py | 2 +- swarms/models/openai_models.py | 3 +- swarms/models/openai_tokenizer.py | 10 +- swarms/models/zephyr.py | 13 +- swarms/prompts/agent_prompt.py | 4 +- swarms/prompts/agent_prompts.py | 83 +++-- swarms/prompts/growth_agent_prompt.py | 26 +- swarms/prompts/multi_modal_prompts.py | 5 +- swarms/prompts/python.py | 64 +++- swarms/prompts/sales.py | 41 ++- swarms/prompts/sales_prompts.py | 41 ++- swarms/prompts/task_assignment_prompt.py | 8 +- swarms/schemas/typings.py | 5 +- swarms/structs/base.py | 2 +- swarms/structs/flow.py | 15 +- swarms/structs/sequential_workflow.py | 18 +- swarms/swarms/groupchat.py | 9 +- swarms/swarms/multi_agent_collab.py | 5 +- swarms/swarms/orchestrate.py | 3 +- swarms/tools/autogpt.py | 4 +- swarms/tools/mm_models.py | 67 ++-- swarms/tools/tool.py | 13 +- swarms/utils/code_interpreter.py | 4 +- swarms/utils/main.py | 5 +- swarms/workers/worker.py | 3 +- 50 files changed, 779 insertions(+), 395 deletions(-) create mode 100644 playground/models/dalle3.jpeg delete mode 100644 playground/models/fuyu.py create mode 100644 playground/models/fuyu_example.py create mode 100644 quality.sh diff --git a/playground/models/dalle3.jpeg b/playground/models/dalle3.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..39753795f0cb5990d6d7d465d6cd5f66faf50356 GIT binary patch literal 228428 zcmce-1zc5I*YLaPF6r*Bz3Gr{>28o_)39l21r=$Ll1}Lk5kv_=x?50MT0lDP!lUPS zp7@^Uz4yEC<_CMl9CMB_*BtXd$5@k_>6=At*n*!E!p95Y_E;o}sv6%w}Qx3mQd@$vy}A)^1Rj`fe~EMWiCD3=8cX5--rb#;LO0thre1*Com zSh{-rE`WclC6~Pg%%02A)z{`$07PQN1GBFxT_N@YEXrf<=vtUY ze(Ywm6paW_R6;8^3v0<=a@I0D8d5(5r9gkbA*G=K{#j$eo9u*}P12?+{d4<|s%2>V z6pnI%Yio7|3yhnujqAw%Nf7c=Fb})O{K?X_ZDGW#yLI2*f0)|mrIuSPs7JE-{s`~9D@UE zF21W$8JnJq0Y_qKue_T+K*vFLsa)>qdvRsv5}$v_0mJ?EPyZ19m;Pa4^~;g|onL@_ zZ~Y=P-}aB9PCq=#%EQLO)5h8v=4$KdW8q;VWoP5!=^+huv7=FPwy?9Ik-074>S66; 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-1,4 +0,0 @@ -from swarms.models import Fuyu - -fuyu = Fuyu() -fuyu("Hello, my name is", "images/github-banner-swarms.png") diff --git a/playground/models/fuyu_example.py b/playground/models/fuyu_example.py new file mode 100644 index 00000000..612c002e --- /dev/null +++ b/playground/models/fuyu_example.py @@ -0,0 +1,7 @@ +from swarms.models.fuyu import Fuyu + +img = "dalle3.jpeg" + +fuyu = Fuyu() + +fuyu("What is this image", img) diff --git a/pyproject.toml b/pyproject.toml index 75f0e7ca..62c0ec13 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.1.0" +version = "2.1.3" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] @@ -37,6 +37,7 @@ duckduckgo-search = "*" faiss-cpu = "*" datasets = "*" diffusers = "*" +accelerate = "*" sentencepiece = "*" wget = "*" griptape = "*" diff --git a/quality.sh b/quality.sh new file mode 100644 index 00000000..bf167079 --- /dev/null +++ b/quality.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Navigate to the directory containing the 'swarms' folder +# cd /path/to/your/code/directory + +# Run autopep8 with max aggressiveness (-aaa) and in-place modification (-i) +# on all Python files (*.py) under the 'swarms' directory. +autopep8 --in-place --aggressive --aggressive --recursive --experimental swarms/ + +# Run black with default settings, since black does not have an aggressiveness level. +# Black will format all Python files it finds in the 'swarms' directory. +black --experimental-string-processing swarms/ + +# Run ruff on the 'swarms' directory. +# Add any additional flags if needed according to your version of ruff. +ruff swarms/ + +# If you want to ensure the script stops if any command fails, add 'set -e' at the top. diff --git a/requirements.txt b/requirements.txt index cb0c65b8..f1a5c689 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,6 +28,7 @@ google-generativeai sentencepiece duckduckgo-search agent-protocol +accelerate chromadb tiktoken open-interpreter diff --git a/swarms/__init__.py b/swarms/__init__.py index f1225d81..8f0dfc26 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -1,3 +1,9 @@ +from swarms.agents import * +from swarms.swarms import * +from swarms.structs import * +from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py +from swarms.chunkers import * +from swarms.workers import * import os import warnings @@ -6,11 +12,3 @@ warnings.filterwarnings("ignore", category=UserWarning) # disable tensorflow warnings os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - - -from swarms.workers import * -from swarms.chunkers import * -from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py -from swarms.structs import * -from swarms.swarms import * -from swarms.agents import * diff --git a/swarms/agents/agent.py b/swarms/agents/agent.py index 109501f9..34d6315f 100644 --- a/swarms/agents/agent.py +++ b/swarms/agents/agent.py @@ -111,8 +111,7 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] [self.token_counter(doc) for doc in relevant_memory] ) content_format = ( - f"This reminds you of these events " - f"from your past:\n{relevant_memory}\n\n" + f"This reminds you of these events from your past:\n{relevant_memory}\n\n" ) memory_message = SystemMessage(content=content_format) used_tokens += self.token_counter(memory_message.content) @@ -233,14 +232,14 @@ class PromptGenerator: formatted_response_format = json.dumps(self.response_format, indent=4) prompt_string = ( f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n" - f"Commands:\n" + "Commands:\n" f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n" f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n" - f"Performance Evaluation:\n" + "Performance Evaluation:\n" f"{self._generate_numbered_list(self.performance_evaluation)}\n\n" - f"You should only respond in JSON format as described below " + "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - f"\nEnsure the response can be parsed by Python json.loads" + "\nEnsure the response can be parsed by Python json.loads" ) return prompt_string @@ -419,13 +418,11 @@ class AutoGPT: else: result = ( f"Unknown command '{action.name}'. " - f"Please refer to the 'COMMANDS' list for available " - f"commands and only respond in the specified JSON format." + "Please refer to the 'COMMANDS' list for available " + "commands and only respond in the specified JSON format." ) - memory_to_add = ( - f"Assistant Reply: {assistant_reply} " f"\nResult: {result} " - ) + memory_to_add = f"Assistant Reply: {assistant_reply} \nResult: {result} " if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: diff --git a/swarms/agents/aot.py b/swarms/agents/aot.py index 22af950e..b36fb43c 100644 --- a/swarms/agents/aot.py +++ b/swarms/agents/aot.py @@ -75,7 +75,8 @@ class OpenAI: except openai_model.error.RateLimitError as e: sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30) print( - f"{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT" + f"{str(e)}, sleep for {sleep_duratoin}s, set it by env" + " OPENAI_RATE_TIMEOUT" ) time.sleep(sleep_duratoin) diff --git a/swarms/agents/browser_agent.py b/swarms/agents/browser_agent.py index 2cede22a..1f4ff12e 100644 --- a/swarms/agents/browser_agent.py +++ b/swarms/agents/browser_agent.py @@ -53,10 +53,12 @@ def record(agent_name: str, autotab_ext_path: Optional[str] = None): file.write(data) print( - "\033[34mYou have the Python debugger open, you can run commands in it like you would in a normal Python shell.\033[0m" + "\033[34mYou have the Python debugger open, you can run commands in it like you" + " would in a normal Python shell.\033[0m" ) print( - "\033[34mTo exit, type 'q' and press enter. For a list of commands type '?' and press enter.\033[0m" + "\033[34mTo exit, type 'q' and press enter. For a list of commands type '?' and" + " press enter.\033[0m" ) breakpoint() @@ -116,7 +118,8 @@ def open_plugin_and_login(driver: AutotabChromeDriver): raise Exception("Invalid API key") else: raise Exception( - f"Error {response.status_code} from backend while logging you in with your API key: {response.text}" + f"Error {response.status_code} from backend while logging you in" + f" with your API key: {response.text}" ) cookie["name"] = cookie["key"] del cookie["key"] @@ -144,7 +147,8 @@ def get_driver( options = webdriver.ChromeOptions() options.add_argument("--no-sandbox") # Necessary for running options.add_argument( - "--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36" + "--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" + " (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36" ) options.add_argument("--enable-webgl") options.add_argument("--enable-3d-apis") @@ -371,7 +375,10 @@ def _login_with_google(driver, url: str, google_credentials: SiteCredentials): ) main_window = driver.current_window_handle - xpath = "//*[contains(text(), 'Continue with Google') or contains(text(), 'Sign in with Google') or contains(@title, 'Sign in with Google')]" + xpath = ( + "//*[contains(text(), 'Continue with Google') or contains(text(), 'Sign in with" + " Google') or contains(@title, 'Sign in with Google')]" + ) WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.XPATH, xpath))) driver.find_element( @@ -496,17 +503,17 @@ google_credentials: # Optional, specify alternative accounts to use with Google login on a per-service basis - email: you@gmail.com # Credentials without a name use email as key password: ... - + credentials: - notion.so: + notion.so: alts: - notion.com login_with_google_account: default - + figma.com: email: ... password: ... - + airtable.com: login_with_google_account: you@gmail.com """ diff --git a/swarms/agents/hf_agents.py b/swarms/agents/hf_agents.py index 28c18c71..7614b1aa 100644 --- a/swarms/agents/hf_agents.py +++ b/swarms/agents/hf_agents.py @@ -229,12 +229,14 @@ class Agent: if len(replacements) > 1: names = "\n".join([f"- {n}: {t}" for n, t in replacements.items()]) logger.warning( - f"The following tools have been replaced by the ones provided in `additional_tools`:\n{names}." + "The following tools have been replaced by the ones provided in" + f" `additional_tools`:\n{names}." ) elif len(replacements) == 1: name = list(replacements.keys())[0] logger.warning( - f"{name} has been replaced by {replacements[name]} as provided in `additional_tools`." + f"{name} has been replaced by {replacements[name]} as provided in" + " `additional_tools`." ) self.prepare_for_new_chat() @@ -425,9 +427,9 @@ class HFAgent(Agent): api_key = os.environ.get("OPENAI_API_KEY", None) if api_key is None: raise ValueError( - "You need an openai key to use `OpenAIAgent`. You can get one here: Get one here " - "https://openai.com/api/`. If you have one, set it in your env with `os.environ['OPENAI_API_KEY'] = " - "xxx." + "You need an openai key to use `OpenAIAgent`. You can get one here: Get" + " one here https://openai.com/api/`. If you have one, set it in your" + " env with `os.environ['OPENAI_API_KEY'] = xxx." ) else: openai.api_key = api_key @@ -540,8 +542,9 @@ class AzureOpenAI(Agent): api_key = os.environ.get("AZURE_OPENAI_API_KEY", None) if api_key is None: raise ValueError( - "You need an Azure openAI key to use `AzureOpenAIAgent`. If you have one, set it in your env with " - "`os.environ['AZURE_OPENAI_API_KEY'] = xxx." + "You need an Azure openAI key to use `AzureOpenAIAgent`. If you have" + " one, set it in your env with `os.environ['AZURE_OPENAI_API_KEY'] =" + " xxx." ) else: openai.api_key = api_key @@ -549,8 +552,9 @@ class AzureOpenAI(Agent): resource_name = os.environ.get("AZURE_OPENAI_RESOURCE_NAME", None) if resource_name is None: raise ValueError( - "You need a resource_name to use `AzureOpenAIAgent`. If you have one, set it in your env with " - "`os.environ['AZURE_OPENAI_RESOURCE_NAME'] = xxx." + "You need a resource_name to use `AzureOpenAIAgent`. If you have one," + " set it in your env with `os.environ['AZURE_OPENAI_RESOURCE_NAME'] =" + " xxx." ) else: openai.api_base = f"https://{resource_name}.openai.azure.com" diff --git a/swarms/agents/multi_modal_visual_agent.py b/swarms/agents/multi_modal_visual_agent.py index 68941ef0..34780594 100644 --- a/swarms/agents/multi_modal_visual_agent.py +++ b/swarms/agents/multi_modal_visual_agent.py @@ -270,10 +270,12 @@ class InstructPix2Pix: @prompts( name="Instruct Image Using Text", - description="useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. ", + description=( + "useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. " + ), ) def inference(self, inputs): """Change style of image.""" @@ -286,8 +288,8 @@ class InstructPix2Pix: updated_image_path = get_new_image_name(image_path, func_name="pix2pix") image.save(updated_image_path) print( - f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" + f" {text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -309,9 +311,12 @@ class Text2Image: @prompts( name="Generate Image From User Input Text", - description="useful when you want to generate an image from a user input text and save it to a file. " - "like: generate an image of an object or something, or generate an image that includes some objects. " - "The input to this tool should be a string, representing the text used to generate image. ", + description=( + "useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. " + ), ) def inference(self, text): image_filename = os.path.join("image", f"{str(uuid.uuid4())[:8]}.png") @@ -319,7 +324,8 @@ class Text2Image: image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0] image.save(image_filename) print( - f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}" + f"\nProcessed Text2Image, Input Text: {text}, Output Image:" + f" {image_filename}" ) return image_filename @@ -338,8 +344,11 @@ class ImageCaptioning: @prompts( name="Get Photo Description", - description="useful when you want to know what is inside the photo. receives image_path as input. " - "The input to this tool should be a string, representing the image_path. ", + description=( + "useful when you want to know what is inside the photo. receives image_path" + " as input. The input to this tool should be a string, representing the" + " image_path. " + ), ) def inference(self, image_path): inputs = self.processor(Image.open(image_path), return_tensors="pt").to( @@ -348,7 +357,8 @@ class ImageCaptioning: out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) print( - f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}" + f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text:" + f" {captions}" ) return captions @@ -361,10 +371,12 @@ class Image2Canny: @prompts( name="Edge Detection On Image", - description="useful when you want to detect the edge of the image. " - "like: detect the edges of this image, or canny detection on image, " - "or perform edge detection on this image, or detect the canny image of this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect the edge of the image. like: detect the" + " edges of this image, or canny detection on image, or perform edge" + " detection on this image, or detect the canny image of this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -376,7 +388,8 @@ class Image2Canny: updated_image_path = get_new_image_name(inputs, func_name="edge") canny.save(updated_image_path) print( - f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}" + f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text:" + f" {updated_image_path}" ) return updated_image_path @@ -410,11 +423,14 @@ class CannyText2Image: @prompts( name="Generate Image Condition On Canny Image", - description="useful when you want to generate a new real image from both the user description and a canny image." - " like: generate a real image of a object or something from this canny image," - " or generate a new real image of a object or something from this edge image. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description. ", + description=( + "useful when you want to generate a new real image from both the user" + " description and a canny image. like: generate a real image of a object or" + " something from this canny image, or generate a new real image of a object" + " or something from this edge image. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description. " + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -435,8 +451,8 @@ class CannyText2Image: updated_image_path = get_new_image_name(image_path, func_name="canny2image") image.save(updated_image_path) print( - f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, " - f"Output Text: {updated_image_path}" + f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text:" + f" {instruct_text}, Output Text: {updated_image_path}" ) return updated_image_path @@ -448,10 +464,13 @@ class Image2Line: @prompts( name="Line Detection On Image", - description="useful when you want to detect the straight line of the image. " - "like: detect the straight lines of this image, or straight line detection on image, " - "or perform straight line detection on this image, or detect the straight line image of this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect the straight line of the image. like:" + " detect the straight lines of this image, or straight line detection on" + " image, or perform straight line detection on this image, or detect the" + " straight line image of this image. The input to this tool should be a" + " string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -459,7 +478,8 @@ class Image2Line: updated_image_path = get_new_image_name(inputs, func_name="line-of") mlsd.save(updated_image_path) print( - f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}" + f"\nProcessed Image2Line, Input Image: {inputs}, Output Line:" + f" {updated_image_path}" ) return updated_image_path @@ -492,12 +512,14 @@ class LineText2Image: @prompts( name="Generate Image Condition On Line Image", - description="useful when you want to generate a new real image from both the user description " - "and a straight line image. " - "like: generate a real image of a object or something from this straight line image, " - "or generate a new real image of a object or something from this straight lines. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description. ", + description=( + "useful when you want to generate a new real image from both the user" + " description and a straight line image. like: generate a real image of a" + " object or something from this straight line image, or generate a new real" + " image of a object or something from this straight lines. The input to" + " this tool should be a comma separated string of two, representing the" + " image_path and the user description. " + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -518,8 +540,8 @@ class LineText2Image: updated_image_path = get_new_image_name(image_path, func_name="line2image") image.save(updated_image_path) print( - f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, " - f"Output Text: {updated_image_path}" + f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text:" + f" {instruct_text}, Output Text: {updated_image_path}" ) return updated_image_path @@ -531,10 +553,13 @@ class Image2Hed: @prompts( name="Hed Detection On Image", - description="useful when you want to detect the soft hed boundary of the image. " - "like: detect the soft hed boundary of this image, or hed boundary detection on image, " - "or perform hed boundary detection on this image, or detect soft hed boundary image of this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect the soft hed boundary of the image. like:" + " detect the soft hed boundary of this image, or hed boundary detection on" + " image, or perform hed boundary detection on this image, or detect soft" + " hed boundary image of this image. The input to this tool should be a" + " string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -542,7 +567,8 @@ class Image2Hed: updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") hed.save(updated_image_path) print( - f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}" + f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed:" + f" {updated_image_path}" ) return updated_image_path @@ -575,12 +601,14 @@ class HedText2Image: @prompts( name="Generate Image Condition On Soft Hed Boundary Image", - description="useful when you want to generate a new real image from both the user description " - "and a soft hed boundary image. " - "like: generate a real image of a object or something from this soft hed boundary image, " - "or generate a new real image of a object or something from this hed boundary. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and a soft hed boundary image. like: generate a real image of" + " a object or something from this soft hed boundary image, or generate a" + " new real image of a object or something from this hed boundary. The input" + " to this tool should be a comma separated string of two, representing the" + " image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -601,8 +629,8 @@ class HedText2Image: updated_image_path = get_new_image_name(image_path, func_name="hed2image") image.save(updated_image_path) print( - f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -614,10 +642,12 @@ class Image2Scribble: @prompts( name="Sketch Detection On Image", - description="useful when you want to generate a scribble of the image. " - "like: generate a scribble of this image, or generate a sketch from this image, " - "detect the sketch from this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to generate a scribble of the image. like: generate a" + " scribble of this image, or generate a sketch from this image, detect the" + " sketch from this image. The input to this tool should be a string," + " representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -625,7 +655,8 @@ class Image2Scribble: updated_image_path = get_new_image_name(inputs, func_name="scribble") scribble.save(updated_image_path) print( - f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}" + f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble:" + f" {updated_image_path}" ) return updated_image_path @@ -659,10 +690,12 @@ class ScribbleText2Image: @prompts( name="Generate Image Condition On Sketch Image", - description="useful when you want to generate a new real image from both the user description and " - "a scribble image or a sketch image. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and a scribble image or a sketch image. The input to this" + " tool should be a comma separated string of two, representing the" + " image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -683,8 +716,8 @@ class ScribbleText2Image: updated_image_path = get_new_image_name(image_path, func_name="scribble2image") image.save(updated_image_path) print( - f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -696,9 +729,11 @@ class Image2Pose: @prompts( name="Pose Detection On Image", - description="useful when you want to detect the human pose of the image. " - "like: generate human poses of this image, or generate a pose image from this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect the human pose of the image. like: generate" + " human poses of this image, or generate a pose image from this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -706,7 +741,8 @@ class Image2Pose: updated_image_path = get_new_image_name(inputs, func_name="human-pose") pose.save(updated_image_path) print( - f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}" + f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose:" + f" {updated_image_path}" ) return updated_image_path @@ -742,12 +778,13 @@ class PoseText2Image: @prompts( name="Generate Image Condition On Pose Image", - description="useful when you want to generate a new real image from both the user description " - "and a human pose image. " - "like: generate a real image of a human from this human pose image, " - "or generate a new real image of a human from this pose. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and a human pose image. like: generate a real image of a" + " human from this human pose image, or generate a new real image of a human" + " from this pose. The input to this tool should be a comma separated string" + " of two, representing the image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -768,8 +805,8 @@ class PoseText2Image: updated_image_path = get_new_image_name(image_path, func_name="pose2image") image.save(updated_image_path) print( - f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -802,11 +839,14 @@ class SegText2Image: @prompts( name="Generate Image Condition On Segmentations", - description="useful when you want to generate a new real image from both the user description and segmentations. " - "like: generate a real image of a object or something from this segmentation image, " - "or generate a new real image of a object or something from these segmentations. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and segmentations. like: generate a real image of a object or" + " something from this segmentation image, or generate a new real image of a" + " object or something from these segmentations. The input to this tool" + " should be a comma separated string of two, representing the image_path" + " and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -827,8 +867,8 @@ class SegText2Image: updated_image_path = get_new_image_name(image_path, func_name="segment2image") image.save(updated_image_path) print( - f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -840,9 +880,12 @@ class Image2Depth: @prompts( name="Predict Depth On Image", - description="useful when you want to detect depth of the image. like: generate the depth from this image, " - "or detect the depth map on this image, or predict the depth for this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect depth of the image. like: generate the" + " depth from this image, or detect the depth map on this image, or predict" + " the depth for this image. The input to this tool should be a string," + " representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -854,7 +897,8 @@ class Image2Depth: updated_image_path = get_new_image_name(inputs, func_name="depth") depth.save(updated_image_path) print( - f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}" + f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}" ) return updated_image_path @@ -888,11 +932,14 @@ class DepthText2Image: @prompts( name="Generate Image Condition On Depth", - description="useful when you want to generate a new real image from both the user description and depth image. " - "like: generate a real image of a object or something from this depth image, " - "or generate a new real image of a object or something from the depth map. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and depth image. like: generate a real image of a object or" + " something from this depth image, or generate a new real image of a object" + " or something from the depth map. The input to this tool should be a comma" + " separated string of two, representing the image_path and the user" + " description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -913,8 +960,8 @@ class DepthText2Image: updated_image_path = get_new_image_name(image_path, func_name="depth2image") image.save(updated_image_path) print( - f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -929,9 +976,11 @@ class Image2Normal: @prompts( name="Predict Normal Map On Image", - description="useful when you want to detect norm map of the image. " - "like: generate normal map from this image, or predict normal map of this image. " - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to detect norm map of the image. like: generate" + " normal map from this image, or predict normal map of this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -954,7 +1003,8 @@ class Image2Normal: updated_image_path = get_new_image_name(inputs, func_name="normal-map") image.save(updated_image_path) print( - f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}" + f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}" ) return updated_image_path @@ -988,11 +1038,14 @@ class NormalText2Image: @prompts( name="Generate Image Condition On Normal Map", - description="useful when you want to generate a new real image from both the user description and normal map. " - "like: generate a real image of a object or something from this normal map, " - "or generate a new real image of a object or something from the normal map. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the user description", + description=( + "useful when you want to generate a new real image from both the user" + " description and normal map. like: generate a real image of a object or" + " something from this normal map, or generate a new real image of a object" + " or something from the normal map. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( @@ -1013,8 +1066,8 @@ class NormalText2Image: updated_image_path = get_new_image_name(image_path, func_name="normal2image") image.save(updated_image_path) print( - f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -1031,9 +1084,12 @@ class VisualQuestionAnswering: @prompts( name="Answer Question About The Image", - description="useful when you need an answer for a question based on an image. " - "like: what is the background color of the last image, how many cats in this figure, what is in this figure. " - "The input to this tool should be a comma separated string of two, representing the image_path and the question", + description=( + "useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" + ), ) def inference(self, inputs): image_path, question = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) @@ -1044,8 +1100,8 @@ class VisualQuestionAnswering: out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) print( - f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, " - f"Output Answer: {answer}" + f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" + f" Question: {question}, Output Answer: {answer}" ) return answer @@ -1245,12 +1301,13 @@ class Segmenting: @prompts( name="Segment the Image", - description="useful when you want to segment all the part of the image, but not segment a certain object." - "like: segment all the object in this image, or generate segmentations on this image, " - "or segment the image," - "or perform segmentation on this image, " - "or segment all the object in this image." - "The input to this tool should be a string, representing the image_path", + description=( + "useful when you want to segment all the part of the image, but not segment" + " a certain object.like: segment all the object in this image, or generate" + " segmentations on this image, or segment the image,or perform segmentation" + " on this image, or segment all the object in this image.The input to this" + " tool should be a string, representing the image_path" + ), ) def inference_all(self, image_path): image = cv2.imread(image_path) @@ -1401,9 +1458,12 @@ class Text2Box: @prompts( name="Detect the Give Object", - description="useful when you only want to detect or find out given objects in the picture" - "The input to this tool should be a comma separated string of two, " - "representing the image_path, the text description of the object to be found", + description=( + "useful when you only want to detect or find out given objects in the" + " pictureThe input to this tool should be a comma separated string of two," + " representing the image_path, the text description of the object to be" + " found" + ), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") @@ -1427,8 +1487,8 @@ class Text2Box: updated_image = image_with_box.resize(size) updated_image.save(updated_image_path) print( - f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be Detect {det_prompt}, " - f"Output Image: {updated_image_path}" + f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be" + f" Detect {det_prompt}, Output Image: {updated_image_path}" ) return updated_image_path @@ -1483,7 +1543,8 @@ class InfinityOutPainting: out = self.ImageVQA.model.generate(**inputs) answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True) print( - f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}" + f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output" + f" Answer: {answer}" ) return answer @@ -1499,9 +1560,9 @@ class InfinityOutPainting: def check_prompt(self, prompt): check = ( - f"Here is a paragraph with adjectives. " + "Here is a paragraph with adjectives. " f"{prompt} " - f"Please change all plural forms in the adjectives to singular forms. " + "Please change all plural forms in the adjectives to singular forms. " ) return self.llm(check) @@ -1512,13 +1573,12 @@ class InfinityOutPainting: ) style = self.get_BLIP_vqa(image, "what is the style of this image") imagine_prompt = ( - f"let's pretend you are an excellent painter and now " - f"there is an incomplete painting with {BLIP_caption} in the center, " - f"please imagine the complete painting and describe it" - f"you should consider the background color is {background_color}, the style is {style}" - f"You should make the painting as vivid and realistic as possible" - f"You can not use words like painting or picture" - f"and you should use no more than 50 words to describe it" + "let's pretend you are an excellent painter and now there is an incomplete" + f" painting with {BLIP_caption} in the center, please imagine the complete" + " painting and describe ityou should consider the background color is" + f" {background_color}, the style is {style}You should make the painting as" + " vivid and realistic as possibleYou can not use words like painting or" + " pictureand you should use no more than 50 words to describe it" ) caption = self.llm(imagine_prompt) if imagine else BLIP_caption caption = self.check_prompt(caption) @@ -1580,9 +1640,12 @@ class InfinityOutPainting: @prompts( name="Extend An Image", - description="useful when you need to extend an image into a larger image." - "like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. " - "The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight", + description=( + "useful when you need to extend an image into a larger image.like: extend" + " the image into a resolution of 2048x1024, extend the image into" + " 2048x1024. The input to this tool should be a comma separated string of" + " two, representing the image_path and the resolution of widthxheight" + ), ) def inference(self, inputs): image_path, resolution = inputs.split(",") @@ -1594,8 +1657,8 @@ class InfinityOutPainting: updated_image_path = get_new_image_name(image_path, func_name="outpainting") out_painted_image.save(updated_image_path) print( - f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, " - f"Output Image: {updated_image_path}" + f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input" + f" Resolution: {resolution}, Output Image: {updated_image_path}" ) return updated_image_path @@ -1610,12 +1673,13 @@ class ObjectSegmenting: @prompts( name="Segment the given object", - description="useful when you only want to segment the certain objects in the picture" - "according to the given text" - "like: segment the cat," - "or can you segment an obeject for me" - "The input to this tool should be a comma separated string of two, " - "representing the image_path, the text description of the object to be found", + description=( + "useful when you only want to segment the certain objects in the" + " pictureaccording to the given textlike: segment the cat,or can you" + " segment an obeject for meThe input to this tool should be a comma" + " separated string of two, representing the image_path, the text" + " description of the object to be found" + ), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") @@ -1627,8 +1691,8 @@ class ObjectSegmenting: image_pil, image_path, boxes_filt, pred_phrases ) print( - f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be Segment {det_prompt}, " - f"Output Image: {updated_image_path}" + f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be" + f" Segment {det_prompt}, Output Image: {updated_image_path}" ) return updated_image_path @@ -1710,10 +1774,12 @@ class ImageEditing: @prompts( name="Remove Something From The Photo", - description="useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. ", + description=( + "useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. " + ), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",")[0], ",".join( @@ -1725,10 +1791,12 @@ class ImageEditing: @prompts( name="Replace Something From The Photo", - description="useful when you want to replace an object from the object description or " - "location with another object from its description. " - "The input to this tool should be a comma separated string of three, " - "representing the image_path, the object to be replaced, the object to be replaced with ", + description=( + "useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with " + ), ) def inference_replace_sam(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") @@ -1758,8 +1826,9 @@ class ImageEditing: updated_image = updated_image.resize(image_pil.size) updated_image.save(updated_image_path) print( - f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, " - f"Output Image: {updated_image_path}" + f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" + f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" + f" {updated_image_path}" ) return updated_image_path @@ -1782,8 +1851,10 @@ class BackgroundRemoving: @prompts( name="Remove the background", - description="useful when you want to extract the object or remove the background," - "the input should be a string image_path", + description=( + "useful when you want to extract the object or remove the background," + "the input should be a string image_path" + ), ) def inference(self, image_path): """ @@ -1833,7 +1904,8 @@ class MultiModalVisualAgent: if "ImageCaptioning" not in load_dict: raise ValueError( - "You have to load ImageCaptioning as a basic function for MultiModalVisualAgent" + "You have to load ImageCaptioning as a basic function for" + " MultiModalVisualAgent" ) self.models = {} @@ -1944,10 +2016,21 @@ class MultiModalVisualAgent: description = self.models["ImageCaptioning"].inference(image_filename) if lang == "Chinese": - Human_prompt = f'\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ: {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚ ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n' + Human_prompt = ( + f"\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ:" + f" {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒ" + "ไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚" + ' ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n' + ) AI_prompt = "ๆ”ถๅˆฐใ€‚ " else: - Human_prompt = f'\nHuman: provide a figure named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say "Received". \n' + Human_prompt = ( + f"\nHuman: provide a figure named {image_filename}. The description is:" + f" {description}. This information helps you to understand this image," + " but you should use tools to finish following tasks, rather than" + " directly imagine from my description. If you understand, say" + ' "Received". \n' + ) AI_prompt = "Received. " self.agent.memory.buffer = ( diff --git a/swarms/agents/profitpilot.py b/swarms/agents/profitpilot.py index 8de7dbf0..6858dc72 100644 --- a/swarms/agents/profitpilot.py +++ b/swarms/agents/profitpilot.py @@ -163,7 +163,9 @@ def get_tools(product_catalog): Tool( name="ProductSearch", func=knowledge_base.run, - description="useful for when you need to answer questions about product information", + description=( + "useful for when you need to answer questions about product information" + ), ), # omnimodal agent ] @@ -224,7 +226,10 @@ class SalesConvoOutputParser(AgentOutputParser): # TODO - this is not entirely reliable, sometimes results in an error. return AgentFinish( { - "output": "I apologize, I was unable to find the answer to your question. Is there anything else I can help with?" + "output": ( + "I apologize, I was unable to find the answer to your question." + " Is there anything else I can help with?" + ) }, text, ) @@ -250,21 +255,62 @@ class ProfitPilot(Chain, BaseModel): use_tools: bool = False conversation_stage_dict: Dict = { - "1": "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.", - "2": "Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.", - "3": "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.", - "4": "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.", - "5": "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.", - "6": "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.", - "7": "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.", + "1": ( + "Introduction: Start the conversation by introducing yourself and your" + " company. Be polite and respectful while keeping the tone of the" + " conversation professional. Your greeting should be welcoming. Always" + " clarify in your greeting the reason why you are contacting the prospect." + ), + "2": ( + "Qualification: Qualify the prospect by confirming if they are the right" + " person to talk to regarding your product/service. Ensure that they have" + " the authority to make purchasing decisions." + ), + "3": ( + "Value proposition: Briefly explain how your product/service can benefit" + " the prospect. Focus on the unique selling points and value proposition of" + " your product/service that sets it apart from competitors." + ), + "4": ( + "Needs analysis: Ask open-ended questions to uncover the prospect's needs" + " and pain points. Listen carefully to their responses and take notes." + ), + "5": ( + "Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": ( + "Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims." + ), + "7": ( + "Close: Ask for the sale by proposing a next step. This could be a demo, a" + " trial or a meeting with decision-makers. Ensure to summarize what has" + " been discussed and reiterate the benefits." + ), } salesperson_name: str = "Ted Lasso" salesperson_role: str = "Business Development Representative" company_name: str = "Sleep Haven" - company_business: str = "Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers." - company_values: str = "Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service." - conversation_purpose: str = "find out whether they are looking to achieve better sleep via buying a premier mattress." + company_business: str = ( + "Sleep Haven is a premium mattress company that provides customers with the" + " most comfortable and supportive sleeping experience possible. We offer a" + " range of high-quality mattresses, pillows, and bedding accessories that are" + " designed to meet the unique needs of our customers." + ) + company_values: str = ( + "Our mission at Sleep Haven is to help people achieve a better night's sleep by" + " providing them with the best possible sleep solutions. We believe that" + " quality sleep is essential to overall health and well-being, and we are" + " committed to helping our customers achieve optimal sleep by offering" + " exceptional products and customer service." + ) + conversation_purpose: str = ( + "find out whether they are looking to achieve better sleep via buying a premier" + " mattress." + ) conversation_type: str = "call" def retrieve_conversation_stage(self, key): @@ -412,14 +458,32 @@ config = dict( salesperson_name="Ted Lasso", salesperson_role="Business Development Representative", company_name="Sleep Haven", - company_business="Sleep Haven is a premium mattress company that provides customers with the most comfortable and supportive sleeping experience possible. We offer a range of high-quality mattresses, pillows, and bedding accessories that are designed to meet the unique needs of our customers.", - company_values="Our mission at Sleep Haven is to help people achieve a better night's sleep by providing them with the best possible sleep solutions. We believe that quality sleep is essential to overall health and well-being, and we are committed to helping our customers achieve optimal sleep by offering exceptional products and customer service.", - conversation_purpose="find out whether they are looking to achieve better sleep via buying a premier mattress.", + company_business=( + "Sleep Haven is a premium mattress company that provides customers with the" + " most comfortable and supportive sleeping experience possible. We offer a" + " range of high-quality mattresses, pillows, and bedding accessories that are" + " designed to meet the unique needs of our customers." + ), + company_values=( + "Our mission at Sleep Haven is to help people achieve a better night's sleep by" + " providing them with the best possible sleep solutions. We believe that" + " quality sleep is essential to overall health and well-being, and we are" + " committed to helping our customers achieve optimal sleep by offering" + " exceptional products and customer service." + ), + conversation_purpose=( + "find out whether they are looking to achieve better sleep via buying a premier" + " mattress." + ), conversation_history=[], conversation_type="call", conversation_stage=conversation_stages.get( "1", - "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional.", + ( + "Introduction: Start the conversation by introducing yourself and your" + " company. Be polite and respectful while keeping the tone of the" + " conversation professional." + ), ), use_tools=True, product_catalog="sample_product_catalog.txt", diff --git a/swarms/agents/registry.py b/swarms/agents/registry.py index b53b5714..aa1f1375 100644 --- a/swarms/agents/registry.py +++ b/swarms/agents/registry.py @@ -19,7 +19,8 @@ class Registry(BaseModel): def build(self, type: str, **kwargs): if type not in self.entries: raise ValueError( - f'{type} is not registered. Please register with the .register("{type}") method provided in {self.name} registry' + f"{type} is not registered. Please register with the" + f' .register("{type}") method provided in {self.name} registry' ) return self.entries[type](**kwargs) diff --git a/swarms/memory/chroma.py b/swarms/memory/chroma.py index 422d0a67..67ba4cb2 100644 --- a/swarms/memory/chroma.py +++ b/swarms/memory/chroma.py @@ -460,7 +460,7 @@ class Chroma(VectorStore): """ if self._embedding_function is None: raise ValueError( - "For MMR search, you must specify an embedding function on" "creation." + "For MMR search, you must specify an embedding function oncreation." ) embedding = self._embedding_function.embed_query(query) diff --git a/swarms/memory/schemas.py b/swarms/memory/schemas.py index 0405323d..bbc71bc2 100644 --- a/swarms/memory/schemas.py +++ b/swarms/memory/schemas.py @@ -111,7 +111,10 @@ class Step(StepRequestBody): output: Optional[str] = Field( None, description="Output of the task step.", - example="I am going to use the write_to_file command and write Washington to a file called output.txt >> fuyu("Hello, my name is", "path/to/image.png") - """ def __init__( self, pretrained_path: str = "adept/fuyu-8b", - device_map: str = "cuda:0", - max_new_tokens: int = 7, + device_map: str = "auto", + max_new_tokens: int = 500, + *args, + **kwargs, ): self.pretrained_path = pretrained_path self.device_map = device_map @@ -44,12 +47,19 @@ class Fuyu: self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path) self.image_processor = FuyuImageProcessor() self.processor = FuyuProcessor( - image_procesor=self.image_processor, tokenizer=self.tokenizer + image_processor=self.image_processor, tokenizer=self.tokenizer, **kwargs ) self.model = FuyuForCausalLM.from_pretrained( - pretrained_path, device_map=device_map + pretrained_path, + device_map=device_map, + **kwargs, ) + def get_img(self, img: str): + """Get the image from the path""" + image_pil = Image.open(img) + return image_pil + def __call__(self, text: str, img: str): """Call the model with text and img paths""" image_pil = Image.open(img) @@ -63,3 +73,9 @@ class Fuyu: output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens) text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) return print(str(text)) + + def get_img_from_web(self, img_url: str): + """Get the image from the web""" + response = requests.get(img_url) + image_pil = Image.open(BytesIO(response.content)) + return image_pil diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py index 99580d82..3fa87443 100644 --- a/swarms/models/gpt4v.py +++ b/swarms/models/gpt4v.py @@ -178,7 +178,8 @@ class GPT4Vision: time.sleep(self.backoff_factor**attempt) except Exception as error: self.logger.error( - f"Unexpected Error: {error} try optimizing your api key and try again" + f"Unexpected Error: {error} try optimizing your api key and try" + " again" ) raise error from None @@ -231,7 +232,10 @@ class GPT4Vision: except Exception as error: print( colored( - f"Error when calling GPT4Vision, Error: {error} Try optimizing your key, and try again", + ( + f"Error when calling GPT4Vision, Error: {error} Try optimizing" + " your key, and try again" + ), "red", ) ) @@ -282,7 +286,10 @@ class GPT4Vision: except Exception as error: print( colored( - f"Error when calling GPT4Vision, Error: {error} Try optimizing your key, and try again", + ( + f"Error when calling GPT4Vision, Error: {error} Try optimizing" + " your key, and try again" + ), "red", ) ) diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index f11bf3df..9279fea4 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -166,7 +166,10 @@ class HuggingfaceLLM: except Exception as e: print( colored( - f"HuggingfaceLLM could not generate text because of error: {e}, try optimizing your arguments", + ( + f"HuggingfaceLLM could not generate text because of error: {e}," + " try optimizing your arguments" + ), "red", ) ) @@ -299,7 +302,7 @@ class HuggingfaceLLM: Task Environment: Task: {task} - + """, "red", ) diff --git a/swarms/models/kosmos_two.py b/swarms/models/kosmos_two.py index eee834f3..596886f3 100644 --- a/swarms/models/kosmos_two.py +++ b/swarms/models/kosmos_two.py @@ -106,7 +106,10 @@ class Kosmos: self.run(prompt, image_url) def referring_expression_generation(self, phrase, image_url): - prompt = " It is" + prompt = ( + "" + " It is" + ) self.run(prompt, image_url) def grounded_vqa(self, question, image_url): diff --git a/swarms/models/openai_embeddings.py b/swarms/models/openai_embeddings.py index 0aa3473d..81dea550 100644 --- a/swarms/models/openai_embeddings.py +++ b/swarms/models/openai_embeddings.py @@ -233,7 +233,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " - f"Instead they were passed in as part of `model_kwargs` parameter." + "Instead they were passed in as part of `model_kwargs` parameter." ) values["model_kwargs"] = extra diff --git a/swarms/models/openai_models.py b/swarms/models/openai_models.py index db030a71..0c803755 100644 --- a/swarms/models/openai_models.py +++ b/swarms/models/openai_models.py @@ -593,7 +593,8 @@ class BaseOpenAI(BaseLLM): if context_size is None: raise ValueError( f"Unknown model: {modelname}. Please provide a valid OpenAI model name." - "Known models are: " + ", ".join(model_token_mapping.keys()) + "Known models are: " + + ", ".join(model_token_mapping.keys()) ) return context_size diff --git a/swarms/models/openai_tokenizer.py b/swarms/models/openai_tokenizer.py index ee0ea363..9ff1fa08 100644 --- a/swarms/models/openai_tokenizer.py +++ b/swarms/models/openai_tokenizer.py @@ -112,18 +112,20 @@ class OpenAITokenizer(BaseTokenizer): tokens_per_name = -1 elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: logging.info( - "gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613." + "gpt-3.5-turbo may update over time. Returning num tokens assuming" + " gpt-3.5-turbo-0613." ) return self.count_tokens(text, model="gpt-3.5-turbo-0613") elif "gpt-4" in model: logging.info( - "gpt-4 may update over time. Returning num tokens assuming gpt-4-0613." + "gpt-4 may update over time. Returning num tokens assuming" + " gpt-4-0613." ) return self.count_tokens(text, model="gpt-4-0613") else: raise NotImplementedError( - f"""token_count() is not implemented for model {model}. - See https://github.com/openai/openai-python/blob/main/chatml.md for + f"""token_count() is not implemented for model {model}. + See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""" ) diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index 5a6467b7..f75945ea 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -33,6 +33,7 @@ class Zephyr: temperature: float = 0.5, top_k: float = 50, top_p: float = 0.95, + do_sample: bool = True, *args, **kwargs, ): @@ -45,6 +46,7 @@ class Zephyr: self.temperature = temperature self.top_k = top_k self.top_p = top_p + self.do_sample = do_sample self.pipe = pipeline( "text-generation", @@ -57,10 +59,6 @@ class Zephyr: "role": "system", "content": f"{self.system_prompt}\n\nUser:", }, - { - "role": "user", - "content": "How many helicopters can a human eat in one sitting?", - }, ] def __call__(self, task: str): @@ -82,14 +80,16 @@ class Zephyr: # Apply the chat template to format the messages prompt = self.pipe.tokenizer.apply_chat_template( - self.messages, tokenize=False, add_generation_prompt=True + self.messages, + tokenize=self.tokenize, + add_generation_prompt=self.add_generation_prompt, ) # Generate a response outputs = self.pipe( prompt, max_new_tokens=self.max_new_tokens, - do_sample=True, + do_sample=self.do_sample, temperature=self.temperature, top_k=self.top_k, top_p=self.top_p, @@ -101,5 +101,4 @@ class Zephyr: # Optionally, you could also add the chatbot's response to the messages list # However, the below line should be adjusted to extract the chatbot's response only # self.messages.append({"role": "bot", "content": generated_text}) - return generated_text diff --git a/swarms/prompts/agent_prompt.py b/swarms/prompts/agent_prompt.py index 747b7949..c4897193 100644 --- a/swarms/prompts/agent_prompt.py +++ b/swarms/prompts/agent_prompt.py @@ -70,9 +70,9 @@ class PromptGenerator: f"Commands:\n{''.join(self.commands)}\n\n" f"Resources:\n{''.join(self.resources)}\n\n" f"Performance Evaluation:\n{''.join(self.performance_evaluation)}\n\n" - f"You should only respond in JSON format as described below " + "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - f"\nEnsure the response can be parsed by Python json.loads" + "\nEnsure the response can be parsed by Python json.loads" ) return prompt_string diff --git a/swarms/prompts/agent_prompts.py b/swarms/prompts/agent_prompts.py index 350545ff..8d145fc0 100644 --- a/swarms/prompts/agent_prompts.py +++ b/swarms/prompts/agent_prompts.py @@ -4,10 +4,28 @@ def generate_agent_role_prompt(agent): Returns: str: The agent role prompt. """ prompts = { - "Finance Agent": "You are a seasoned finance analyst AI assistant. Your primary goal is to compose comprehensive, astute, impartial, and methodically arranged financial reports based on provided data and trends.", - "Travel Agent": "You are a world-travelled AI tour guide assistant. Your main purpose is to draft engaging, insightful, unbiased, and well-structured travel reports on given locations, including history, attractions, and cultural insights.", - "Academic Research Agent": "You are an AI academic research assistant. Your primary responsibility is to create thorough, academically rigorous, unbiased, and systematically organized reports on a given research topic, following the standards of scholarly work.", - "Default Agent": "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text.", + "Finance Agent": ( + "You are a seasoned finance analyst AI assistant. Your primary goal is to" + " compose comprehensive, astute, impartial, and methodically arranged" + " financial reports based on provided data and trends." + ), + "Travel Agent": ( + "You are a world-travelled AI tour guide assistant. Your main purpose is to" + " draft engaging, insightful, unbiased, and well-structured travel reports" + " on given locations, including history, attractions, and cultural" + " insights." + ), + "Academic Research Agent": ( + "You are an AI academic research assistant. Your primary responsibility is" + " to create thorough, academically rigorous, unbiased, and systematically" + " organized reports on a given research topic, following the standards of" + " scholarly work." + ), + "Default Agent": ( + "You are an AI critical thinker research assistant. Your sole purpose is to" + " write well written, critically acclaimed, objective and structured" + " reports on given text." + ), } return prompts.get(agent, "No such agent") @@ -22,10 +40,11 @@ def generate_report_prompt(question, research_summary): return ( f'"""{research_summary}""" Using the above information, answer the following' - f' question or topic: "{question}" in a detailed report --' - " The report should focus on the answer to the question, should be well structured, informative," - " in depth, with facts and numbers if available, a minimum of 1,200 words and with markdown syntax and apa format. " - "Write all source urls at the end of the report in apa format" + f' question or topic: "{question}" in a detailed report -- The report should' + " focus on the answer to the question, should be well structured, informative," + " in depth, with facts and numbers if available, a minimum of 1,200 words and" + " with markdown syntax and apa format. Write all source urls at the end of the" + " report in apa format" ) @@ -36,8 +55,9 @@ def generate_search_queries_prompt(question): """ return ( - f'Write 4 google search queries to search online that form an objective opinion from the following: "{question}"' - f'You must respond with a list of strings in the following format: ["query 1", "query 2", "query 3", "query 4"]' + "Write 4 google search queries to search online that form an objective opinion" + f' from the following: "{question}"You must respond with a list of strings in' + ' the following format: ["query 1", "query 2", "query 3", "query 4"]' ) @@ -52,13 +72,15 @@ def generate_resource_report_prompt(question, research_summary): str: The resource report prompt for the given question and research summary. """ return ( - f'"""{research_summary}""" Based on the above information, generate a bibliography recommendation report for the following' - f' question or topic: "{question}". The report should provide a detailed analysis of each recommended resource,' - " explaining how each source can contribute to finding answers to the research question." - " Focus on the relevance, reliability, and significance of each source." - " Ensure that the report is well-structured, informative, in-depth, and follows Markdown syntax." - " Include relevant facts, figures, and numbers whenever available." - " The report should have a minimum length of 1,200 words." + f'"""{research_summary}""" Based on the above information, generate a' + " bibliography recommendation report for the following question or topic:" + f' "{question}". The report should provide a detailed analysis of each' + " recommended resource, explaining how each source can contribute to finding" + " answers to the research question. Focus on the relevance, reliability, and" + " significance of each source. Ensure that the report is well-structured," + " informative, in-depth, and follows Markdown syntax. Include relevant facts," + " figures, and numbers whenever available. The report should have a minimum" + " length of 1,200 words." ) @@ -70,11 +92,13 @@ def generate_outline_report_prompt(question, research_summary): """ return ( - f'"""{research_summary}""" Using the above information, generate an outline for a research report in Markdown syntax' - f' for the following question or topic: "{question}". The outline should provide a well-structured framework' - " for the research report, including the main sections, subsections, and key points to be covered." - " The research report should be detailed, informative, in-depth, and a minimum of 1,200 words." - " Use appropriate Markdown syntax to format the outline and ensure readability." + f'"""{research_summary}""" Using the above information, generate an outline for' + " a research report in Markdown syntax for the following question or topic:" + f' "{question}". The outline should provide a well-structured framework for the' + " research report, including the main sections, subsections, and key points to" + " be covered. The research report should be detailed, informative, in-depth," + " and a minimum of 1,200 words. Use appropriate Markdown syntax to format the" + " outline and ensure readability." ) @@ -86,9 +110,11 @@ def generate_concepts_prompt(question, research_summary): """ return ( - f'"""{research_summary}""" Using the above information, generate a list of 5 main concepts to learn for a research report' - f' on the following question or topic: "{question}". The outline should provide a well-structured framework' - 'You must respond with a list of strings in the following format: ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]' + f'"""{research_summary}""" Using the above information, generate a list of 5' + " main concepts to learn for a research report on the following question or" + f' topic: "{question}". The outline should provide a well-structured' + " frameworkYou must respond with a list of strings in the following format:" + ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]' ) @@ -102,9 +128,10 @@ def generate_lesson_prompt(concept): """ prompt = ( - f"generate a comprehensive lesson about {concept} in Markdown syntax. This should include the definition" - f"of {concept}, its historical background and development, its applications or uses in different" - f"fields, and notable events or facts related to {concept}." + f"generate a comprehensive lesson about {concept} in Markdown syntax. This" + f" should include the definitionof {concept}, its historical background and" + " development, its applications or uses in differentfields, and notable events" + f" or facts related to {concept}." ) return prompt diff --git a/swarms/prompts/growth_agent_prompt.py b/swarms/prompts/growth_agent_prompt.py index 9ac74a06..117148d9 100644 --- a/swarms/prompts/growth_agent_prompt.py +++ b/swarms/prompts/growth_agent_prompt.py @@ -46,47 +46,47 @@ Growth Agent is a dynamic fusion of digital marketing, content creation, and cus - **3.1 Data Assimilation and Interpretation** - *3.1.1* Efficiently process vast volumes of data using state-of-the-art algorithms. - + - *3.1.2* Identify key patterns, trends, and anomalies to derive actionable insights. - + - *3.1.3* Use these insights to predict future trends and user behaviors. - **3.2 Ad Generation** - *3.2.1* Leverage Generative Adversarial Networks (GANs) to craft engaging ads. - + - *3.2.2* Implement A/B testing mechanisms to select high-performing ads. - + - *3.2.3* Continuously refine ad generation based on user feedback and interactions. - **3.3 Website Creation and Optimization** - *3.3.1* Use responsive design principles for accessibility across devices. - + - *3.3.2* Integrate user tracking tools to gain insights into navigation patterns. - + - *3.3.3* Leverage AI-driven chatbots and interactive elements to improve user engagement and retention. - **3.4 Messaging Sequences** - *3.4.1* Craft sequences tailored to individual user behaviors and interactions. - + - *3.4.2* Harness advanced Natural Language Processing (NLP) tools for optimal communication. - + - *3.4.3* Periodically update sequences based on user feedback and evolving market trends. - **3.5 Systematic Growth and Enhancement** - *3.5.1* Implement reinforcement learning for real-time adaptation and strategy refinement. - + - *3.5.2* Engage in regular feedback loops with users to understand needs and pain points. - + - *3.5.3* Benchmark performance against industry leaders to identify areas of improvement. - **3.6 Integration and Collaboration** - *3.6.1* Seamlessly integrate with other digital platforms and tools. - + - *3.6.2* Collaborate with other AI models or systems to harness collective intelligence. --- @@ -96,9 +96,9 @@ Growth Agent is a dynamic fusion of digital marketing, content creation, and cus Achieving world-class expertise is a journey, not a destination. Ensure: - **4.1** Regular system diagnostics and optimization checks. - + - **4.2** Inclusion of emerging platforms and technologies into the learning paradigm. - + - **4.3** Frequent benchmarking against top industry standards. --- diff --git a/swarms/prompts/multi_modal_prompts.py b/swarms/prompts/multi_modal_prompts.py index 9165eb3e..f558c3c4 100644 --- a/swarms/prompts/multi_modal_prompts.py +++ b/swarms/prompts/multi_modal_prompts.py @@ -1,4 +1,7 @@ -ERROR_PROMPT = "An error has occurred for the following text: \n{promptedQuery} Please explain this error.\n {e}" +ERROR_PROMPT = ( + "An error has occurred for the following text: \n{promptedQuery} Please explain" + " this error.\n {e}" +) IMAGE_PROMPT = """ provide a figure named {filename}. The description is: {description}. diff --git a/swarms/prompts/python.py b/swarms/prompts/python.py index 6ddda6ae..9d1f4a1e 100644 --- a/swarms/prompts/python.py +++ b/swarms/prompts/python.py @@ -1,12 +1,43 @@ PY_SIMPLE_COMPLETION_INSTRUCTION = "# Write the body of this function only." -PY_REFLEXION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature).\n\n-----" -PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation.\n\n-----" -USE_PYTHON_CODEBLOCK_INSTRUCTION = "Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```" - -PY_SIMPLE_CHAT_INSTRUCTION = "You are an AI that only responds with python code, NOT ENGLISH. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)." -PY_SIMPLE_CHAT_INSTRUCTION_V2 = "You are an AI that only responds with only python code. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)." -PY_REFLEXION_CHAT_INSTRUCTION = "You are an AI Python assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature)." -PY_REFLEXION_CHAT_INSTRUCTION_V2 = "You are an AI Python assistant. You will be given your previous implementation of a function, a series of unit tests results, and your self-reflection on your previous implementation. Write your full implementation (restate the function signature)." +PY_REFLEXION_COMPLETION_INSTRUCTION = ( + "You are a Python writing assistant. You will be given your past function" + " implementation, a series of unit tests, and a hint to change the implementation" + " appropriately. Write your full implementation (restate the function" + " signature).\n\n-----" +) +PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = ( + "You are a Python writing assistant. You will be given a function implementation" + " and a series of unit tests. Your goal is to write a few sentences to explain why" + " your implementation is wrong as indicated by the tests. You will need this as a" + " hint when you try again later. Only provide the few sentence description in your" + " answer, not the implementation.\n\n-----" +) +USE_PYTHON_CODEBLOCK_INSTRUCTION = ( + "Use a Python code block to write your response. For" + " example:\n```python\nprint('Hello world!')\n```" +) + +PY_SIMPLE_CHAT_INSTRUCTION = ( + "You are an AI that only responds with python code, NOT ENGLISH. You will be given" + " a function signature and its docstring by the user. Write your full" + " implementation (restate the function signature)." +) +PY_SIMPLE_CHAT_INSTRUCTION_V2 = ( + "You are an AI that only responds with only python code. You will be given a" + " function signature and its docstring by the user. Write your full implementation" + " (restate the function signature)." +) +PY_REFLEXION_CHAT_INSTRUCTION = ( + "You are an AI Python assistant. You will be given your past function" + " implementation, a series of unit tests, and a hint to change the implementation" + " appropriately. Write your full implementation (restate the function signature)." +) +PY_REFLEXION_CHAT_INSTRUCTION_V2 = ( + "You are an AI Python assistant. You will be given your previous implementation of" + " a function, a series of unit tests results, and your self-reflection on your" + " previous implementation. Write your full implementation (restate the function" + " signature)." +) PY_REFLEXION_FEW_SHOT_ADD = '''Example 1: [previous impl]: ```python @@ -139,8 +170,21 @@ def fullJustify(words: List[str], maxWidth: int) -> List[str]: END EXAMPLES ''' -PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are a Python programming assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation." -PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = "You are a Python programming assistant. You will be given a function implementation and a series of unit test results. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as guidance when you try again later. Only provide the few sentence description in your answer, not the implementation. You will be given a few examples by the user." +PY_SELF_REFLECTION_CHAT_INSTRUCTION = ( + "You are a Python programming assistant. You will be given a function" + " implementation and a series of unit tests. Your goal is to write a few sentences" + " to explain why your implementation is wrong as indicated by the tests. You will" + " need this as a hint when you try again later. Only provide the few sentence" + " description in your answer, not the implementation." +) +PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = ( + "You are a Python programming assistant. You will be given a function" + " implementation and a series of unit test results. Your goal is to write a few" + " sentences to explain why your implementation is wrong as indicated by the tests." + " You will need this as guidance when you try again later. Only provide the few" + " sentence description in your answer, not the implementation. You will be given a" + " few examples by the user." +) PY_SELF_REFLECTION_FEW_SHOT = """Example 1: [function impl]: ```python diff --git a/swarms/prompts/sales.py b/swarms/prompts/sales.py index 42f8d4ea..6c945332 100644 --- a/swarms/prompts/sales.py +++ b/swarms/prompts/sales.py @@ -1,11 +1,38 @@ conversation_stages = { - "1": "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.", - "2": "Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.", - "3": "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.", - "4": "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.", - "5": "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.", - "6": "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.", - "7": "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.", + "1": ( + "Introduction: Start the conversation by introducing yourself and your company." + " Be polite and respectful while keeping the tone of the conversation" + " professional. Your greeting should be welcoming. Always clarify in your" + " greeting the reason why you are contacting the prospect." + ), + "2": ( + "Qualification: Qualify the prospect by confirming if they are the right person" + " to talk to regarding your product/service. Ensure that they have the" + " authority to make purchasing decisions." + ), + "3": ( + "Value proposition: Briefly explain how your product/service can benefit the" + " prospect. Focus on the unique selling points and value proposition of your" + " product/service that sets it apart from competitors." + ), + "4": ( + "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" + " pain points. Listen carefully to their responses and take notes." + ), + "5": ( + "Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": ( + "Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims." + ), + "7": ( + "Close: Ask for the sale by proposing a next step. This could be a demo, a" + " trial or a meeting with decision-makers. Ensure to summarize what has been" + " discussed and reiterate the benefits." + ), } diff --git a/swarms/prompts/sales_prompts.py b/swarms/prompts/sales_prompts.py index 806f0ad2..ec4ef168 100644 --- a/swarms/prompts/sales_prompts.py +++ b/swarms/prompts/sales_prompts.py @@ -46,11 +46,38 @@ Conversation history: """ conversation_stages = { - "1": "Introduction: Start the conversation by introducing yourself and your company. Be polite and respectful while keeping the tone of the conversation professional. Your greeting should be welcoming. Always clarify in your greeting the reason why you are contacting the prospect.", - "2": "Qualification: Qualify the prospect by confirming if they are the right person to talk to regarding your product/service. Ensure that they have the authority to make purchasing decisions.", - "3": "Value proposition: Briefly explain how your product/service can benefit the prospect. Focus on the unique selling points and value proposition of your product/service that sets it apart from competitors.", - "4": "Needs analysis: Ask open-ended questions to uncover the prospect's needs and pain points. Listen carefully to their responses and take notes.", - "5": "Solution presentation: Based on the prospect's needs, present your product/service as the solution that can address their pain points.", - "6": "Objection handling: Address any objections that the prospect may have regarding your product/service. Be prepared to provide evidence or testimonials to support your claims.", - "7": "Close: Ask for the sale by proposing a next step. This could be a demo, a trial or a meeting with decision-makers. Ensure to summarize what has been discussed and reiterate the benefits.", + "1": ( + "Introduction: Start the conversation by introducing yourself and your company." + " Be polite and respectful while keeping the tone of the conversation" + " professional. Your greeting should be welcoming. Always clarify in your" + " greeting the reason why you are contacting the prospect." + ), + "2": ( + "Qualification: Qualify the prospect by confirming if they are the right person" + " to talk to regarding your product/service. Ensure that they have the" + " authority to make purchasing decisions." + ), + "3": ( + "Value proposition: Briefly explain how your product/service can benefit the" + " prospect. Focus on the unique selling points and value proposition of your" + " product/service that sets it apart from competitors." + ), + "4": ( + "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" + " pain points. Listen carefully to their responses and take notes." + ), + "5": ( + "Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": ( + "Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims." + ), + "7": ( + "Close: Ask for the sale by proposing a next step. This could be a demo, a" + " trial or a meeting with decision-makers. Ensure to summarize what has been" + " discussed and reiterate the benefits." + ), } diff --git a/swarms/prompts/task_assignment_prompt.py b/swarms/prompts/task_assignment_prompt.py index 9589d3f5..9dc59fa4 100644 --- a/swarms/prompts/task_assignment_prompt.py +++ b/swarms/prompts/task_assignment_prompt.py @@ -1,10 +1,10 @@ def task_planner_prompt(objective): return f""" - You are a planner who is an expert at coming up with a todo list for a given objective. - useful for when you need to come up with todo lists. + You are a planner who is an expert at coming up with a todo list for a given objective. + useful for when you need to come up with todo lists. - - Input: an objective to create a todo list for. Output: a todo list for that objective. For the main objective + + Input: an objective to create a todo list for. Output: a todo list for that objective. For the main objective layout each import subtask that needs to be accomplished and provide all subtasks with a ranking system prioritizing the most important subtasks first that are likely to accomplish the main objective. Use the following ranking system: 0.0 -> 1.0, 1.0 being the most important subtask. diff --git a/swarms/schemas/typings.py b/swarms/schemas/typings.py index faa902b5..d281a870 100644 --- a/swarms/schemas/typings.py +++ b/swarms/schemas/typings.py @@ -20,7 +20,10 @@ class ChatbotError(Exception): def __init__(self, *args: object) -> None: if SUPPORT_ADD_NOTES: super().add_note( - "Please check that the input is correct, or you can resolve this issue by filing an issue", + ( + "Please check that the input is correct, or you can resolve this" + " issue by filing an issue" + ), ) super().add_note("Project URL: https://github.com/acheong08/ChatGPT") super().__init__(*args) diff --git a/swarms/structs/base.py b/swarms/structs/base.py index 4208ba39..559416f0 100644 --- a/swarms/structs/base.py +++ b/swarms/structs/base.py @@ -1,5 +1,5 @@ """ Base Structure for all Swarm Structures - + """ diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 4ba0ca4a..7be03036 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -8,7 +8,7 @@ TODO: - add async processing for run and batch run - add plan module - concurrent -- +- """ import json @@ -30,9 +30,9 @@ This will enable you to leave the autonomous loop. # Constants FLOW_SYSTEM_PROMPT = f""" You are an autonomous agent granted autonomy from a Flow structure. -Your role is to engage in multi-step conversations with your self or the user, -generate long-form content like blogs, screenplays, or SOPs, -and accomplish tasks. You can have internal dialogues with yourself or can interact with the user +Your role is to engage in multi-step conversations with your self or the user, +generate long-form content like blogs, screenplays, or SOPs, +and accomplish tasks. You can have internal dialogues with yourself or can interact with the user to aid in these complex tasks. Your responses should be coherent, contextually relevant, and tailored to the task at hand. @@ -239,7 +239,7 @@ class Flow: Dynamic Temperature: {self.dynamic_temperature} Autosave: {self.autosave} Saved State: {self.saved_state_path} - + ---------------------------------------- """, "green", @@ -259,7 +259,10 @@ class Flow: except Exception as error: print( colored( - "Error activating autonomous agent. Try optimizing your parameters...", + ( + "Error activating autonomous agent. Try optimizing your" + " parameters..." + ), "red", ) ) diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index a7e1cd63..8c7d9760 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -257,9 +257,9 @@ class SequentialWorkflow: Metadata: kwargs: {kwargs} - - - + + + """, "cyan", attrs=["bold", "underline"], @@ -348,7 +348,8 @@ class SequentialWorkflow: # Ensure that 'task' is provided in the kwargs if "task" not in task.kwargs: raise ValueError( - f"The 'task' argument is required for the Flow flow execution in '{task.description}'" + "The 'task' argument is required for the Flow flow" + f" execution in '{task.description}'" ) # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") @@ -376,7 +377,11 @@ class SequentialWorkflow: except Exception as e: print( colored( - f"Error initializing the Sequential workflow: {e} try optimizing your inputs like the flow class and task description", + ( + f"Error initializing the Sequential workflow: {e} try" + " optimizing your inputs like the flow class and task" + " description" + ), "red", attrs=["bold", "underline"], ) @@ -399,7 +404,8 @@ class SequentialWorkflow: # Ensure that 'task' is provided in the kwargs if "task" not in task.kwargs: raise ValueError( - f"The 'task' argument is required for the Flow flow execution in '{task.description}'" + "The 'task' argument is required for the Flow flow" + f" execution in '{task.description}'" ) # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") diff --git a/swarms/swarms/groupchat.py b/swarms/swarms/groupchat.py index 6bbe0898..dd3e36a2 100644 --- a/swarms/swarms/groupchat.py +++ b/swarms/swarms/groupchat.py @@ -54,7 +54,8 @@ class GroupChat: n_agents = len(self.agent_names) if n_agents < 3: logger.warning( - f"GroupChat is underpopulated with {n_agents} agents. Direct communication would be more efficient." + f"GroupChat is underpopulated with {n_agents} agents. Direct" + " communication would be more efficient." ) name = selector.generate_reply( @@ -63,7 +64,11 @@ class GroupChat: + [ { "role": "system", - "content": f"Read the above conversation. Then select the next most suitable role from {self.agent_names} to play. Only return the role.", + "content": ( + "Read the above conversation. Then select the next most" + f" suitable role from {self.agent_names} to play. Only" + " return the role." + ), } ] ) diff --git a/swarms/swarms/multi_agent_collab.py b/swarms/swarms/multi_agent_collab.py index 6413b662..9a5f27bc 100644 --- a/swarms/swarms/multi_agent_collab.py +++ b/swarms/swarms/multi_agent_collab.py @@ -6,7 +6,10 @@ from langchain.output_parsers import RegexParser # utils class BidOutputParser(RegexParser): def get_format_instructions(self) -> str: - return "Your response should be an integrater delimited by angled brackets like this: " + return ( + "Your response should be an integrater delimited by angled brackets like" + " this: " + ) bid_parser = BidOutputParser( diff --git a/swarms/swarms/orchestrate.py b/swarms/swarms/orchestrate.py index 09914485..f522911b 100644 --- a/swarms/swarms/orchestrate.py +++ b/swarms/swarms/orchestrate.py @@ -153,7 +153,8 @@ class Orchestrator: except Exception as error: logging.error( - f"Failed to process task {id(task)} by agent {id(agent)}. Error: {error}" + f"Failed to process task {id(task)} by agent {id(agent)}. Error:" + f" {error}" ) finally: with self.condition: diff --git a/swarms/tools/autogpt.py b/swarms/tools/autogpt.py index c2f56db6..cf5450e6 100644 --- a/swarms/tools/autogpt.py +++ b/swarms/tools/autogpt.py @@ -181,8 +181,8 @@ def VQAinference(self, inputs): answer = processor.decode(out[0], skip_special_tokens=True) logger.debug( - f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, " - f"Output Answer: {answer}" + f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" + f" Question: {question}, Output Answer: {answer}" ) return answer diff --git a/swarms/tools/mm_models.py b/swarms/tools/mm_models.py index e8da2e5c..58fe11e5 100644 --- a/swarms/tools/mm_models.py +++ b/swarms/tools/mm_models.py @@ -75,10 +75,12 @@ class ImageEditing: @tool( name="Remove Something From The Photo", - description="useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. ", + description=( + "useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. " + ), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",") @@ -86,10 +88,12 @@ class ImageEditing: @tool( name="Replace Something From The Photo", - description="useful when you want to replace an object from the object description or " - "location with another object from its description. " - "The input to this tool should be a comma separated string of three, " - "representing the image_path, the object to be replaced, the object to be replaced with ", + description=( + "useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with " + ), ) def inference_replace(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") @@ -108,8 +112,9 @@ class ImageEditing: updated_image.save(updated_image_path) logger.debug( - f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, " - f"Output Image: {updated_image_path}" + f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" + f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" + f" {updated_image_path}" ) return updated_image_path @@ -131,10 +136,12 @@ class InstructPix2Pix: @tool( name="Instruct Image Using Text", - description="useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. ", + description=( + "useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. " + ), ) def inference(self, inputs): """Change style of image.""" @@ -148,8 +155,8 @@ class InstructPix2Pix: image.save(updated_image_path) logger.debug( - f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, " - f"Output Image: {updated_image_path}" + f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" + f" {text}, Output Image: {updated_image_path}" ) return updated_image_path @@ -172,9 +179,12 @@ class Text2Image: @tool( name="Generate Image From User Input Text", - description="useful when you want to generate an image from a user input text and save it to a file. " - "like: generate an image of an object or something, or generate an image that includes some objects. " - "The input to this tool should be a string, representing the text used to generate image. ", + description=( + "useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. " + ), ) def inference(self, text): image_filename = os.path.join("image", str(uuid.uuid4())[0:8] + ".png") @@ -183,7 +193,8 @@ class Text2Image: image.save(image_filename) logger.debug( - f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}" + f"\nProcessed Text2Image, Input Text: {text}, Output Image:" + f" {image_filename}" ) return image_filename @@ -201,9 +212,12 @@ class VisualQuestionAnswering: @tool( name="Answer Question About The Image", - description="useful when you need an answer for a question based on an image. " - "like: what is the background color of the last image, how many cats in this figure, what is in this figure. " - "The input to this tool should be a comma separated string of two, representing the image_path and the question", + description=( + "useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" + ), ) def inference(self, inputs): image_path, question = inputs.split(",") @@ -215,8 +229,8 @@ class VisualQuestionAnswering: answer = self.processor.decode(out[0], skip_special_tokens=True) logger.debug( - f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, " - f"Output Answer: {answer}" + f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" + f" Question: {question}, Output Answer: {answer}" ) return answer @@ -250,7 +264,8 @@ class ImageCaptioning(BaseHandler): out = self.model.generate(**inputs) description = self.processor.decode(out[0], skip_special_tokens=True) print( - f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text: {description}" + f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text:" + f" {description}" ) return IMAGE_PROMPT.format(filename=filename, description=description) diff --git a/swarms/tools/tool.py b/swarms/tools/tool.py index 8f01ac0d..1b1072a5 100644 --- a/swarms/tools/tool.py +++ b/swarms/tools/tool.py @@ -121,10 +121,10 @@ class ChildTool(BaseTool): name = cls.__name__ raise SchemaAnnotationError( f"Tool definition for {name} must include valid type annotations" - f" for argument 'args_schema' to behave as expected.\n" - f"Expected annotation of 'Type[BaseModel]'" + " for argument 'args_schema' to behave as expected.\n" + "Expected annotation of 'Type[BaseModel]'" f" but got '{args_schema_type}'.\n" - f"Expected class looks like:\n" + "Expected class looks like:\n" f"{typehint_mandate}" ) @@ -353,7 +353,7 @@ class ChildTool(BaseTool): observation = self.handle_tool_error(e) else: raise ValueError( - f"Got unexpected type of `handle_tool_error`. Expected bool, str " + "Got unexpected type of `handle_tool_error`. Expected bool, str " f"or callable. Received: {self.handle_tool_error}" ) run_manager.on_tool_end( @@ -428,7 +428,7 @@ class ChildTool(BaseTool): observation = self.handle_tool_error(e) else: raise ValueError( - f"Got unexpected type of `handle_tool_error`. Expected bool, str " + "Got unexpected type of `handle_tool_error`. Expected bool, str " f"or callable. Received: {self.handle_tool_error}" ) await run_manager.on_tool_end( @@ -492,8 +492,7 @@ class Tool(BaseTool): all_args = list(args) + list(kwargs.values()) if len(all_args) != 1: raise ToolException( - f"Too many arguments to single-input tool {self.name}." - f" Args: {all_args}" + f"Too many arguments to single-input tool {self.name}. Args: {all_args}" ) return tuple(all_args), {} diff --git a/swarms/utils/code_interpreter.py b/swarms/utils/code_interpreter.py index 2448edc7..80eb6700 100644 --- a/swarms/utils/code_interpreter.py +++ b/swarms/utils/code_interpreter.py @@ -98,7 +98,7 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): code = self.preprocess_code(code) if not self.process: self.start_process() - except: + except BaseException: yield {"output": traceback.format_exc()} return @@ -112,7 +112,7 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): self.process.stdin.write(code + "\n") self.process.stdin.flush() break - except: + except BaseException: if retry_count != 0: # For UX, I like to hide this if it happens once. Obviously feels better to not see errors # Most of the time it doesn't matter, but we should figure out why it happens frequently with: diff --git a/swarms/utils/main.py b/swarms/utils/main.py index 3fa4b2ea..9c1342aa 100644 --- a/swarms/utils/main.py +++ b/swarms/utils/main.py @@ -383,7 +383,7 @@ class FileHandler: if FileType.from_url(url) == FileType.IMAGE: raise Exception( f"No handler for {FileType.from_url(url)}. " - f"Please set USE_GPU to True in env/settings.py" + "Please set USE_GPU to True in env/settings.py" ) else: raise Exception(f"No handler for {FileType.from_url(url)}") @@ -408,7 +408,8 @@ class CsvToDataframe(BaseHandler): ) print( - f"\nProcessed CsvToDataframe, Input CSV: {filename}, Output Description: {description}" + f"\nProcessed CsvToDataframe, Input CSV: {filename}, Output Description:" + f" {description}" ) return DATAFRAME_PROMPT.format(filename=filename, description=description) diff --git a/swarms/workers/worker.py b/swarms/workers/worker.py index be422ff2..9986666a 100644 --- a/swarms/workers/worker.py +++ b/swarms/workers/worker.py @@ -163,7 +163,8 @@ class Worker: except Exception as error: raise RuntimeError( - f"Error setting up memory perhaps try try tuning the embedding size: {error}" + "Error setting up memory perhaps try try tuning the embedding size:" + f" {error}" ) def setup_agent(self): From cf70e35f694880a5aea26e2d3c4ca7a98f56107a Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 17:44:31 -0500 Subject: [PATCH 59/63] yapf code quality Former-commit-id: 2e7905db461fe5116023aa34a4b5affdd3a6cbf9 --- quality.sh | 5 +- swarms/agents/__init__.py | 2 - swarms/agents/agent.py | 121 ++- swarms/agents/aot.py | 54 +- swarms/agents/browser_agent.py | 91 +- swarms/agents/hf_agents.py | 115 ++- swarms/agents/meta_prompter.py | 14 +- swarms/agents/multi_modal_visual_agent.py | 950 +++++++++--------- .../neural_architecture_search_worker.py | 1 + swarms/agents/omni_modal_agent.py | 32 +- swarms/agents/profitpilot.py | 105 +- swarms/agents/refiner_agent.py | 4 +- swarms/agents/registry.py | 4 +- swarms/agents/simple_agent.py | 3 +- swarms/artifacts/base.py | 8 +- swarms/artifacts/main.py | 15 +- swarms/chunkers/__init__.py | 1 - swarms/chunkers/base.py | 39 +- swarms/chunkers/omni_chunker.py | 8 +- swarms/loaders/asana.py | 80 +- swarms/loaders/base.py | 128 +-- swarms/memory/base.py | 113 +-- swarms/memory/chroma.py | 90 +- swarms/memory/cosine_similarity.py | 6 +- swarms/memory/db.py | 23 +- swarms/memory/ocean.py | 9 +- swarms/memory/pg.py | 59 +- swarms/memory/pinecone.py | 53 +- swarms/memory/schemas.py | 34 +- swarms/memory/utils.py | 9 +- swarms/models/__init__.py | 2 - swarms/models/anthropic.py | 97 +- swarms/models/bioclip.py | 31 +- swarms/models/biogpt.py | 18 +- swarms/models/dalle3.py | 24 +- swarms/models/distilled_whisperx.py | 32 +- swarms/models/fastvit.py | 25 +- swarms/models/fuyu.py | 18 +- swarms/models/gpt4v.py | 106 +- swarms/models/huggingface.py | 88 +- swarms/models/idefics.py | 54 +- swarms/models/jina_embeds.py | 25 +- swarms/models/kosmos2.py | 48 +- swarms/models/kosmos_two.py | 80 +- swarms/models/llava.py | 5 +- swarms/models/mistral.py | 9 +- swarms/models/mpt.py | 18 +- swarms/models/nougat.py | 12 +- swarms/models/openai_assistant.py | 11 +- swarms/models/openai_embeddings.py | 128 +-- swarms/models/openai_models.py | 302 +++--- swarms/models/openai_tokenizer.py | 36 +- swarms/models/palm.py | 35 +- swarms/models/pegasus.py | 10 +- swarms/models/simple_ada.py | 4 +- swarms/models/speecht5.py | 15 +- swarms/models/timm.py | 5 +- swarms/models/trocr.py | 5 +- swarms/models/vilt.py | 6 +- swarms/models/wizard_storytelling.py | 76 +- swarms/models/yarn_mistral.py | 70 +- swarms/models/zephyr.py | 5 +- swarms/prompts/agent_output_parser.py | 5 +- swarms/prompts/agent_prompt.py | 25 +- swarms/prompts/agent_prompts.py | 30 +- swarms/prompts/base.py | 26 +- swarms/prompts/chat_prompt.py | 13 +- swarms/prompts/debate.py | 5 +- swarms/prompts/multi_modal_prompts.py | 5 +- swarms/prompts/python.py | 24 +- swarms/prompts/sales.py | 32 +- swarms/prompts/sales_prompts.py | 32 +- swarms/prompts/summaries_prompts.py | 4 - swarms/schemas/typings.py | 11 +- swarms/structs/document.py | 13 +- swarms/structs/flow.py | 46 +- swarms/structs/nonlinear_workflow.py | 13 +- swarms/structs/sequential_workflow.py | 81 +- swarms/structs/task.py | 18 +- swarms/structs/workflow.py | 12 +- swarms/swarms/autoscaler.py | 3 +- swarms/swarms/base.py | 4 +- swarms/swarms/battle_royal.py | 10 +- swarms/swarms/god_mode.py | 26 +- swarms/swarms/groupchat.py | 46 +- swarms/swarms/multi_agent_collab.py | 11 +- swarms/swarms/multi_agent_debate.py | 3 +- swarms/swarms/orchestrate.py | 27 +- swarms/swarms/simple_swarm.py | 1 + swarms/tools/autogpt.py | 42 +- swarms/tools/mm_models.py | 141 ++- swarms/tools/stt.py | 17 +- swarms/tools/tool.py | 223 ++-- swarms/tools/tool_registry.py | 5 +- swarms/utils/code_interpreter.py | 12 +- swarms/utils/decorators.py | 14 +- swarms/utils/futures.py | 4 +- swarms/utils/hash.py | 3 +- swarms/utils/main.py | 79 +- swarms/utils/parse_code.py | 3 +- swarms/utils/revutils.py | 38 +- swarms/utils/serializable.py | 17 +- swarms/utils/static.py | 1 + swarms/workers/worker.py | 20 +- 104 files changed, 2256 insertions(+), 2465 deletions(-) diff --git a/quality.sh b/quality.sh index bf167079..032085ca 100644 --- a/quality.sh +++ b/quality.sh @@ -5,7 +5,7 @@ # Run autopep8 with max aggressiveness (-aaa) and in-place modification (-i) # on all Python files (*.py) under the 'swarms' directory. -autopep8 --in-place --aggressive --aggressive --recursive --experimental swarms/ +autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes swarms/ # Run black with default settings, since black does not have an aggressiveness level. # Black will format all Python files it finds in the 'swarms' directory. @@ -15,4 +15,5 @@ black --experimental-string-processing swarms/ # Add any additional flags if needed according to your version of ruff. ruff swarms/ -# If you want to ensure the script stops if any command fails, add 'set -e' at the top. +# YAPF +yapf --recursive --in-place --verbose --style=google --parallel swarms diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index 355f0ad1..cd3aa221 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -8,8 +8,6 @@ from swarms.agents.registry import Registry # from swarms.agents.idea_to_image_agent import Idea2Image from swarms.agents.simple_agent import SimpleAgent - - """Agent Infrastructure, models, memory, utils, tools""" __all__ = [ diff --git a/swarms/agents/agent.py b/swarms/agents/agent.py index 34d6315f..c16dd780 100644 --- a/swarms/agents/agent.py +++ b/swarms/agents/agent.py @@ -8,8 +8,7 @@ from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.memory import ChatMessageHistory from langchain.prompts.chat import ( - BaseChatPromptTemplate, -) + BaseChatPromptTemplate,) from langchain.schema import ( BaseChatMessageHistory, Document, @@ -34,7 +33,6 @@ from langchain_experimental.autonomous_agents.autogpt.prompt_generator import ( ) from langchain_experimental.pydantic_v1 import BaseModel, ValidationError - # PROMPT FINISH_NAME = "finish" @@ -72,14 +70,12 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] send_token_limit: int = 4196 def construct_full_prompt(self, goals: List[str]) -> str: - prompt_start = ( - "Your decisions must always be made independently " - "without seeking user assistance.\n" - "Play to your strengths as an LLM and pursue simple " - "strategies with no legal complications.\n" - "If you have completed all your tasks, make sure to " - 'use the "finish" command.' - ) + prompt_start = ("Your decisions must always be made independently " + "without seeking user assistance.\n" + "Play to your strengths as an LLM and pursue simple " + "strategies with no legal complications.\n" + "If you have completed all your tasks, make sure to " + 'use the "finish" command.') # Construct full prompt full_prompt = ( f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n" @@ -91,25 +87,23 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] return full_prompt def format_messages(self, **kwargs: Any) -> List[BaseMessage]: - base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"])) + base_prompt = SystemMessage( + content=self.construct_full_prompt(kwargs["goals"])) time_prompt = SystemMessage( - content=f"The current time and date is {time.strftime('%c')}" - ) - used_tokens = self.token_counter(base_prompt.content) + self.token_counter( - time_prompt.content - ) + content=f"The current time and date is {time.strftime('%c')}") + used_tokens = self.token_counter( + base_prompt.content) + self.token_counter(time_prompt.content) memory: VectorStoreRetriever = kwargs["memory"] previous_messages = kwargs["messages"] - relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:])) + relevant_docs = memory.get_relevant_documents( + str(previous_messages[-10:])) relevant_memory = [d.page_content for d in relevant_docs] relevant_memory_tokens = sum( - [self.token_counter(doc) for doc in relevant_memory] - ) + [self.token_counter(doc) for doc in relevant_memory]) while used_tokens + relevant_memory_tokens > 2500: relevant_memory = relevant_memory[:-1] relevant_memory_tokens = sum( - [self.token_counter(doc) for doc in relevant_memory] - ) + [self.token_counter(doc) for doc in relevant_memory]) content_format = ( f"This reminds you of these events from your past:\n{relevant_memory}\n\n" ) @@ -147,13 +141,23 @@ class PromptGenerator: self.performance_evaluation: List[str] = [] self.response_format = { "thoughts": { - "text": "thought", - "reasoning": "reasoning", - "plan": "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": "constructive self-criticism", - "speak": "thoughts summary to say to user", + "text": + "thought", + "reasoning": + "reasoning", + "plan": + "- short bulleted\n- list that conveys\n- long-term plan", + "criticism": + "constructive self-criticism", + "speak": + "thoughts summary to say to user", + }, + "command": { + "name": "command name", + "args": { + "arg name": "value" + } }, - "command": {"name": "command name", "args": {"arg name": "value"}}, } def add_constraint(self, constraint: str) -> None: @@ -191,7 +195,9 @@ class PromptGenerator: """ self.performance_evaluation.append(evaluation) - def _generate_numbered_list(self, items: list, item_type: str = "list") -> str: + def _generate_numbered_list(self, + items: list, + item_type: str = "list") -> str: """ Generate a numbered list from given items based on the item_type. @@ -209,16 +215,11 @@ class PromptGenerator: for i, item in enumerate(items) ] finish_description = ( - "use this to signal that you have finished all your objectives" - ) - finish_args = ( - '"response": "final response to let ' - 'people know you have finished your objectives"' - ) - finish_string = ( - f"{len(items) + 1}. {FINISH_NAME}: " - f"{finish_description}, args: {finish_args}" - ) + "use this to signal that you have finished all your objectives") + finish_args = ('"response": "final response to let ' + 'people know you have finished your objectives"') + finish_string = (f"{len(items) + 1}. {FINISH_NAME}: " + f"{finish_description}, args: {finish_args}") return "\n".join(command_strings + [finish_string]) else: return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items)) @@ -239,8 +240,7 @@ class PromptGenerator: f"{self._generate_numbered_list(self.performance_evaluation)}\n\n" "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - "\nEnsure the response can be parsed by Python json.loads" - ) + "\nEnsure the response can be parsed by Python json.loads") return prompt_string @@ -261,13 +261,11 @@ def get_prompt(tools: List[BaseTool]) -> str: prompt_generator.add_constraint( "~16000 word limit for short term memory. " "Your short term memory is short, " - "so immediately save important information to files." - ) + "so immediately save important information to files.") prompt_generator.add_constraint( "If you are unsure how you previously did something " "or want to recall past events, " - "thinking about similar events will help you remember." - ) + "thinking about similar events will help you remember.") prompt_generator.add_constraint("No user assistance") prompt_generator.add_constraint( 'Exclusively use the commands listed in double quotes e.g. "command name"' @@ -279,29 +277,23 @@ def get_prompt(tools: List[BaseTool]) -> str: # Add resources to the PromptGenerator object prompt_generator.add_resource( - "Internet access for searches and information gathering." - ) + "Internet access for searches and information gathering.") prompt_generator.add_resource("Long Term memory management.") prompt_generator.add_resource( - "GPT-3.5 powered Agents for delegation of simple tasks." - ) + "GPT-3.5 powered Agents for delegation of simple tasks.") prompt_generator.add_resource("File output.") # Add performance evaluations to the PromptGenerator object prompt_generator.add_performance_evaluation( "Continuously review and analyze your actions " - "to ensure you are performing to the best of your abilities." - ) + "to ensure you are performing to the best of your abilities.") prompt_generator.add_performance_evaluation( - "Constructively self-criticize your big-picture behavior constantly." - ) + "Constructively self-criticize your big-picture behavior constantly.") prompt_generator.add_performance_evaluation( - "Reflect on past decisions and strategies to refine your approach." - ) + "Reflect on past decisions and strategies to refine your approach.") prompt_generator.add_performance_evaluation( "Every command has a cost, so be smart and efficient. " - "Aim to complete tasks in the least number of steps." - ) + "Aim to complete tasks in the least number of steps.") # Generate the prompt string prompt_string = prompt_generator.generate_prompt_string() @@ -372,10 +364,8 @@ class AutoGPT: ) def run(self, goals: List[str]) -> str: - user_input = ( - "Determine which next command to use, " - "and respond using the format specified above:" - ) + user_input = ("Determine which next command to use, " + "and respond using the format specified above:") # Interaction Loop loop_count = 0 while True: @@ -392,8 +382,10 @@ class AutoGPT: # Print Assistant thoughts print(assistant_reply) - self.chat_history_memory.add_message(HumanMessage(content=user_input)) - self.chat_history_memory.add_message(AIMessage(content=assistant_reply)) + self.chat_history_memory.add_message( + HumanMessage(content=user_input)) + self.chat_history_memory.add_message( + AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) @@ -419,8 +411,7 @@ class AutoGPT: result = ( f"Unknown command '{action.name}'. " "Please refer to the 'COMMANDS' list for available " - "commands and only respond in the specified JSON format." - ) + "commands and only respond in the specified JSON format.") memory_to_add = f"Assistant Reply: {assistant_reply} \nResult: {result} " if self.feedback_tool is not None: diff --git a/swarms/agents/aot.py b/swarms/agents/aot.py index b36fb43c..123f5591 100644 --- a/swarms/agents/aot.py +++ b/swarms/agents/aot.py @@ -4,13 +4,13 @@ import time import openai_model -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) +logging.basicConfig(level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) class OpenAI: + def __init__( self, api_key, @@ -68,16 +68,13 @@ class OpenAI: temperature=temperature, ) with open("openai.logs", "a") as log_file: - log_file.write( - "\n" + "-----------" + "\n" + "Prompt : " + prompt + "\n" - ) + log_file.write("\n" + "-----------" + "\n" + "Prompt : " + + prompt + "\n") return response except openai_model.error.RateLimitError as e: sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30) - print( - f"{str(e)}, sleep for {sleep_duratoin}s, set it by env" - " OPENAI_RATE_TIMEOUT" - ) + print(f"{str(e)}, sleep for {sleep_duratoin}s, set it by env" + " OPENAI_RATE_TIMEOUT") time.sleep(sleep_duratoin) def openai_choice2text_handler(self, choice): @@ -100,11 +97,16 @@ class OpenAI: else: response = self.run(prompt, 300, 0.5, k) thoughts = [ - self.openai_choice2text_handler(choice) for choice in response.choices + self.openai_choice2text_handler(choice) + for choice in response.choices ] return thoughts - def generate_thoughts(self, state, k, initial_prompt, rejected_solutions=None): + def generate_thoughts(self, + state, + k, + initial_prompt, + rejected_solutions=None): if isinstance(state, str): pass else: @@ -177,7 +179,8 @@ class OpenAI: """ response = self.run(prompt, 10, 1) try: - value_text = self.openai_choice2text_handler(response.choices[0]) + value_text = self.openai_choice2text_handler( + response.choices[0]) # print(f'state: {value_text}') value = float(value_text) print(f"Evaluated Thought Value: {value}") @@ -187,10 +190,12 @@ class OpenAI: return state_values else: - raise ValueError("Invalid evaluation strategy. Choose 'value' or 'vote'.") + raise ValueError( + "Invalid evaluation strategy. Choose 'value' or 'vote'.") class AoTAgent: + def __init__( self, num_thoughts: int = None, @@ -222,7 +227,8 @@ class AoTAgent: return None best_state, _ = max(self.output, key=lambda x: x[1]) - solution = self.model.generate_solution(self.initial_prompt, best_state) + solution = self.model.generate_solution(self.initial_prompt, + best_state) print(f"Solution is {solution}") return solution if solution else best_state except Exception as error: @@ -239,11 +245,8 @@ class AoTAgent: for next_state in thoughts: state_value = self.evaluated_thoughts[next_state] if state_value > self.value_threshold: - child = ( - (state, next_state) - if isinstance(state, str) - else (*state, next_state) - ) + child = ((state, next_state) if isinstance(state, str) else + (*state, next_state)) self.dfs(child, step + 1) # backtracking @@ -253,17 +256,14 @@ class AoTAgent: continue def generate_and_filter_thoughts(self, state): - thoughts = self.model.generate_thoughts( - state, self.num_thoughts, self.initial_prompt - ) + thoughts = self.model.generate_thoughts(state, self.num_thoughts, + self.initial_prompt) self.evaluated_thoughts = self.model.evaluate_states( - thoughts, self.initial_prompt - ) + thoughts, self.initial_prompt) filtered_thoughts = [ - thought - for thought in thoughts + thought for thought in thoughts if self.evaluated_thoughts[thought] >= self.pruning_threshold ] print(f"filtered_thoughts: {filtered_thoughts}") diff --git a/swarms/agents/browser_agent.py b/swarms/agents/browser_agent.py index 1f4ff12e..3a274468 100644 --- a/swarms/agents/browser_agent.py +++ b/swarms/agents/browser_agent.py @@ -38,7 +38,8 @@ def record(agent_name: str, autotab_ext_path: Optional[str] = None): if not os.path.exists("agents"): os.makedirs("agents") - if os.path.exists(f"agents/{agent_name}.py") and config.environment != "local": + if os.path.exists( + f"agents/{agent_name}.py") and config.environment != "local": if not _is_blank_agent(agent_name=agent_name): raise Exception(f"Agent with name {agent_name} already exists") driver = get_driver( # noqa: F841 @@ -54,12 +55,10 @@ def record(agent_name: str, autotab_ext_path: Optional[str] = None): print( "\033[34mYou have the Python debugger open, you can run commands in it like you" - " would in a normal Python shell.\033[0m" - ) + " would in a normal Python shell.\033[0m") print( "\033[34mTo exit, type 'q' and press enter. For a list of commands type '?' and" - " press enter.\033[0m" - ) + " press enter.\033[0m") breakpoint() @@ -79,12 +78,13 @@ def extract_domain_from_url(url: str): class AutotabChromeDriver(uc.Chrome): + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - def find_element_with_retry( - self, by=By.ID, value: Optional[str] = None - ) -> WebElement: + def find_element_with_retry(self, + by=By.ID, + value: Optional[str] = None) -> WebElement: try: return super().find_element(by, value) except Exception as e: @@ -102,11 +102,8 @@ def open_plugin(driver: AutotabChromeDriver): def open_plugin_and_login(driver: AutotabChromeDriver): if config.autotab_api_key is not None: - backend_url = ( - "http://localhost:8000" - if config.environment == "local" - else "https://api.autotab.com" - ) + backend_url = ("http://localhost:8000" if config.environment == "local" + else "https://api.autotab.com") driver.get(f"{backend_url}/auth/signin-api-key-page") response = requests.post( f"{backend_url}/auth/signin-api-key", @@ -119,8 +116,7 @@ def open_plugin_and_login(driver: AutotabChromeDriver): else: raise Exception( f"Error {response.status_code} from backend while logging you in" - f" with your API key: {response.text}" - ) + f" with your API key: {response.text}") cookie["name"] = cookie["key"] del cookie["key"] driver.add_cookie(cookie) @@ -130,26 +126,21 @@ def open_plugin_and_login(driver: AutotabChromeDriver): else: print("No autotab API key found, heading to autotab.com to sign up") - url = ( - "http://localhost:3000/dashboard" - if config.environment == "local" - else "https://autotab.com/dashboard" - ) + url = ("http://localhost:3000/dashboard" if config.environment + == "local" else "https://autotab.com/dashboard") driver.get(url) time.sleep(0.5) open_plugin(driver) -def get_driver( - autotab_ext_path: Optional[str] = None, record_mode: bool = False -) -> AutotabChromeDriver: +def get_driver(autotab_ext_path: Optional[str] = None, + record_mode: bool = False) -> AutotabChromeDriver: options = webdriver.ChromeOptions() options.add_argument("--no-sandbox") # Necessary for running options.add_argument( "--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" - " (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36" - ) + " (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36") options.add_argument("--enable-webgl") options.add_argument("--enable-3d-apis") options.add_argument("--enable-clipboard-read-write") @@ -238,7 +229,8 @@ class Config(BaseModel): return cls( autotab_api_key=autotab_api_key, credentials=_credentials, - google_credentials=GoogleCredentials(credentials=google_credentials), + google_credentials=GoogleCredentials( + credentials=google_credentials), chrome_binary_location=config.get("chrome_binary_location"), environment=config.get("environment", "prod"), ) @@ -256,9 +248,9 @@ def is_signed_in_to_google(driver): return len([c for c in cookies if c["name"] == "SAPISID"]) != 0 -def google_login( - driver, credentials: Optional[SiteCredentials] = None, navigate: bool = True -): +def google_login(driver, + credentials: Optional[SiteCredentials] = None, + navigate: bool = True): print("Logging in to Google") if navigate: driver.get("https://accounts.google.com/") @@ -290,8 +282,7 @@ def google_login( email_input.send_keys(credentials.email) email_input.send_keys(Keys.ENTER) WebDriverWait(driver, 10).until( - EC.element_to_be_clickable((By.CSS_SELECTOR, "[type='password']")) - ) + EC.element_to_be_clickable((By.CSS_SELECTOR, "[type='password']"))) password_input = driver.find_element(By.CSS_SELECTOR, "[type='password']") password_input.send_keys(credentials.password) @@ -314,21 +305,20 @@ def google_login( cookies = driver.get_cookies() cookie_names = ["__Host-GAPS", "SMSV", "NID", "ACCOUNT_CHOOSER"] google_cookies = [ - cookie - for cookie in cookies - if cookie["domain"] in [".google.com", "accounts.google.com"] - and cookie["name"] in cookie_names + cookie for cookie in cookies + if cookie["domain"] in [".google.com", "accounts.google.com"] and + cookie["name"] in cookie_names ] with open("google_cookies.json", "w") as f: json.dump(google_cookies, f) # Log back in login_button = driver.find_element( - By.CSS_SELECTOR, f"[data-identifier='{credentials.email}']" - ) + By.CSS_SELECTOR, f"[data-identifier='{credentials.email}']") login_button.click() time.sleep(1) - password_input = driver.find_element(By.CSS_SELECTOR, "[type='password']") + password_input = driver.find_element(By.CSS_SELECTOR, + "[type='password']") password_input.send_keys(credentials.password) password_input.send_keys(Keys.ENTER) @@ -343,8 +333,7 @@ def login(driver, url: str): login_url = credentials.login_url if credentials.login_with_google_account: google_credentials = config.google_credentials.credentials[ - credentials.login_with_google_account - ] + credentials.login_with_google_account] _login_with_google(driver, login_url, google_credentials) else: _login(driver, login_url, credentials=credentials) @@ -371,16 +360,15 @@ def _login_with_google(driver, url: str, google_credentials: SiteCredentials): driver.get(url) WebDriverWait(driver, 10).until( - EC.presence_of_element_located((By.TAG_NAME, "body")) - ) + EC.presence_of_element_located((By.TAG_NAME, "body"))) main_window = driver.current_window_handle xpath = ( "//*[contains(text(), 'Continue with Google') or contains(text(), 'Sign in with" - " Google') or contains(@title, 'Sign in with Google')]" - ) + " Google') or contains(@title, 'Sign in with Google')]") - WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.XPATH, xpath))) + WebDriverWait(driver, + 10).until(EC.presence_of_element_located((By.XPATH, xpath))) driver.find_element( By.XPATH, xpath, @@ -388,8 +376,8 @@ def _login_with_google(driver, url: str, google_credentials: SiteCredentials): driver.switch_to.window(driver.window_handles[-1]) driver.find_element( - By.XPATH, f"//*[contains(text(), '{google_credentials.email}')]" - ).click() + By.XPATH, + f"//*[contains(text(), '{google_credentials.email}')]").click() driver.switch_to.window(main_window) @@ -442,8 +430,11 @@ def should_update(): # Parse the XML file root = ET.fromstring(xml_content) - namespaces = {"ns": "http://www.google.com/update2/response"} # add namespaces - xml_version = root.find(".//ns:app/ns:updatecheck", namespaces).get("version") + namespaces = { + "ns": "http://www.google.com/update2/response" + } # add namespaces + xml_version = root.find(".//ns:app/ns:updatecheck", + namespaces).get("version") # Load the local JSON file with open("src/extension/autotab/manifest.json", "r") as f: @@ -484,8 +475,6 @@ def play(agent_name: Optional[str] = None): if __name__ == "__main__": play() - - """ diff --git a/swarms/agents/hf_agents.py b/swarms/agents/hf_agents.py index 7614b1aa..e13d3462 100644 --- a/swarms/agents/hf_agents.py +++ b/swarms/agents/hf_agents.py @@ -19,7 +19,6 @@ from transformers.utils import is_offline_mode, is_openai_available, logging # utils logger = logging.get_logger(__name__) - if is_openai_available(): import openai @@ -28,7 +27,6 @@ else: _tools_are_initialized = False - BASE_PYTHON_TOOLS = { "print": print, "range": range, @@ -48,7 +46,6 @@ class PreTool: HUGGINGFACE_DEFAULT_TOOLS = {} - HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB = [ "image-transformation", "text-download", @@ -59,23 +56,24 @@ HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB = [ def get_remote_tools(organization="huggingface-tools"): if is_offline_mode(): - logger.info("You are in offline mode, so remote tools are not available.") + logger.info( + "You are in offline mode, so remote tools are not available.") return {} spaces = list_spaces(author=organization) tools = {} for space_info in spaces: repo_id = space_info.id - resolved_config_file = hf_hub_download( - repo_id, TOOL_CONFIG_FILE, repo_type="space" - ) + resolved_config_file = hf_hub_download(repo_id, + TOOL_CONFIG_FILE, + repo_type="space") with open(resolved_config_file, encoding="utf-8") as reader: config = json.load(reader) task = repo_id.split("/")[-1] - tools[config["name"]] = PreTool( - task=task, description=config["description"], repo_id=repo_id - ) + tools[config["name"]] = PreTool(task=task, + description=config["description"], + repo_id=repo_id) return tools @@ -95,8 +93,7 @@ def _setup_default_tools(): tool_class = getattr(tools_module, tool_class_name) description = tool_class.description HUGGINGFACE_DEFAULT_TOOLS[tool_class.name] = PreTool( - task=task_name, description=description, repo_id=None - ) + task=task_name, description=description, repo_id=None) if not is_offline_mode(): for task_name in HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB: @@ -200,18 +197,19 @@ class Agent: one of the default tools, that default tool will be overridden. """ - def __init__( - self, chat_prompt_template=None, run_prompt_template=None, additional_tools=None - ): + def __init__(self, + chat_prompt_template=None, + run_prompt_template=None, + additional_tools=None): _setup_default_tools() agent_name = self.__class__.__name__ - self.chat_prompt_template = download_prompt( - chat_prompt_template, agent_name, mode="chat" - ) - self.run_prompt_template = download_prompt( - run_prompt_template, agent_name, mode="run" - ) + self.chat_prompt_template = download_prompt(chat_prompt_template, + agent_name, + mode="chat") + self.run_prompt_template = download_prompt(run_prompt_template, + agent_name, + mode="run") self._toolbox = HUGGINGFACE_DEFAULT_TOOLS.copy() self.log = print if additional_tools is not None: @@ -227,17 +225,16 @@ class Agent: } self._toolbox.update(additional_tools) if len(replacements) > 1: - names = "\n".join([f"- {n}: {t}" for n, t in replacements.items()]) + names = "\n".join( + [f"- {n}: {t}" for n, t in replacements.items()]) logger.warning( "The following tools have been replaced by the ones provided in" - f" `additional_tools`:\n{names}." - ) + f" `additional_tools`:\n{names}.") elif len(replacements) == 1: name = list(replacements.keys())[0] logger.warning( f"{name} has been replaced by {replacements[name]} as provided in" - " `additional_tools`." - ) + " `additional_tools`.") self.prepare_for_new_chat() @@ -247,17 +244,20 @@ class Agent: return self._toolbox def format_prompt(self, task, chat_mode=False): - description = "\n".join( - [f"- {name}: {tool.description}" for name, tool in self.toolbox.items()] - ) + description = "\n".join([ + f"- {name}: {tool.description}" + for name, tool in self.toolbox.items() + ]) if chat_mode: if self.chat_history is None: - prompt = self.chat_prompt_template.replace("<>", description) + prompt = self.chat_prompt_template.replace( + "<>", description) else: prompt = self.chat_history prompt += CHAT_MESSAGE_PROMPT.replace("<>", task) else: - prompt = self.run_prompt_template.replace("<>", description) + prompt = self.run_prompt_template.replace("<>", + description) prompt = prompt.replace("<>", task) return prompt @@ -306,14 +306,19 @@ class Agent: if not return_code: self.log("\n\n==Result==") self.cached_tools = resolve_tools( - code, self.toolbox, remote=remote, cached_tools=self.cached_tools - ) + code, + self.toolbox, + remote=remote, + cached_tools=self.cached_tools) self.chat_state.update(kwargs) - return evaluate( - code, self.cached_tools, self.chat_state, chat_mode=True - ) + return evaluate(code, + self.cached_tools, + self.chat_state, + chat_mode=True) else: - tool_code = get_tool_creation_code(code, self.toolbox, remote=remote) + tool_code = get_tool_creation_code(code, + self.toolbox, + remote=remote) return f"{tool_code}\n{code}" def prepare_for_new_chat(self): @@ -355,12 +360,15 @@ class Agent: self.log(f"\n\n==Code generated by the agent==\n{code}") if not return_code: self.log("\n\n==Result==") - self.cached_tools = resolve_tools( - code, self.toolbox, remote=remote, cached_tools=self.cached_tools - ) + self.cached_tools = resolve_tools(code, + self.toolbox, + remote=remote, + cached_tools=self.cached_tools) return evaluate(code, self.cached_tools, state=kwargs.copy()) else: - tool_code = get_tool_creation_code(code, self.toolbox, remote=remote) + tool_code = get_tool_creation_code(code, + self.toolbox, + remote=remote) return f"{tool_code}\n{code}" def generate_one(self, prompt, stop): @@ -420,8 +428,7 @@ class HFAgent(Agent): ): if not is_openai_available(): raise ImportError( - "Using `OpenAiAgent` requires `openai`: `pip install openai`." - ) + "Using `OpenAiAgent` requires `openai`: `pip install openai`.") if api_key is None: api_key = os.environ.get("OPENAI_API_KEY", None) @@ -429,8 +436,7 @@ class HFAgent(Agent): raise ValueError( "You need an openai key to use `OpenAIAgent`. You can get one here: Get" " one here https://openai.com/api/`. If you have one, set it in your" - " env with `os.environ['OPENAI_API_KEY'] = xxx." - ) + " env with `os.environ['OPENAI_API_KEY'] = xxx.") else: openai.api_key = api_key self.model = model @@ -455,7 +461,10 @@ class HFAgent(Agent): def _chat_generate(self, prompt, stop): result = openai.ChatCompletion.create( model=self.model, - messages=[{"role": "user", "content": prompt}], + messages=[{ + "role": "user", + "content": prompt + }], temperature=0, stop=stop, ) @@ -533,8 +542,7 @@ class AzureOpenAI(Agent): ): if not is_openai_available(): raise ImportError( - "Using `OpenAiAgent` requires `openai`: `pip install openai`." - ) + "Using `OpenAiAgent` requires `openai`: `pip install openai`.") self.deployment_id = deployment_id openai.api_type = "azure" @@ -544,8 +552,7 @@ class AzureOpenAI(Agent): raise ValueError( "You need an Azure openAI key to use `AzureOpenAIAgent`. If you have" " one, set it in your env with `os.environ['AZURE_OPENAI_API_KEY'] =" - " xxx." - ) + " xxx.") else: openai.api_key = api_key if resource_name is None: @@ -554,8 +561,7 @@ class AzureOpenAI(Agent): raise ValueError( "You need a resource_name to use `AzureOpenAIAgent`. If you have one," " set it in your env with `os.environ['AZURE_OPENAI_RESOURCE_NAME'] =" - " xxx." - ) + " xxx.") else: openai.api_base = f"https://{resource_name}.openai.azure.com" openai.api_version = api_version @@ -585,7 +591,10 @@ class AzureOpenAI(Agent): def _chat_generate(self, prompt, stop): result = openai.ChatCompletion.create( engine=self.deployment_id, - messages=[{"role": "user", "content": prompt}], + messages=[{ + "role": "user", + "content": prompt + }], temperature=0, stop=stop, ) diff --git a/swarms/agents/meta_prompter.py b/swarms/agents/meta_prompter.py index aeee9878..f744e38e 100644 --- a/swarms/agents/meta_prompter.py +++ b/swarms/agents/meta_prompter.py @@ -88,9 +88,8 @@ class MetaPrompterAgent: Assistant: """ - prompt = PromptTemplate( - input_variables=["history", "human_input"], template=template - ) + prompt = PromptTemplate(input_variables=["history", "human_input"], + template=template) self.chain = LLMChain( llm=self.llm(), @@ -102,13 +101,15 @@ class MetaPrompterAgent: def get_chat_history(self, chain_memory): """Get Chat History from the memory""" memory_key = chain_memory.memory_key - chat_history = chain_memory.load_memory_variables(memory_key)[memory_key] + chat_history = chain_memory.load_memory_variables( + memory_key)[memory_key] return chat_history def get_new_instructions(self, meta_output): """Get New Instructions from the meta_output""" delimiter = "Instructions: " - new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter) :] + new_instructions = meta_output[meta_output.find(delimiter) + + len(delimiter):] return new_instructions def run(self, task: str): @@ -149,8 +150,7 @@ class MetaPrompterAgent: meta_chain = self.initialize_meta_chain() meta_output = meta_chain.predict( - chat_history=self.get_chat_history(chain.memory) - ) + chat_history=self.get_chat_history(chain.memory)) print(f"Feedback: {meta_output}") self.instructions = self.get_new_instructions(meta_output) diff --git a/swarms/agents/multi_modal_visual_agent.py b/swarms/agents/multi_modal_visual_agent.py index 34780594..72b6c50e 100644 --- a/swarms/agents/multi_modal_visual_agent.py +++ b/swarms/agents/multi_modal_visual_agent.py @@ -150,6 +150,7 @@ def seed_everything(seed): def prompts(name, description): + def decorator(func): func.name = name func.description = description @@ -171,9 +172,12 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): kernel = np.multiply(kernel_h, np.transpose(kernel_w)) kernel[steps:-steps, steps:-steps] = 1 - kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1] - kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] - kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1] + kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, + steps - 1] + kernel[:steps, + -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] + kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, + steps - 1] kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps] kernel = np.expand_dims(kernel, 2) kernel = np.repeat(kernel, 3, 2) @@ -207,12 +211,12 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): kernel[steps:-steps, :steps] = left kernel[steps:-steps, -steps:] = right - pt_gt_img = easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] - gaussian_gt_img = ( - kernel * gt_img_array + (1 - kernel) * pt_gt_img - ) # gt img with blur img + pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] + gaussian_gt_img = (kernel * gt_img_array + (1 - kernel) * pt_gt_img + ) # gt img with blur img gaussian_gt_img = gaussian_gt_img.astype(np.int64) - easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] = gaussian_gt_img + easy_img[pos_h:pos_h + old_size[1], + pos_w:pos_w + old_size[0]] = gaussian_gt_img gaussian_img = Image.fromarray(easy_img) return gaussian_img @@ -252,6 +256,7 @@ def get_new_image_name(org_img_name, func_name="update"): class InstructPix2Pix: + def __init__(self, device): print(f"Initializing InstructPix2Pix to {device}") self.device = device @@ -260,110 +265,102 @@ class InstructPix2Pix: self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ).to(device) self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) @prompts( name="Instruct Image Using Text", - description=( - "useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. " - ), + description= + ("useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. "), ) def inference(self, inputs): """Change style of image.""" print("===>Starting InstructPix2Pix Inference") image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) original_image = Image.open(image_path) - image = self.pipe( - text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2 - ).images[0] + image = self.pipe(text, + image=original_image, + num_inference_steps=40, + image_guidance_scale=1.2).images[0] updated_image_path = get_new_image_name(image_path, func_name="pix2pix") image.save(updated_image_path) print( f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" - f" {text}, Output Image: {updated_image_path}" - ) + f" {text}, Output Image: {updated_image_path}") return updated_image_path class Text2Image: + def __init__(self, device): print(f"Initializing Text2Image to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.pipe = StableDiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype - ) + "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype) self.pipe.to(device) self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image From User Input Text", - description=( - "useful when you want to generate an image from a user input text and save" - " it to a file. like: generate an image of an object or something, or" - " generate an image that includes some objects. The input to this tool" - " should be a string, representing the text used to generate image. " - ), + description= + ("useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. "), ) def inference(self, text): image_filename = os.path.join("image", f"{str(uuid.uuid4())[:8]}.png") prompt = text + ", " + self.a_prompt image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0] image.save(image_filename) - print( - f"\nProcessed Text2Image, Input Text: {text}, Output Image:" - f" {image_filename}" - ) + print(f"\nProcessed Text2Image, Input Text: {text}, Output Image:" + f" {image_filename}") return image_filename class ImageCaptioning: + def __init__(self, device): print(f"Initializing ImageCaptioning to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-image-captioning-base" - ) + "Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained( - "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype - ).to(self.device) + "Salesforce/blip-image-captioning-base", + torch_dtype=self.torch_dtype).to(self.device) @prompts( name="Get Photo Description", - description=( - "useful when you want to know what is inside the photo. receives image_path" - " as input. The input to this tool should be a string, representing the" - " image_path. " - ), + description= + ("useful when you want to know what is inside the photo. receives image_path" + " as input. The input to this tool should be a string, representing the" + " image_path. "), ) def inference(self, image_path): - inputs = self.processor(Image.open(image_path), return_tensors="pt").to( - self.device, self.torch_dtype - ) + inputs = self.processor(Image.open(image_path), + return_tensors="pt").to(self.device, + self.torch_dtype) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text:" - f" {captions}" - ) + f" {captions}") return captions class Image2Canny: + def __init__(self, device): print("Initializing Image2Canny") self.low_threshold = 100 @@ -371,12 +368,11 @@ class Image2Canny: @prompts( name="Edge Detection On Image", - description=( - "useful when you want to detect the edge of the image. like: detect the" - " edges of this image, or canny detection on image, or perform edge" - " detection on this image, or detect the canny image of this image. The" - " input to this tool should be a string, representing the image_path" - ), + description= + ("useful when you want to detect the edge of the image. like: detect the" + " edges of this image, or canny detection on image, or perform edge" + " detection on this image, or detect the canny image of this image. The" + " input to this tool should be a string, representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) @@ -387,14 +383,13 @@ class Image2Canny: canny = Image.fromarray(canny) updated_image_path = get_new_image_name(inputs, func_name="edge") canny.save(updated_image_path) - print( - f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text:" + f" {updated_image_path}") return updated_image_path class CannyText2Image: + def __init__(self, device): print(f"Initializing CannyText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -406,36 +401,31 @@ class CannyText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Canny Image", - description=( - "useful when you want to generate a new real image from both the user" - " description and a canny image. like: generate a real image of a object or" - " something from this canny image, or generate a new real image of a object" - " or something from this edge image. The input to this tool should be a" - " comma separated string of two, representing the image_path and the user" - " description. " - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and a canny image. like: generate a real image of a object or" + " something from this canny image, or generate a new real image of a object" + " or something from this edge image. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description. "), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -448,83 +438,77 @@ class CannyText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="canny2image") + updated_image_path = get_new_image_name(image_path, + func_name="canny2image") image.save(updated_image_path) print( f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text:" - f" {instruct_text}, Output Text: {updated_image_path}" - ) + f" {instruct_text}, Output Text: {updated_image_path}") return updated_image_path class Image2Line: + def __init__(self, device): print("Initializing Image2Line") self.detector = MLSDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Line Detection On Image", - description=( - "useful when you want to detect the straight line of the image. like:" - " detect the straight lines of this image, or straight line detection on" - " image, or perform straight line detection on this image, or detect the" - " straight line image of this image. The input to this tool should be a" - " string, representing the image_path" - ), + description= + ("useful when you want to detect the straight line of the image. like:" + " detect the straight lines of this image, or straight line detection on" + " image, or perform straight line detection on this image, or detect the" + " straight line image of this image. The input to this tool should be a" + " string, representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) mlsd = self.detector(image) updated_image_path = get_new_image_name(inputs, func_name="line-of") mlsd.save(updated_image_path) - print( - f"\nProcessed Image2Line, Input Image: {inputs}, Output Line:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line:" + f" {updated_image_path}") return updated_image_path class LineText2Image: + def __init__(self, device): print(f"Initializing LineText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-mlsd", torch_dtype=self.torch_dtype - ) + "fusing/stable-diffusion-v1-5-controlnet-mlsd", + torch_dtype=self.torch_dtype) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Line Image", - description=( - "useful when you want to generate a new real image from both the user" - " description and a straight line image. like: generate a real image of a" - " object or something from this straight line image, or generate a new real" - " image of a object or something from this straight lines. The input to" - " this tool should be a comma separated string of two, representing the" - " image_path and the user description. " - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and a straight line image. like: generate a real image of a" + " object or something from this straight line image, or generate a new real" + " image of a object or something from this straight lines. The input to" + " this tool should be a comma separated string of two, representing the" + " image_path and the user description. "), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -537,83 +521,78 @@ class LineText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="line2image") + updated_image_path = get_new_image_name(image_path, + func_name="line2image") image.save(updated_image_path) print( f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text:" - f" {instruct_text}, Output Text: {updated_image_path}" - ) + f" {instruct_text}, Output Text: {updated_image_path}") return updated_image_path class Image2Hed: + def __init__(self, device): print("Initializing Image2Hed") self.detector = HEDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Hed Detection On Image", - description=( - "useful when you want to detect the soft hed boundary of the image. like:" - " detect the soft hed boundary of this image, or hed boundary detection on" - " image, or perform hed boundary detection on this image, or detect soft" - " hed boundary image of this image. The input to this tool should be a" - " string, representing the image_path" - ), + description= + ("useful when you want to detect the soft hed boundary of the image. like:" + " detect the soft hed boundary of this image, or hed boundary detection on" + " image, or perform hed boundary detection on this image, or detect soft" + " hed boundary image of this image. The input to this tool should be a" + " string, representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) hed = self.detector(image) - updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") + updated_image_path = get_new_image_name(inputs, + func_name="hed-boundary") hed.save(updated_image_path) - print( - f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed:" + f" {updated_image_path}") return updated_image_path class HedText2Image: + def __init__(self, device): print(f"Initializing HedText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-hed", torch_dtype=self.torch_dtype - ) + "fusing/stable-diffusion-v1-5-controlnet-hed", + torch_dtype=self.torch_dtype) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Soft Hed Boundary Image", - description=( - "useful when you want to generate a new real image from both the user" - " description and a soft hed boundary image. like: generate a real image of" - " a object or something from this soft hed boundary image, or generate a" - " new real image of a object or something from this hed boundary. The input" - " to this tool should be a comma separated string of two, representing the" - " image_path and the user description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and a soft hed boundary image. like: generate a real image of" + " a object or something from this soft hed boundary image, or generate a" + " new real image of a object or something from this hed boundary. The input" + " to this tool should be a comma separated string of two, representing the" + " image_path and the user description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -626,28 +605,27 @@ class HedText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="hed2image") + updated_image_path = get_new_image_name(image_path, + func_name="hed2image") image.save(updated_image_path) - print( - f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class Image2Scribble: + def __init__(self, device): print("Initializing Image2Scribble") self.detector = HEDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Sketch Detection On Image", - description=( - "useful when you want to generate a scribble of the image. like: generate a" - " scribble of this image, or generate a sketch from this image, detect the" - " sketch from this image. The input to this tool should be a string," - " representing the image_path" - ), + description= + ("useful when you want to generate a scribble of the image. like: generate a" + " scribble of this image, or generate a sketch from this image, detect the" + " sketch from this image. The input to this tool should be a string," + " representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) @@ -656,12 +634,12 @@ class Image2Scribble: scribble.save(updated_image_path) print( f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble:" - f" {updated_image_path}" - ) + f" {updated_image_path}") return updated_image_path class ScribbleText2Image: + def __init__(self, device): print(f"Initializing ScribbleText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -673,34 +651,29 @@ class ScribbleText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Sketch Image", - description=( - "useful when you want to generate a new real image from both the user" - " description and a scribble image or a sketch image. The input to this" - " tool should be a comma separated string of two, representing the" - " image_path and the user description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and a scribble image or a sketch image. The input to this" + " tool should be a comma separated string of two, representing the" + " image_path and the user description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -713,41 +686,41 @@ class ScribbleText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="scribble2image") + updated_image_path = get_new_image_name(image_path, + func_name="scribble2image") image.save(updated_image_path) print( f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class Image2Pose: + def __init__(self, device): print("Initializing Image2Pose") - self.detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") + self.detector = OpenposeDetector.from_pretrained( + "lllyasviel/ControlNet") @prompts( name="Pose Detection On Image", - description=( - "useful when you want to detect the human pose of the image. like: generate" - " human poses of this image, or generate a pose image from this image. The" - " input to this tool should be a string, representing the image_path" - ), + description= + ("useful when you want to detect the human pose of the image. like: generate" + " human poses of this image, or generate a pose image from this image. The" + " input to this tool should be a string, representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) pose = self.detector(image) updated_image_path = get_new_image_name(inputs, func_name="human-pose") pose.save(updated_image_path) - print( - f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose:" + f" {updated_image_path}") return updated_image_path class PoseText2Image: + def __init__(self, device): print(f"Initializing PoseText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -759,13 +732,11 @@ class PoseText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.num_inference_steps = 20 self.seed = -1 @@ -773,23 +744,20 @@ class PoseText2Image: self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality" - ) + " fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Pose Image", - description=( - "useful when you want to generate a new real image from both the user" - " description and a human pose image. like: generate a real image of a" - " human from this human pose image, or generate a new real image of a human" - " from this pose. The input to this tool should be a comma separated string" - " of two, representing the image_path and the user description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and a human pose image. like: generate a real image of a" + " human from this human pose image, or generate a new real image of a human" + " from this pose. The input to this tool should be a comma separated string" + " of two, representing the image_path and the user description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -802,56 +770,52 @@ class PoseText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="pose2image") + updated_image_path = get_new_image_name(image_path, + func_name="pose2image") image.save(updated_image_path) print( f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class SegText2Image: + def __init__(self, device): print(f"Initializing SegText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=self.torch_dtype - ) + "fusing/stable-diffusion-v1-5-controlnet-seg", + torch_dtype=self.torch_dtype) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality" - ) + " fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Segmentations", - description=( - "useful when you want to generate a new real image from both the user" - " description and segmentations. like: generate a real image of a object or" - " something from this segmentation image, or generate a new real image of a" - " object or something from these segmentations. The input to this tool" - " should be a comma separated string of two, representing the image_path" - " and the user description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and segmentations. like: generate a real image of a object or" + " something from this segmentation image, or generate a new real image of a" + " object or something from these segmentations. The input to this tool" + " should be a comma separated string of two, representing the image_path" + " and the user description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -864,28 +828,27 @@ class SegText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="segment2image") + updated_image_path = get_new_image_name(image_path, + func_name="segment2image") image.save(updated_image_path) - print( - f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class Image2Depth: + def __init__(self, device): print("Initializing Image2Depth") self.depth_estimator = pipeline("depth-estimation") @prompts( name="Predict Depth On Image", - description=( - "useful when you want to detect depth of the image. like: generate the" - " depth from this image, or detect the depth map on this image, or predict" - " the depth for this image. The input to this tool should be a string," - " representing the image_path" - ), + description= + ("useful when you want to detect depth of the image. like: generate the" + " depth from this image, or detect the depth map on this image, or predict" + " the depth for this image. The input to this tool should be a string," + " representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) @@ -896,14 +859,13 @@ class Image2Depth: depth = Image.fromarray(depth) updated_image_path = get_new_image_name(inputs, func_name="depth") depth.save(updated_image_path) - print( - f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}") return updated_image_path class DepthText2Image: + def __init__(self, device): print(f"Initializing DepthText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -915,36 +877,31 @@ class DepthText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality" - ) + " fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Depth", - description=( - "useful when you want to generate a new real image from both the user" - " description and depth image. like: generate a real image of a object or" - " something from this depth image, or generate a new real image of a object" - " or something from the depth map. The input to this tool should be a comma" - " separated string of two, representing the image_path and the user" - " description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and depth image. like: generate a real image of a object or" + " something from this depth image, or generate a new real image of a object" + " or something from the depth map. The input to this tool should be a comma" + " separated string of two, representing the image_path and the user" + " description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -957,30 +914,29 @@ class DepthText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="depth2image") + updated_image_path = get_new_image_name(image_path, + func_name="depth2image") image.save(updated_image_path) print( f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class Image2Normal: + def __init__(self, device): print("Initializing Image2Normal") - self.depth_estimator = pipeline( - "depth-estimation", model="Intel/dpt-hybrid-midas" - ) + self.depth_estimator = pipeline("depth-estimation", + model="Intel/dpt-hybrid-midas") self.bg_threhold = 0.4 @prompts( name="Predict Normal Map On Image", - description=( - "useful when you want to detect norm map of the image. like: generate" - " normal map from this image, or predict normal map of this image. The" - " input to this tool should be a string, representing the image_path" - ), + description= + ("useful when you want to detect norm map of the image. like: generate" + " normal map from this image, or predict normal map of this image. The" + " input to this tool should be a string, representing the image_path"), ) def inference(self, inputs): image = Image.open(inputs) @@ -996,20 +952,19 @@ class Image2Normal: y[image_depth < self.bg_threhold] = 0 z = np.ones_like(x) * np.pi * 2.0 image = np.stack([x, y, z], axis=2) - image /= np.sum(image**2.0, axis=2, keepdims=True) ** 0.5 + image /= np.sum(image**2.0, axis=2, keepdims=True)**0.5 image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) image = Image.fromarray(image) image = image.resize(original_size) updated_image_path = get_new_image_name(inputs, func_name="normal-map") image.save(updated_image_path) - print( - f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth:" - f" {updated_image_path}" - ) + print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}") return updated_image_path class NormalText2Image: + def __init__(self, device): print(f"Initializing NormalText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -1021,36 +976,31 @@ class NormalText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality" - ) + " fewer digits, cropped, worst quality, low quality") @prompts( name="Generate Image Condition On Normal Map", - description=( - "useful when you want to generate a new real image from both the user" - " description and normal map. like: generate a real image of a object or" - " something from this normal map, or generate a new real image of a object" - " or something from the normal map. The input to this tool should be a" - " comma separated string of two, representing the image_path and the user" - " description" - ), + description= + ("useful when you want to generate a new real image from both the user" + " description and normal map. like: generate a real image of a object or" + " something from this normal map, or generate a new real image of a object" + " or something from the normal map. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description"), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -1063,50 +1013,53 @@ class NormalText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, func_name="normal2image") + updated_image_path = get_new_image_name(image_path, + func_name="normal2image") image.save(updated_image_path) print( f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}" - ) + f" {instruct_text}, Output Image: {updated_image_path}") return updated_image_path class VisualQuestionAnswering: + def __init__(self, device): print(f"Initializing VisualQuestionAnswering to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.device = device - self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") + self.processor = BlipProcessor.from_pretrained( + "Salesforce/blip-vqa-base") self.model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype - ).to(self.device) + "Salesforce/blip-vqa-base", + torch_dtype=self.torch_dtype).to(self.device) @prompts( name="Answer Question About The Image", - description=( - "useful when you need an answer for a question based on an image. like:" - " what is the background color of the last image, how many cats in this" - " figure, what is in this figure. The input to this tool should be a comma" - " separated string of two, representing the image_path and the question" + description= + ("useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" ), ) def inference(self, inputs): - image_path, question = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) + image_path, question = inputs.split(",")[0], ",".join( + inputs.split(",")[1:]) raw_image = Image.open(image_path).convert("RGB") - inputs = self.processor(raw_image, question, return_tensors="pt").to( - self.device, self.torch_dtype - ) + inputs = self.processor(raw_image, question, + return_tensors="pt").to(self.device, + self.torch_dtype) out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}" - ) + f" Question: {question}, Output Answer: {answer}") return answer class Segmenting: + def __init__(self, device): print(f"Inintializing Segmentation to {device}") self.device = device @@ -1151,7 +1104,8 @@ class Segmenting: h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) * 255 - image = cv2.addWeighted(image, 0.7, mask_image.astype("uint8"), transparency, 0) + image = cv2.addWeighted(image, 0.7, mask_image.astype("uint8"), + transparency, 0) return image @@ -1159,10 +1113,12 @@ class Segmenting: x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch( - plt.Rectangle( - (x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=2 - ) - ) + plt.Rectangle((x0, y0), + w, + h, + edgecolor="green", + facecolor=(0, 0, 0, 0), + lw=2)) ax.text(x0, y0, label) def get_mask_with_boxes(self, image_pil, image, boxes_filt): @@ -1175,8 +1131,7 @@ class Segmenting: boxes_filt = boxes_filt.cpu() transformed_boxes = self.sam_predictor.transform.apply_boxes_torch( - boxes_filt, image.shape[:2] - ).to(self.device) + boxes_filt, image.shape[:2]).to(self.device) masks, _, _ = self.sam_predictor.predict_torch( point_coords=None, @@ -1186,7 +1141,8 @@ class Segmenting: ) return masks - def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, pred_phrases): + def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, + pred_phrases): image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam_predictor.set_image(image) @@ -1196,11 +1152,13 @@ class Segmenting: # draw output image for mask in masks: - image = self.show_mask( - mask[0].cpu().numpy(), image, random_color=True, transparency=0.3 - ) + image = self.show_mask(mask[0].cpu().numpy(), + image, + random_color=True, + transparency=0.3) - updated_image_path = get_new_image_name(image_path, func_name="segmentation") + updated_image_path = get_new_image_name(image_path, + func_name="segmentation") new_image = Image.fromarray(image) new_image.save(updated_image_path) @@ -1212,9 +1170,8 @@ class Segmenting: with torch.cuda.amp.autocast(): self.sam_predictor.set_image(img) - def show_points( - self, coords: np.ndarray, labels: np.ndarray, image: np.ndarray - ) -> np.ndarray: + def show_points(self, coords: np.ndarray, labels: np.ndarray, + image: np.ndarray) -> np.ndarray: """Visualize points on top of an image. Args: @@ -1228,13 +1185,17 @@ class Segmenting: pos_points = coords[labels == 1] neg_points = coords[labels == 0] for p in pos_points: - image = cv2.circle( - image, p.astype(int), radius=3, color=(0, 255, 0), thickness=-1 - ) + image = cv2.circle(image, + p.astype(int), + radius=3, + color=(0, 255, 0), + thickness=-1) for p in neg_points: - image = cv2.circle( - image, p.astype(int), radius=3, color=(255, 0, 0), thickness=-1 - ) + image = cv2.circle(image, + p.astype(int), + radius=3, + color=(255, 0, 0), + thickness=-1) return image def segment_image_with_click(self, img, is_positive: bool): @@ -1252,13 +1213,17 @@ class Segmenting: multimask_output=False, ) - img = self.show_mask(masks[0], img, random_color=False, transparency=0.3) + img = self.show_mask(masks[0], + img, + random_color=False, + transparency=0.3) img = self.show_points(input_point, input_label, img) return img - def segment_image_with_coordinate(self, img, is_positive: bool, coordinate: tuple): + def segment_image_with_coordinate(self, img, is_positive: bool, + coordinate: tuple): """ Args: img (numpy.ndarray): the given image, shape: H x W x 3. @@ -1289,7 +1254,10 @@ class Segmenting: multimask_output=False, ) - img = self.show_mask(masks[0], img, random_color=False, transparency=0.3) + img = self.show_mask(masks[0], + img, + random_color=False, + transparency=0.3) img = self.show_points(input_point, input_label, img) @@ -1301,13 +1269,12 @@ class Segmenting: @prompts( name="Segment the Image", - description=( - "useful when you want to segment all the part of the image, but not segment" - " a certain object.like: segment all the object in this image, or generate" - " segmentations on this image, or segment the image,or perform segmentation" - " on this image, or segment all the object in this image.The input to this" - " tool should be a string, representing the image_path" - ), + description= + ("useful when you want to segment all the part of the image, but not segment" + " a certain object.like: segment all the object in this image, or generate" + " segmentations on this image, or segment the image,or perform segmentation" + " on this image, or segment all the object in this image.The input to this" + " tool should be a string, representing the image_path"), ) def inference_all(self, image_path): image = cv2.imread(image_path) @@ -1328,19 +1295,26 @@ class Segmenting: img[:, :, i] = color_mask[i] ax.imshow(np.dstack((img, m))) - updated_image_path = get_new_image_name(image_path, func_name="segment-image") + updated_image_path = get_new_image_name(image_path, + func_name="segment-image") plt.axis("off") - plt.savefig(updated_image_path, bbox_inches="tight", dpi=300, pad_inches=0.0) + plt.savefig(updated_image_path, + bbox_inches="tight", + dpi=300, + pad_inches=0.0) return updated_image_path class Text2Box: + def __init__(self, device): print(f"Initializing ObjectDetection to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 - self.model_checkpoint_path = os.path.join("checkpoints", "groundingdino") - self.model_config_path = os.path.join("checkpoints", "grounding_config.py") + self.model_checkpoint_path = os.path.join("checkpoints", + "groundingdino") + self.model_config_path = os.path.join("checkpoints", + "grounding_config.py") self.download_parameters() self.box_threshold = 0.3 self.text_threshold = 0.25 @@ -1358,13 +1332,11 @@ class Text2Box: # load image image_pil = Image.open(image_path).convert("RGB") # load image - transform = T.Compose( - [ - T.RandomResize([512], max_size=1333), - T.ToTensor(), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] - ) + transform = T.Compose([ + T.RandomResize([512], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) image, _ = transform(image_pil, None) # 3, h, w return image_pil, image @@ -1373,9 +1345,8 @@ class Text2Box: args.device = self.device model = build_model(args) checkpoint = torch.load(self.model_checkpoint_path, map_location="cpu") - load_res = model.load_state_dict( - clean_state_dict(checkpoint["model"]), strict=False - ) + load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), + strict=False) print(load_res) _ = model.eval() return model @@ -1406,11 +1377,11 @@ class Text2Box: # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): - pred_phrase = get_phrases_from_posmap( - logit > self.text_threshold, tokenized, tokenlizer - ) + pred_phrase = get_phrases_from_posmap(logit > self.text_threshold, + tokenized, tokenlizer) if with_logits: - pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") + pred_phrases.append(pred_phrase + + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) @@ -1420,7 +1391,8 @@ class Text2Box: H, W = tgt["size"] boxes = tgt["boxes"] labels = tgt["labels"] - assert len(boxes) == len(labels), "boxes and labels must have same length" + assert len(boxes) == len( + labels), "boxes and labels must have same length" draw = ImageDraw.Draw(image_pil) mask = Image.new("L", image_pil.size, 0) @@ -1458,12 +1430,11 @@ class Text2Box: @prompts( name="Detect the Give Object", - description=( - "useful when you only want to detect or find out given objects in the" - " pictureThe input to this tool should be a comma separated string of two," - " representing the image_path, the text description of the object to be" - " found" - ), + description= + ("useful when you only want to detect or find out given objects in the" + " pictureThe input to this tool should be a comma separated string of two," + " representing the image_path, the text description of the object to be" + " found"), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") @@ -1481,19 +1452,18 @@ class Text2Box: image_with_box = self.plot_boxes_to_image(image_pil, pred_dict)[0] - updated_image_path = get_new_image_name( - image_path, func_name="detect-something" - ) + updated_image_path = get_new_image_name(image_path, + func_name="detect-something") updated_image = image_with_box.resize(size) updated_image.save(updated_image_path) print( f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be" - f" Detect {det_prompt}, Output Image: {updated_image_path}" - ) + f" Detect {det_prompt}, Output Image: {updated_image_path}") return updated_image_path class Inpainting: + def __init__(self, device): self.device = device self.revision = "fp16" if "cuda" in self.device else None @@ -1504,13 +1474,16 @@ class Inpainting: revision=self.revision, torch_dtype=self.torch_dtype, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker" - ), + "CompVis/stable-diffusion-safety-checker"), ).to(device) - def __call__( - self, prompt, image, mask_image, height=512, width=512, num_inference_steps=50 - ): + def __call__(self, + prompt, + image, + mask_image, + height=512, + width=512, + num_inference_steps=50): update_image = self.inpaint( prompt=prompt, image=image.resize((width, height)), @@ -1533,29 +1506,27 @@ class InfinityOutPainting: self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") def get_BLIP_vqa(self, image, question): - inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to( - self.ImageVQA.device, self.ImageVQA.torch_dtype - ) + inputs = self.ImageVQA.processor(image, question, + return_tensors="pt").to( + self.ImageVQA.device, + self.ImageVQA.torch_dtype) out = self.ImageVQA.model.generate(**inputs) - answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True) + answer = self.ImageVQA.processor.decode(out[0], + skip_special_tokens=True) print( f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output" - f" Answer: {answer}" - ) + f" Answer: {answer}") return answer def get_BLIP_caption(self, image): inputs = self.ImageCaption.processor(image, return_tensors="pt").to( - self.ImageCaption.device, self.ImageCaption.torch_dtype - ) + self.ImageCaption.device, self.ImageCaption.torch_dtype) out = self.ImageCaption.model.generate(**inputs) BLIP_caption = self.ImageCaption.processor.decode( - out[0], skip_special_tokens=True - ) + out[0], skip_special_tokens=True) return BLIP_caption def check_prompt(self, prompt): @@ -1569,8 +1540,7 @@ class InfinityOutPainting: def get_imagine_caption(self, image, imagine): BLIP_caption = self.get_BLIP_caption(image) background_color = self.get_BLIP_vqa( - image, "what is the background color of this image" - ) + image, "what is the background color of this image") style = self.get_BLIP_vqa(image, "what is the style of this image") imagine_prompt = ( "let's pretend you are an excellent painter and now there is an incomplete" @@ -1578,54 +1548,47 @@ class InfinityOutPainting: " painting and describe ityou should consider the background color is" f" {background_color}, the style is {style}You should make the painting as" " vivid and realistic as possibleYou can not use words like painting or" - " pictureand you should use no more than 50 words to describe it" - ) + " pictureand you should use no more than 50 words to describe it") caption = self.llm(imagine_prompt) if imagine else BLIP_caption caption = self.check_prompt(caption) - print( - f"BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}" - ) if imagine else print(f"Prompt: {caption}") + print(f"BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}" + ) if imagine else print(f"Prompt: {caption}") return caption def resize_image(self, image, max_size=1000000, multiple=8): aspect_ratio = image.size[0] / image.size[1] new_width = int(math.sqrt(max_size * aspect_ratio)) new_height = int(new_width / aspect_ratio) - new_width, new_height = new_width - (new_width % multiple), new_height - ( - new_height % multiple - ) + new_width, new_height = new_width - ( + new_width % multiple), new_height - (new_height % multiple) return image.resize((new_width, new_height)) def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt): old_img = original_img while old_img.size != tosize: - prompt = ( - self.check_prompt(usr_prompt) - if usr_prompt - else self.get_imagine_caption(old_img, imagine) - ) + prompt = (self.check_prompt(usr_prompt) if usr_prompt else + self.get_imagine_caption(old_img, imagine)) crop_w = 15 if old_img.size[0] != tosize[0] else 0 crop_h = 15 if old_img.size[1] != tosize[1] else 0 old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h)) temp_canvas_size = ( expand_ratio * old_img.width - if expand_ratio * old_img.width < tosize[0] - else tosize[0], + if expand_ratio * old_img.width < tosize[0] else tosize[0], expand_ratio * old_img.height - if expand_ratio * old_img.height < tosize[1] - else tosize[1], + if expand_ratio * old_img.height < tosize[1] else tosize[1], ) - temp_canvas, temp_mask = Image.new( - "RGB", temp_canvas_size, color="white" - ), Image.new("L", temp_canvas_size, color="white") + temp_canvas, temp_mask = Image.new("RGB", + temp_canvas_size, + color="white"), Image.new( + "L", + temp_canvas_size, + color="white") x, y = (temp_canvas.width - old_img.width) // 2, ( - temp_canvas.height - old_img.height - ) // 2 + temp_canvas.height - old_img.height) // 2 temp_canvas.paste(old_img, (x, y)) temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height)) resized_temp_canvas, resized_temp_mask = self.resize_image( - temp_canvas - ), self.resize_image(temp_mask) + temp_canvas), self.resize_image(temp_mask) image = self.inpaint( prompt=prompt, image=resized_temp_canvas, @@ -1640,11 +1603,11 @@ class InfinityOutPainting: @prompts( name="Extend An Image", - description=( - "useful when you need to extend an image into a larger image.like: extend" - " the image into a resolution of 2048x1024, extend the image into" - " 2048x1024. The input to this tool should be a comma separated string of" - " two, representing the image_path and the resolution of widthxheight" + description= + ("useful when you need to extend an image into a larger image.like: extend" + " the image into a resolution of 2048x1024, extend the image into" + " 2048x1024. The input to this tool should be a comma separated string of" + " two, representing the image_path and the resolution of widthxheight" ), ) def inference(self, inputs): @@ -1654,12 +1617,12 @@ class InfinityOutPainting: image = Image.open(image_path) image = ImageOps.crop(image, (10, 10, 10, 10)) out_painted_image = self.dowhile(image, tosize, 4, True, False) - updated_image_path = get_new_image_name(image_path, func_name="outpainting") + updated_image_path = get_new_image_name(image_path, + func_name="outpainting") out_painted_image.save(updated_image_path) print( f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input" - f" Resolution: {resolution}, Output Image: {updated_image_path}" - ) + f" Resolution: {resolution}, Output Image: {updated_image_path}") return updated_image_path @@ -1678,22 +1641,20 @@ class ObjectSegmenting: " pictureaccording to the given textlike: segment the cat,or can you" " segment an obeject for meThe input to this tool should be a comma" " separated string of two, representing the image_path, the text" - " description of the object to be found" - ), + " description of the object to be found"), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") print(f"image_path={image_path}, text_prompt={det_prompt}") image_pil, image = self.grounding.load_image(image_path) - boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, det_prompt) + boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( + image, det_prompt) updated_image_path = self.sam.segment_image_with_boxes( - image_pil, image_path, boxes_filt, pred_phrases - ) + image_pil, image_path, boxes_filt, pred_phrases) print( f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be" - f" Segment {det_prompt}, Output Image: {updated_image_path}" - ) + f" Segment {det_prompt}, Output Image: {updated_image_path}") return updated_image_path def merge_masks(self, masks): @@ -1724,8 +1685,7 @@ class ObjectSegmenting: image_pil, image = self.grounding.load_image(image_path) boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( - image, text_prompt - ) + image, text_prompt) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam.sam_predictor.set_image(image) @@ -1738,9 +1698,10 @@ class ObjectSegmenting: # draw output image for mask in masks: - image = self.sam.show_mask( - mask[0].cpu().numpy(), image, random_color=True, transparency=0.3 - ) + image = self.sam.show_mask(mask[0].cpu().numpy(), + image, + random_color=True, + transparency=0.3) Image.fromarray(merged_mask) @@ -1750,9 +1711,8 @@ class ObjectSegmenting: class ImageEditing: template_model = True - def __init__( - self, Text2Box: Text2Box, Segmenting: Segmenting, Inpainting: Inpainting - ): + def __init__(self, Text2Box: Text2Box, Segmenting: Segmenting, + Inpainting: Inpainting): print("Initializing ImageEditing") self.sam = Segmenting self.grounding = Text2Box @@ -1765,8 +1725,7 @@ class ImageEditing: mask_array = np.zeros_like(mask, dtype=bool) for idx in true_indices: padded_slice = tuple( - slice(max(0, i - padding), i + padding + 1) for i in idx - ) + slice(max(0, i - padding), i + padding + 1) for i in idx) mask_array[padded_slice] = True new_mask = (mask_array * 255).astype(np.uint8) # new_mask @@ -1774,38 +1733,34 @@ class ImageEditing: @prompts( name="Remove Something From The Photo", - description=( - "useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. " - ), + description= + ("useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. "), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",")[0], ",".join( - inputs.split(",")[1:] - ) + inputs.split(",")[1:]) return self.inference_replace_sam( - f"{image_path},{to_be_removed_txt},background" - ) + f"{image_path},{to_be_removed_txt},background") @prompts( name="Replace Something From The Photo", - description=( - "useful when you want to replace an object from the object description or" - " location with another object from its description. The input to this tool" - " should be a comma separated string of three, representing the image_path," - " the object to be replaced, the object to be replaced with " - ), + description= + ("useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with "), ) def inference_replace_sam(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") - print(f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}") + print( + f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}") image_pil, image = self.grounding.load_image(image_path) boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( - image, to_be_replaced_txt - ) + image, to_be_replaced_txt) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam.sam_predictor.set_image(image) @@ -1817,19 +1772,16 @@ class ImageEditing: mask = self.pad_edge(mask, padding=20) # numpy mask_image = Image.fromarray(mask) - updated_image = self.inpaint( - prompt=replace_with_txt, image=image_pil, mask_image=mask_image - ) - updated_image_path = get_new_image_name( - image_path, func_name="replace-something" - ) + updated_image = self.inpaint(prompt=replace_with_txt, + image=image_pil, + mask_image=mask_image) + updated_image_path = get_new_image_name(image_path, + func_name="replace-something") updated_image = updated_image.resize(image_pil.size) updated_image.save(updated_image_path) - print( - f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" - f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" - f" {updated_image_path}" - ) + print(f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" + f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" + f" {updated_image_path}") return updated_image_path @@ -1851,10 +1803,9 @@ class BackgroundRemoving: @prompts( name="Remove the background", - description=( - "useful when you want to extract the object or remove the background," - "the input should be a string image_path" - ), + description= + ("useful when you want to extract the object or remove the background," + "the input should be a string image_path"), ) def inference(self, image_path): """ @@ -1868,9 +1819,8 @@ class BackgroundRemoving: mask = Image.fromarray(mask) image.putalpha(mask) - updated_image_path = get_new_image_name( - image_path, func_name="detect-something" - ) + updated_image_path = get_new_image_name(image_path, + func_name="detect-something") image.save(updated_image_path) return updated_image_path @@ -1893,6 +1843,7 @@ class BackgroundRemoving: class MultiModalVisualAgent: + def __init__( self, load_dict, @@ -1905,8 +1856,7 @@ class MultiModalVisualAgent: if "ImageCaptioning" not in load_dict: raise ValueError( "You have to load ImageCaptioning as a basic function for" - " MultiModalVisualAgent" - ) + " MultiModalVisualAgent") self.models = {} @@ -1916,17 +1866,18 @@ class MultiModalVisualAgent: for class_name, module in globals().items(): if getattr(module, "template_model", False): template_required_names = { - k - for k in inspect.signature(module.__init__).parameters.keys() - if k != "self" + k for k in inspect.signature( + module.__init__).parameters.keys() if k != "self" } - loaded_names = set([type(e).__name__ for e in self.models.values()]) + loaded_names = set( + [type(e).__name__ for e in self.models.values()]) if template_required_names.issubset(loaded_names): - self.models[class_name] = globals()[class_name]( - **{name: self.models[name] for name in template_required_names} - ) + self.models[class_name] = globals()[class_name](**{ + name: self.models[name] + for name in template_required_names + }) print(f"All the Available Functions: {self.models}") @@ -1936,13 +1887,13 @@ class MultiModalVisualAgent: if e.startswith("inference"): func = getattr(instance, e) self.tools.append( - Tool(name=func.name, description=func.description, func=func) - ) + Tool(name=func.name, + description=func.description, + func=func)) self.llm = OpenAI(temperature=0) - self.memory = ConversationBufferMemory( - memory_key="chat_history", output_key="output" - ) + self.memory = ConversationBufferMemory(memory_key="chat_history", + output_key="output") def init_agent(self, lang): self.memory.clear() @@ -1980,8 +1931,7 @@ class MultiModalVisualAgent: def run_text(self, text): self.agent.memory.buffer = cut_dialogue_history( - self.agent.memory.buffer, keep_last_n_words=500 - ) + self.agent.memory.buffer, keep_last_n_words=500) res = self.agent({"input": text.strip()}) res["output"] = res["output"].replace("\\", "/") @@ -1991,10 +1941,8 @@ class MultiModalVisualAgent: res["output"], ) - print( - f"\nProcessed run_text, Input text: {text}\n" - f"Current Memory: {self.agent.memory.buffer}" - ) + print(f"\nProcessed run_text, Input text: {text}\n" + f"Current Memory: {self.agent.memory.buffer}") return response @@ -2016,12 +1964,10 @@ class MultiModalVisualAgent: description = self.models["ImageCaptioning"].inference(image_filename) if lang == "Chinese": - Human_prompt = ( - f"\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ:" - f" {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒ" - "ไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚" - ' ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n' - ) + Human_prompt = (f"\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ:" + f" {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒ" + "ไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚" + ' ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n') AI_prompt = "ๆ”ถๅˆฐใ€‚ " else: Human_prompt = ( @@ -2029,18 +1975,14 @@ class MultiModalVisualAgent: f" {description}. This information helps you to understand this image," " but you should use tools to finish following tasks, rather than" " directly imagine from my description. If you understand, say" - ' "Received". \n' - ) + ' "Received". \n') AI_prompt = "Received. " - self.agent.memory.buffer = ( - self.agent.memory.buffer + Human_prompt + "AI: " + AI_prompt - ) + self.agent.memory.buffer = (self.agent.memory.buffer + Human_prompt + + "AI: " + AI_prompt) - print( - f"\nProcessed run_image, Input image: {image_filename}\n" - f"Current Memory: {self.agent.memory.buffer}" - ) + print(f"\nProcessed run_image, Input image: {image_filename}\n" + f"Current Memory: {self.agent.memory.buffer}") return AI_prompt @@ -2087,7 +2029,10 @@ class MultiModalAgent: """ - def __init__(self, load_dict, temperature: int = 0.1, language: str = "english"): + def __init__(self, + load_dict, + temperature: int = 0.1, + language: str = "english"): self.load_dict = load_dict self.temperature = temperature self.langigage = language @@ -2123,7 +2068,10 @@ class MultiModalAgent: except Exception as error: return f"Error processing image: {str(error)}" - def chat(self, msg: str = None, language: str = "english", streaming: bool = False): + def chat(self, + msg: str = None, + language: str = "english", + streaming: bool = False): """ Run chat with the multi-modal agent diff --git a/swarms/agents/neural_architecture_search_worker.py b/swarms/agents/neural_architecture_search_worker.py index fd253b95..3bfd8323 100644 --- a/swarms/agents/neural_architecture_search_worker.py +++ b/swarms/agents/neural_architecture_search_worker.py @@ -2,6 +2,7 @@ class Replicator: + def __init__( self, model_name, diff --git a/swarms/agents/omni_modal_agent.py b/swarms/agents/omni_modal_agent.py index 007a2219..b6fdfbdc 100644 --- a/swarms/agents/omni_modal_agent.py +++ b/swarms/agents/omni_modal_agent.py @@ -3,23 +3,20 @@ from typing import Dict, List from langchain.base_language import BaseLanguageModel from langchain.tools.base import BaseTool from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import ( - load_response_generator, -) + load_response_generator,) from langchain_experimental.autonomous_agents.hugginggpt.task_executor import ( - TaskExecutor, -) + TaskExecutor,) from langchain_experimental.autonomous_agents.hugginggpt.task_planner import ( - load_chat_planner, -) + load_chat_planner,) from transformers import load_tool from swarms.agents.message import Message class Step: - def __init__( - self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool - ): + + def __init__(self, task: str, id: int, dep: List[int], args: Dict[str, str], + tool: BaseTool): self.task = task self.id = id self.dep = dep @@ -28,6 +25,7 @@ class Step: class Plan: + def __init__(self, steps: List[Step]): self.steps = steps @@ -73,8 +71,7 @@ class OmniModalAgent: print("Loading tools...") self.tools = [ - load_tool(tool_name) - for tool_name in [ + load_tool(tool_name) for tool_name in [ "document-question-answering", "image-captioning", "image-question-answering", @@ -99,18 +96,15 @@ class OmniModalAgent: def run(self, input: str) -> str: """Run the OmniAgent""" - plan = self.chat_planner.plan( - inputs={ - "input": input, - "hf_tools": self.tools, - } - ) + plan = self.chat_planner.plan(inputs={ + "input": input, + "hf_tools": self.tools, + }) self.task_executor = TaskExecutor(plan) self.task_executor.run() response = self.response_generator.generate( - {"task_execution": self.task_executor} - ) + {"task_execution": self.task_executor}) return response diff --git a/swarms/agents/profitpilot.py b/swarms/agents/profitpilot.py index 6858dc72..a4ff13a5 100644 --- a/swarms/agents/profitpilot.py +++ b/swarms/agents/profitpilot.py @@ -145,13 +145,12 @@ def setup_knowledge_base(product_catalog: str = None): llm = OpenAI(temperature=0) embeddings = OpenAIEmbeddings() - docsearch = Chroma.from_texts( - texts, embeddings, collection_name="product-knowledge-base" - ) + docsearch = Chroma.from_texts(texts, + embeddings, + collection_name="product-knowledge-base") knowledge_base = RetrievalQA.from_chain_type( - llm=llm, chain_type="stuff", retriever=docsearch.as_retriever() - ) + llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()) return knowledge_base @@ -163,8 +162,8 @@ def get_tools(product_catalog): Tool( name="ProductSearch", func=knowledge_base.run, - description=( - "useful for when you need to answer questions about product information" + description= + ("useful for when you need to answer questions about product information" ), ), # omnimodal agent @@ -194,8 +193,7 @@ class CustomPromptTemplateForTools(StringPromptTemplate): tools = self.tools_getter(kwargs["input"]) # Create a tools variable from the list of tools provided kwargs["tools"] = "\n".join( - [f"{tool.name}: {tool.description}" for tool in tools] - ) + [f"{tool.name}: {tool.description}" for tool in tools]) # Create a list of tool names for the tools provided kwargs["tool_names"] = ", ".join([tool.name for tool in tools]) return self.template.format(**kwargs) @@ -218,8 +216,7 @@ class SalesConvoOutputParser(AgentOutputParser): print("-------") if f"{self.ai_prefix}:" in text: return AgentFinish( - {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text - ) + {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text) regex = r"Action: (.*?)[\n]*Action Input: (.*)" match = re.search(regex, text) if not match: @@ -228,15 +225,15 @@ class SalesConvoOutputParser(AgentOutputParser): { "output": ( "I apologize, I was unable to find the answer to your question." - " Is there anything else I can help with?" - ) + " Is there anything else I can help with?") }, text, ) # raise OutputParserException(f"Could not parse LLM output: `{text}`") action = match.group(1) action_input = match.group(2) - return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text) + return AgentAction(action.strip(), + action_input.strip(" ").strip('"'), text) @property def _type(self) -> str: @@ -264,13 +261,11 @@ class ProfitPilot(Chain, BaseModel): "2": ( "Qualification: Qualify the prospect by confirming if they are the right" " person to talk to regarding your product/service. Ensure that they have" - " the authority to make purchasing decisions." - ), + " the authority to make purchasing decisions."), "3": ( "Value proposition: Briefly explain how your product/service can benefit" " the prospect. Focus on the unique selling points and value proposition of" - " your product/service that sets it apart from competitors." - ), + " your product/service that sets it apart from competitors."), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs" " and pain points. Listen carefully to their responses and take notes." @@ -282,13 +277,11 @@ class ProfitPilot(Chain, BaseModel): "6": ( "Objection handling: Address any objections that the prospect may have" " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims." - ), + " testimonials to support your claims."), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has" - " been discussed and reiterate the benefits." - ), + " been discussed and reiterate the benefits."), } salesperson_name: str = "Ted Lasso" @@ -298,19 +291,16 @@ class ProfitPilot(Chain, BaseModel): "Sleep Haven is a premium mattress company that provides customers with the" " most comfortable and supportive sleeping experience possible. We offer a" " range of high-quality mattresses, pillows, and bedding accessories that are" - " designed to meet the unique needs of our customers." - ) + " designed to meet the unique needs of our customers.") company_values: str = ( "Our mission at Sleep Haven is to help people achieve a better night's sleep by" " providing them with the best possible sleep solutions. We believe that" " quality sleep is essential to overall health and well-being, and we are" " committed to helping our customers achieve optimal sleep by offering" - " exceptional products and customer service." - ) + " exceptional products and customer service.") conversation_purpose: str = ( "find out whether they are looking to achieve better sleep via buying a premier" - " mattress." - ) + " mattress.") conversation_type: str = "call" def retrieve_conversation_stage(self, key): @@ -336,8 +326,7 @@ class ProfitPilot(Chain, BaseModel): ) self.current_conversation_stage = self.retrieve_conversation_stage( - conversation_stage_id - ) + conversation_stage_id) print(f"Conversation Stage: {self.current_conversation_stage}") @@ -391,13 +380,15 @@ class ProfitPilot(Chain, BaseModel): return {} @classmethod - def from_llm(cls, llm: BaseLLM, verbose: bool = False, **kwargs): # noqa: F821 + def from_llm(cls, + llm: BaseLLM, + verbose: bool = False, + **kwargs): # noqa: F821 """Initialize the SalesGPT Controller.""" stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose) sales_conversation_utterance_chain = SalesConversationChain.from_llm( - llm, verbose=verbose - ) + llm, verbose=verbose) if "use_tools" in kwargs.keys() and kwargs["use_tools"] is False: sales_agent_executor = None @@ -430,7 +421,8 @@ class ProfitPilot(Chain, BaseModel): # WARNING: this output parser is NOT reliable yet # It makes assumptions about output from LLM which can break and throw an error - output_parser = SalesConvoOutputParser(ai_prefix=kwargs["salesperson_name"]) + output_parser = SalesConvoOutputParser( + ai_prefix=kwargs["salesperson_name"]) sales_agent_with_tools = LLMSingleActionAgent( llm_chain=llm_chain, @@ -441,12 +433,12 @@ class ProfitPilot(Chain, BaseModel): ) sales_agent_executor = AgentExecutor.from_agent_and_tools( - agent=sales_agent_with_tools, tools=tools, verbose=verbose - ) + agent=sales_agent_with_tools, tools=tools, verbose=verbose) return cls( stage_analyzer_chain=stage_analyzer_chain, - sales_conversation_utterance_chain=sales_conversation_utterance_chain, + sales_conversation_utterance_chain= + sales_conversation_utterance_chain, sales_agent_executor=sales_agent_executor, verbose=verbose, **kwargs, @@ -458,32 +450,27 @@ config = dict( salesperson_name="Ted Lasso", salesperson_role="Business Development Representative", company_name="Sleep Haven", - company_business=( - "Sleep Haven is a premium mattress company that provides customers with the" - " most comfortable and supportive sleeping experience possible. We offer a" - " range of high-quality mattresses, pillows, and bedding accessories that are" - " designed to meet the unique needs of our customers." - ), - company_values=( - "Our mission at Sleep Haven is to help people achieve a better night's sleep by" - " providing them with the best possible sleep solutions. We believe that" - " quality sleep is essential to overall health and well-being, and we are" - " committed to helping our customers achieve optimal sleep by offering" - " exceptional products and customer service." - ), - conversation_purpose=( - "find out whether they are looking to achieve better sleep via buying a premier" - " mattress." - ), + company_business= + ("Sleep Haven is a premium mattress company that provides customers with the" + " most comfortable and supportive sleeping experience possible. We offer a" + " range of high-quality mattresses, pillows, and bedding accessories that are" + " designed to meet the unique needs of our customers."), + company_values= + ("Our mission at Sleep Haven is to help people achieve a better night's sleep by" + " providing them with the best possible sleep solutions. We believe that" + " quality sleep is essential to overall health and well-being, and we are" + " committed to helping our customers achieve optimal sleep by offering" + " exceptional products and customer service."), + conversation_purpose= + ("find out whether they are looking to achieve better sleep via buying a premier" + " mattress."), conversation_history=[], conversation_type="call", conversation_stage=conversation_stages.get( "1", - ( - "Introduction: Start the conversation by introducing yourself and your" - " company. Be polite and respectful while keeping the tone of the" - " conversation professional." - ), + ("Introduction: Start the conversation by introducing yourself and your" + " company. Be polite and respectful while keeping the tone of the" + " conversation professional."), ), use_tools=True, product_catalog="sample_product_catalog.txt", diff --git a/swarms/agents/refiner_agent.py b/swarms/agents/refiner_agent.py index 2a1383e9..509484e3 100644 --- a/swarms/agents/refiner_agent.py +++ b/swarms/agents/refiner_agent.py @@ -1,9 +1,11 @@ class PromptRefiner: + def __init__(self, system_prompt: str, llm): super().__init__() self.system_prompt = system_prompt self.llm = llm def run(self, task: str): - refine = self.llm(f"System Prompt: {self.system_prompt} Current task: {task}") + refine = self.llm( + f"System Prompt: {self.system_prompt} Current task: {task}") return refine diff --git a/swarms/agents/registry.py b/swarms/agents/registry.py index aa1f1375..5cf2c0d5 100644 --- a/swarms/agents/registry.py +++ b/swarms/agents/registry.py @@ -10,6 +10,7 @@ class Registry(BaseModel): entries: Dict = {} def register(self, key: str): + def decorator(class_builder): self.entries[key] = class_builder return class_builder @@ -20,8 +21,7 @@ class Registry(BaseModel): if type not in self.entries: raise ValueError( f"{type} is not registered. Please register with the" - f' .register("{type}") method provided in {self.name} registry' - ) + f' .register("{type}") method provided in {self.name} registry') return self.entries[type](**kwargs) def get_all_entries(self): diff --git a/swarms/agents/simple_agent.py b/swarms/agents/simple_agent.py index 88327095..847cbc67 100644 --- a/swarms/agents/simple_agent.py +++ b/swarms/agents/simple_agent.py @@ -29,7 +29,8 @@ class SimpleAgent: def run(self, task: str) -> str: """Run method""" - metrics = print(colored(f"Agent {self.name} is running task: {task}", "red")) + metrics = print( + colored(f"Agent {self.name} is running task: {task}", "red")) print(metrics) response = self.flow.run(task) diff --git a/swarms/artifacts/base.py b/swarms/artifacts/base.py index dac7a523..1357a86b 100644 --- a/swarms/artifacts/base.py +++ b/swarms/artifacts/base.py @@ -10,9 +10,8 @@ from marshmallow.exceptions import RegistryError @define class BaseArtifact(ABC): id: str = field(default=Factory(lambda: uuid.uuid4().hex), kw_only=True) - name: str = field( - default=Factory(lambda self: self.id, takes_self=True), kw_only=True - ) + name: str = field(default=Factory(lambda self: self.id, takes_self=True), + kw_only=True) value: any = field() type: str = field( default=Factory(lambda self: self.__class__.__name__, takes_self=True), @@ -54,7 +53,8 @@ class BaseArtifact(ABC): class_registry.register("ListArtifact", ListArtifactSchema) try: - return class_registry.get_class(artifact_dict["type"])().load(artifact_dict) + return class_registry.get_class( + artifact_dict["type"])().load(artifact_dict) except RegistryError: raise ValueError("Unsupported artifact type") diff --git a/swarms/artifacts/main.py b/swarms/artifacts/main.py index 4b240b22..8845ada3 100644 --- a/swarms/artifacts/main.py +++ b/swarms/artifacts/main.py @@ -15,8 +15,7 @@ class Artifact(BaseModel): artifact_id: StrictStr = Field(..., description="ID of the artifact") file_name: StrictStr = Field(..., description="Filename of the artifact") relative_path: Optional[StrictStr] = Field( - None, description="Relative path of the artifact" - ) + None, description="Relative path of the artifact") __properties = ["artifact_id", "file_name", "relative_path"] class Config: @@ -49,12 +48,10 @@ class Artifact(BaseModel): if not isinstance(obj, dict): return Artifact.parse_obj(obj) - _obj = Artifact.parse_obj( - { - "artifact_id": obj.get("artifact_id"), - "file_name": obj.get("file_name"), - "relative_path": obj.get("relative_path"), - } - ) + _obj = Artifact.parse_obj({ + "artifact_id": obj.get("artifact_id"), + "file_name": obj.get("file_name"), + "relative_path": obj.get("relative_path"), + }) return _obj diff --git a/swarms/chunkers/__init__.py b/swarms/chunkers/__init__.py index 5e09586b..159e8d5b 100644 --- a/swarms/chunkers/__init__.py +++ b/swarms/chunkers/__init__.py @@ -3,7 +3,6 @@ # from swarms.chunkers.text import TextChunker # from swarms.chunkers.pdf import PdfChunker - # __all__ = [ # "BaseChunker", # "ChunkSeparator", diff --git a/swarms/chunkers/base.py b/swarms/chunkers/base.py index 0fabdcef..d243bd0d 100644 --- a/swarms/chunkers/base.py +++ b/swarms/chunkers/base.py @@ -48,15 +48,13 @@ class BaseChunker(ABC): kw_only=True, ) tokenizer: OpenAITokenizer = field( - default=Factory( - lambda: OpenAITokenizer( - model=OpenAITokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL - ) - ), + default=Factory(lambda: OpenAITokenizer( + model=OpenAITokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL)), kw_only=True, ) max_tokens: int = field( - default=Factory(lambda self: self.tokenizer.max_tokens, takes_self=True), + default=Factory(lambda self: self.tokenizer.max_tokens, + takes_self=True), kw_only=True, ) @@ -66,8 +64,9 @@ class BaseChunker(ABC): return [TextArtifact(c) for c in self._chunk_recursively(text)] def _chunk_recursively( - self, chunk: str, current_separator: Optional[ChunkSeparator] = None - ) -> list[str]: + self, + chunk: str, + current_separator: Optional[ChunkSeparator] = None) -> list[str]: token_count = self.tokenizer.count_tokens(chunk) if token_count <= self.max_tokens: @@ -79,7 +78,8 @@ class BaseChunker(ABC): half_token_count = token_count // 2 if current_separator: - separators = self.separators[self.separators.index(current_separator) :] + separators = self.separators[self.separators. + index(current_separator):] else: separators = self.separators @@ -102,26 +102,19 @@ class BaseChunker(ABC): if separator.is_prefix: first_subchunk = separator.value + separator.value.join( - subchanks[: balance_index + 1] - ) + subchanks[:balance_index + 1]) second_subchunk = separator.value + separator.value.join( - subchanks[balance_index + 1 :] - ) + subchanks[balance_index + 1:]) else: - first_subchunk = ( - separator.value.join(subchanks[: balance_index + 1]) - + separator.value - ) + first_subchunk = (separator.value.join( + subchanks[:balance_index + 1]) + separator.value) second_subchunk = separator.value.join( - subchanks[balance_index + 1 :] - ) + subchanks[balance_index + 1:]) first_subchunk_rec = self._chunk_recursively( - first_subchunk.strip(), separator - ) + first_subchunk.strip(), separator) second_subchunk_rec = self._chunk_recursively( - second_subchunk.strip(), separator - ) + second_subchunk.strip(), separator) if first_subchunk_rec and second_subchunk_rec: return first_subchunk_rec + second_subchunk_rec diff --git a/swarms/chunkers/omni_chunker.py b/swarms/chunkers/omni_chunker.py index 70a11380..c4870e2b 100644 --- a/swarms/chunkers/omni_chunker.py +++ b/swarms/chunkers/omni_chunker.py @@ -76,8 +76,7 @@ class OmniChunker: colored( f"Could not decode file with extension {file_extension}: {e}", "yellow", - ) - ) + )) return "" def chunk_content(self, content: str) -> List[str]: @@ -91,7 +90,7 @@ class OmniChunker: List[str]: The list of chunks. """ return [ - content[i : i + self.chunk_size] + content[i:i + self.chunk_size] for i in range(0, len(content), self.chunk_size) ] @@ -113,5 +112,4 @@ class OmniChunker: {self.metrics()} """, "cyan", - ) - ) + )) diff --git a/swarms/loaders/asana.py b/swarms/loaders/asana.py index dd14cff4..022b685b 100644 --- a/swarms/loaders/asana.py +++ b/swarms/loaders/asana.py @@ -18,9 +18,9 @@ class AsanaReader(BaseReader): self.client = asana.Client.access_token(asana_token) - def load_data( - self, workspace_id: Optional[str] = None, project_id: Optional[str] = None - ) -> List[Document]: + def load_data(self, + workspace_id: Optional[str] = None, + project_id: Optional[str] = None) -> List[Document]: """Load data from the workspace. Args: @@ -31,18 +31,20 @@ class AsanaReader(BaseReader): """ if workspace_id is None and project_id is None: - raise ValueError("Either workspace_id or project_id must be provided") + raise ValueError( + "Either workspace_id or project_id must be provided") if workspace_id is not None and project_id is not None: raise ValueError( - "Only one of workspace_id or project_id should be provided" - ) + "Only one of workspace_id or project_id should be provided") results = [] if workspace_id is not None: - workspace_name = self.client.workspaces.find_by_id(workspace_id)["name"] - projects = self.client.projects.find_all({"workspace": workspace_id}) + workspace_name = self.client.workspaces.find_by_id( + workspace_id)["name"] + projects = self.client.projects.find_all( + {"workspace": workspace_id}) # Case: Only project_id is provided else: # since we've handled the other cases, this means project_id is not None @@ -50,54 +52,58 @@ class AsanaReader(BaseReader): workspace_name = projects[0]["workspace"]["name"] for project in projects: - tasks = self.client.tasks.find_all( - { - "project": project["gid"], - "opt_fields": "name,notes,completed,completed_at,completed_by,assignee,followers,custom_fields", - } - ) + tasks = self.client.tasks.find_all({ + "project": + project["gid"], + "opt_fields": + "name,notes,completed,completed_at,completed_by,assignee,followers,custom_fields", + }) for task in tasks: - stories = self.client.tasks.stories(task["gid"], opt_fields="type,text") - comments = "\n".join( - [ - story["text"] - for story in stories - if story.get("type") == "comment" and "text" in story - ] - ) + stories = self.client.tasks.stories(task["gid"], + opt_fields="type,text") + comments = "\n".join([ + story["text"] + for story in stories + if story.get("type") == "comment" and "text" in story + ]) task_metadata = { - "task_id": task.get("gid", ""), - "name": task.get("name", ""), + "task_id": + task.get("gid", ""), + "name": + task.get("name", ""), "assignee": (task.get("assignee") or {}).get("name", ""), - "completed_on": task.get("completed_at", ""), - "completed_by": (task.get("completed_by") or {}).get("name", ""), - "project_name": project.get("name", ""), + "completed_on": + task.get("completed_at", ""), + "completed_by": (task.get("completed_by") or + {}).get("name", ""), + "project_name": + project.get("name", ""), "custom_fields": [ i["display_value"] for i in task.get("custom_fields") if task.get("custom_fields") is not None ], - "workspace_name": workspace_name, - "url": f"https://app.asana.com/0/{project['gid']}/{task['gid']}", + "workspace_name": + workspace_name, + "url": + f"https://app.asana.com/0/{project['gid']}/{task['gid']}", } if task.get("followers") is not None: task_metadata["followers"] = [ - i.get("name") for i in task.get("followers") if "name" in i + i.get("name") + for i in task.get("followers") + if "name" in i ] else: task_metadata["followers"] = [] results.append( Document( - text=task.get("name", "") - + " " - + task.get("notes", "") - + " " - + comments, + text=task.get("name", "") + " " + + task.get("notes", "") + " " + comments, extra_info=task_metadata, - ) - ) + )) return results diff --git a/swarms/loaders/base.py b/swarms/loaders/base.py index a59a93e2..2d5c7cdb 100644 --- a/swarms/loaders/base.py +++ b/swarms/loaders/base.py @@ -15,7 +15,6 @@ if TYPE_CHECKING: from haystack.schema import Document as HaystackDocument from semantic_kernel.memory.memory_record import MemoryRecord - #### DEFAULT_TEXT_NODE_TMPL = "{metadata_str}\n\n{content}" DEFAULT_METADATA_TMPL = "{key}: {value}" @@ -48,7 +47,8 @@ class BaseComponent(BaseModel): # TODO: return type here not supported by current mypy version @classmethod - def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self: # type: ignore + def from_dict(cls, data: Dict[str, Any], + **kwargs: Any) -> Self: # type: ignore if isinstance(kwargs, dict): data.update(kwargs) @@ -119,13 +119,10 @@ class BaseNode(BaseComponent): class Config: allow_population_by_field_name = True - id_: str = Field( - default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node." - ) + id_: str = Field(default_factory=lambda: str(uuid.uuid4()), + description="Unique ID of the node.") embedding: Optional[List[float]] = Field( - default=None, description="Embedding of the node." - ) - + default=None, description="Embedding of the node.") """" metadata fields - injected as part of the text shown to LLMs as context @@ -140,7 +137,8 @@ class BaseNode(BaseComponent): ) excluded_embed_metadata_keys: List[str] = Field( default_factory=list, - description="Metadata keys that are excluded from text for the embed model.", + description= + "Metadata keys that are excluded from text for the embed model.", ) excluded_llm_metadata_keys: List[str] = Field( default_factory=list, @@ -158,7 +156,8 @@ class BaseNode(BaseComponent): """Get Object type.""" @abstractmethod - def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str: + def get_content(self, + metadata_mode: MetadataMode = MetadataMode.ALL) -> str: """Get object content.""" @abstractmethod @@ -189,7 +188,8 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.SOURCE] if isinstance(relation, list): - raise ValueError("Source object must be a single RelatedNodeInfo object") + raise ValueError( + "Source object must be a single RelatedNodeInfo object") return relation @property @@ -200,7 +200,8 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.PREVIOUS] if not isinstance(relation, RelatedNodeInfo): - raise ValueError("Previous object must be a single RelatedNodeInfo object") + raise ValueError( + "Previous object must be a single RelatedNodeInfo object") return relation @property @@ -211,7 +212,8 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.NEXT] if not isinstance(relation, RelatedNodeInfo): - raise ValueError("Next object must be a single RelatedNodeInfo object") + raise ValueError( + "Next object must be a single RelatedNodeInfo object") return relation @property @@ -222,7 +224,8 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.PARENT] if not isinstance(relation, RelatedNodeInfo): - raise ValueError("Parent object must be a single RelatedNodeInfo object") + raise ValueError( + "Parent object must be a single RelatedNodeInfo object") return relation @property @@ -233,7 +236,8 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.CHILD] if not isinstance(relation, list): - raise ValueError("Child objects must be a list of RelatedNodeInfo objects.") + raise ValueError( + "Child objects must be a list of RelatedNodeInfo objects.") return relation @property @@ -250,12 +254,10 @@ class BaseNode(BaseComponent): return self.metadata def __str__(self) -> str: - source_text_truncated = truncate_text( - self.get_content().strip(), TRUNCATE_LENGTH - ) - source_text_wrapped = textwrap.fill( - f"Text: {source_text_truncated}\n", width=WRAP_WIDTH - ) + source_text_truncated = truncate_text(self.get_content().strip(), + TRUNCATE_LENGTH) + source_text_wrapped = textwrap.fill(f"Text: {source_text_truncated}\n", + width=WRAP_WIDTH) return f"Node ID: {self.node_id}\n{source_text_wrapped}" def get_embedding(self) -> List[float]: @@ -281,28 +283,23 @@ class BaseNode(BaseComponent): class TextNode(BaseNode): text: str = Field(default="", description="Text content of the node.") start_char_idx: Optional[int] = Field( - default=None, description="Start char index of the node." - ) + default=None, description="Start char index of the node.") end_char_idx: Optional[int] = Field( - default=None, description="End char index of the node." - ) + default=None, description="End char index of the node.") text_template: str = Field( default=DEFAULT_TEXT_NODE_TMPL, - description=( - "Template for how text is formatted, with {content} and " - "{metadata_str} placeholders." - ), + description=("Template for how text is formatted, with {content} and " + "{metadata_str} placeholders."), ) metadata_template: str = Field( default=DEFAULT_METADATA_TMPL, - description=( - "Template for how metadata is formatted, with {key} and " - "{value} placeholders." - ), + description=("Template for how metadata is formatted, with {key} and " + "{value} placeholders."), ) metadata_seperator: str = Field( default="\n", - description="Separator between metadata fields when converting to string.", + description= + "Separator between metadata fields when converting to string.", ) @classmethod @@ -316,8 +313,7 @@ class TextNode(BaseNode): metadata = values.get("metadata", {}) doc_identity = str(text) + str(metadata) values["hash"] = str( - sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest() - ) + sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest()) return values @classmethod @@ -325,15 +321,15 @@ class TextNode(BaseNode): """Get Object type.""" return ObjectType.TEXT - def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + def get_content(self, + metadata_mode: MetadataMode = MetadataMode.NONE) -> str: """Get object content.""" metadata_str = self.get_metadata_str(mode=metadata_mode).strip() if not metadata_str: return self.text - return self.text_template.format( - content=self.text, metadata_str=metadata_str - ).strip() + return self.text_template.format(content=self.text, + metadata_str=metadata_str).strip() def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str: """Metadata info string.""" @@ -350,13 +346,11 @@ class TextNode(BaseNode): if key in usable_metadata_keys: usable_metadata_keys.remove(key) - return self.metadata_seperator.join( - [ - self.metadata_template.format(key=key, value=str(value)) - for key, value in self.metadata.items() - if key in usable_metadata_keys - ] - ) + return self.metadata_seperator.join([ + self.metadata_template.format(key=key, value=str(value)) + for key, value in self.metadata.items() + if key in usable_metadata_keys + ]) def set_content(self, value: str) -> None: """Set the content of the node.""" @@ -480,7 +474,8 @@ class NodeWithScore(BaseComponent): else: raise ValueError("Node must be a TextNode to get text.") - def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + def get_content(self, + metadata_mode: MetadataMode = MetadataMode.NONE) -> str: return self.node.get_content(metadata_mode=metadata_mode) def get_embedding(self) -> List[float]: @@ -517,12 +512,10 @@ class Document(TextNode): return self.id_ def __str__(self) -> str: - source_text_truncated = truncate_text( - self.get_content().strip(), TRUNCATE_LENGTH - ) - source_text_wrapped = textwrap.fill( - f"Text: {source_text_truncated}\n", width=WRAP_WIDTH - ) + source_text_truncated = truncate_text(self.get_content().strip(), + TRUNCATE_LENGTH) + source_text_wrapped = textwrap.fill(f"Text: {source_text_truncated}\n", + width=WRAP_WIDTH) return f"Doc ID: {self.doc_id}\n{source_text_wrapped}" def get_doc_id(self) -> str: @@ -538,22 +531,27 @@ class Document(TextNode): """Convert struct to Haystack document format.""" from haystack.schema import Document as HaystackDocument - return HaystackDocument( - content=self.text, meta=self.metadata, embedding=self.embedding, id=self.id_ - ) + return HaystackDocument(content=self.text, + meta=self.metadata, + embedding=self.embedding, + id=self.id_) @classmethod def from_haystack_format(cls, doc: "HaystackDocument") -> "Document": """Convert struct from Haystack document format.""" - return cls( - text=doc.content, metadata=doc.meta, embedding=doc.embedding, id_=doc.id - ) + return cls(text=doc.content, + metadata=doc.meta, + embedding=doc.embedding, + id_=doc.id) def to_embedchain_format(self) -> Dict[str, Any]: """Convert struct to EmbedChain document format.""" return { "doc_id": self.id_, - "data": {"content": self.text, "meta_data": self.metadata}, + "data": { + "content": self.text, + "meta_data": self.metadata + }, } @classmethod @@ -583,7 +581,8 @@ class Document(TextNode): return cls( text=doc._text, metadata={"additional_metadata": doc._additional_metadata}, - embedding=doc._embedding.tolist() if doc._embedding is not None else None, + embedding=doc._embedding.tolist() + if doc._embedding is not None else None, id_=doc._id, ) @@ -591,7 +590,10 @@ class Document(TextNode): def example(cls) -> "Document": return Document( text=SAMPLE_TEXT, - metadata={"filename": "README.md", "category": "codebase"}, + metadata={ + "filename": "README.md", + "category": "codebase" + }, ) @classmethod diff --git a/swarms/memory/base.py b/swarms/memory/base.py index 7f71c4b9..7c08af6f 100644 --- a/swarms/memory/base.py +++ b/swarms/memory/base.py @@ -30,32 +30,25 @@ class BaseVectorStore(ABC): embedding_driver: Any futures_executor: futures.Executor = field( - default=Factory(lambda: futures.ThreadPoolExecutor()), kw_only=True - ) - - def upsert_text_artifacts( - self, - artifacts: dict[str, list[TextArtifact]], - meta: Optional[dict] = None, - **kwargs - ) -> None: - execute_futures_dict( - { - namespace: self.futures_executor.submit( - self.upsert_text_artifact, a, namespace, meta, **kwargs - ) - for namespace, artifact_list in artifacts.items() - for a in artifact_list - } - ) - - def upsert_text_artifact( - self, - artifact: TextArtifact, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs - ) -> str: + default=Factory(lambda: futures.ThreadPoolExecutor()), kw_only=True) + + def upsert_text_artifacts(self, + artifacts: dict[str, list[TextArtifact]], + meta: Optional[dict] = None, + **kwargs) -> None: + execute_futures_dict({ + namespace: + self.futures_executor.submit(self.upsert_text_artifact, a, + namespace, meta, **kwargs) + for namespace, artifact_list in artifacts.items() + for a in artifact_list + }) + + def upsert_text_artifact(self, + artifact: TextArtifact, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs) -> str: if not meta: meta = {} @@ -66,39 +59,37 @@ class BaseVectorStore(ABC): else: vector = artifact.generate_embedding(self.embedding_driver) - return self.upsert_vector( - vector, vector_id=artifact.id, namespace=namespace, meta=meta, **kwargs - ) - - def upsert_text( - self, - string: str, - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs - ) -> str: - return self.upsert_vector( - self.embedding_driver.embed_string(string), - vector_id=vector_id, - namespace=namespace, - meta=meta if meta else {}, - **kwargs - ) + return self.upsert_vector(vector, + vector_id=artifact.id, + namespace=namespace, + meta=meta, + **kwargs) + + def upsert_text(self, + string: str, + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs) -> str: + return self.upsert_vector(self.embedding_driver.embed_string(string), + vector_id=vector_id, + namespace=namespace, + meta=meta if meta else {}, + **kwargs) @abstractmethod - def upsert_vector( - self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs - ) -> str: + def upsert_vector(self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs) -> str: ... @abstractmethod - def load_entry(self, vector_id: str, namespace: Optional[str] = None) -> Entry: + def load_entry(self, + vector_id: str, + namespace: Optional[str] = None) -> Entry: ... @abstractmethod @@ -106,12 +97,10 @@ class BaseVectorStore(ABC): ... @abstractmethod - def query( - self, - query: str, - count: Optional[int] = None, - namespace: Optional[str] = None, - include_vectors: bool = False, - **kwargs - ) -> list[QueryResult]: + def query(self, + query: str, + count: Optional[int] = None, + namespace: Optional[str] = None, + include_vectors: bool = False, + **kwargs) -> list[QueryResult]: ... diff --git a/swarms/memory/chroma.py b/swarms/memory/chroma.py index 67ba4cb2..080245fb 100644 --- a/swarms/memory/chroma.py +++ b/swarms/memory/chroma.py @@ -80,10 +80,8 @@ class Chroma(VectorStore): import chromadb import chromadb.config except ImportError: - raise ImportError( - "Could not import chromadb python package. " - "Please install it with `pip install chromadb`." - ) + raise ImportError("Could not import chromadb python package. " + "Please install it with `pip install chromadb`.") if client is not None: self._client_settings = client_settings @@ -94,8 +92,7 @@ class Chroma(VectorStore): # If client_settings is provided with persist_directory specified, # then it is "in-memory and persisting to disk" mode. client_settings.persist_directory = ( - persist_directory or client_settings.persist_directory - ) + persist_directory or client_settings.persist_directory) if client_settings.persist_directory is not None: # Maintain backwards compatibility with chromadb < 0.4.0 major, minor, _ = chromadb.__version__.split(".") @@ -108,25 +105,23 @@ class Chroma(VectorStore): major, minor, _ = chromadb.__version__.split(".") if int(major) == 0 and int(minor) < 4: _client_settings = chromadb.config.Settings( - chroma_db_impl="duckdb+parquet", - ) + chroma_db_impl="duckdb+parquet",) else: - _client_settings = chromadb.config.Settings(is_persistent=True) + _client_settings = chromadb.config.Settings( + is_persistent=True) _client_settings.persist_directory = persist_directory else: _client_settings = chromadb.config.Settings() self._client_settings = _client_settings self._client = chromadb.Client(_client_settings) - self._persist_directory = ( - _client_settings.persist_directory or persist_directory - ) + self._persist_directory = (_client_settings.persist_directory or + persist_directory) self._embedding_function = embedding_function self._collection = self._client.get_or_create_collection( name=collection_name, embedding_function=self._embedding_function.embed_documents - if self._embedding_function is not None - else None, + if self._embedding_function is not None else None, metadata=collection_metadata, ) self.override_relevance_score_fn = relevance_score_fn @@ -149,10 +144,8 @@ class Chroma(VectorStore): try: import chromadb # noqa: F401 except ImportError: - raise ValueError( - "Could not import chromadb python package. " - "Please install it with `pip install chromadb`." - ) + raise ValueError("Could not import chromadb python package. " + "Please install it with `pip install chromadb`.") return self._collection.query( query_texts=query_texts, query_embeddings=query_embeddings, @@ -202,9 +195,9 @@ class Chroma(VectorStore): if non_empty_ids: metadatas = [metadatas[idx] for idx in non_empty_ids] texts_with_metadatas = [texts[idx] for idx in non_empty_ids] - embeddings_with_metadatas = ( - [embeddings[idx] for idx in non_empty_ids] if embeddings else None - ) + embeddings_with_metadatas = ([ + embeddings[idx] for idx in non_empty_ids + ] if embeddings else None) ids_with_metadata = [ids[idx] for idx in non_empty_ids] try: self._collection.upsert( @@ -225,8 +218,7 @@ class Chroma(VectorStore): if empty_ids: texts_without_metadatas = [texts[j] for j in empty_ids] embeddings_without_metadatas = ( - [embeddings[j] for j in empty_ids] if embeddings else None - ) + [embeddings[j] for j in empty_ids] if embeddings else None) ids_without_metadatas = [ids[j] for j in empty_ids] self._collection.upsert( embeddings=embeddings_without_metadatas, @@ -258,7 +250,9 @@ class Chroma(VectorStore): Returns: List[Document]: List of documents most similar to the query text. """ - docs_and_scores = self.similarity_search_with_score(query, k, filter=filter) + docs_and_scores = self.similarity_search_with_score(query, + k, + filter=filter) return [doc for doc, _ in docs_and_scores] def similarity_search_by_vector( @@ -381,8 +375,7 @@ class Chroma(VectorStore): raise ValueError( "No supported normalization function" f" for distance metric of type: {distance}." - "Consider providing relevance_score_fn to Chroma constructor." - ) + "Consider providing relevance_score_fn to Chroma constructor.") def max_marginal_relevance_search_by_vector( self, @@ -428,7 +421,9 @@ class Chroma(VectorStore): candidates = _results_to_docs(results) - selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected] + selected_results = [ + r for i, r in enumerate(candidates) if i in mmr_selected + ] return selected_results def max_marginal_relevance_search( @@ -523,10 +518,8 @@ class Chroma(VectorStore): It will also be called automatically when the object is destroyed. """ if self._persist_directory is None: - raise ValueError( - "You must specify a persist_directory on" - "creation to persist the collection." - ) + raise ValueError("You must specify a persist_directory on" + "creation to persist the collection.") import chromadb # Maintain backwards compatibility with chromadb < 0.4.0 @@ -543,7 +536,8 @@ class Chroma(VectorStore): """ return self.update_documents([document_id], [document]) - def update_documents(self, ids: List[str], documents: List[Document]) -> None: + def update_documents(self, ids: List[str], + documents: List[Document]) -> None: """Update a document in the collection. Args: @@ -558,17 +552,16 @@ class Chroma(VectorStore): ) embeddings = self._embedding_function.embed_documents(text) - if hasattr( - self._collection._client, "max_batch_size" - ): # for Chroma 0.4.10 and above + if hasattr(self._collection._client, + "max_batch_size"): # for Chroma 0.4.10 and above from chromadb.utils.batch_utils import create_batches for batch in create_batches( - api=self._collection._client, - ids=ids, - metadatas=metadata, - documents=text, - embeddings=embeddings, + api=self._collection._client, + ids=ids, + metadatas=metadata, + documents=text, + embeddings=embeddings, ): self._collection.update( ids=batch[0], @@ -628,16 +621,15 @@ class Chroma(VectorStore): ) if ids is None: ids = [str(uuid.uuid1()) for _ in texts] - if hasattr( - chroma_collection._client, "max_batch_size" - ): # for Chroma 0.4.10 and above + if hasattr(chroma_collection._client, + "max_batch_size"): # for Chroma 0.4.10 and above from chromadb.utils.batch_utils import create_batches for batch in create_batches( - api=chroma_collection._client, - ids=ids, - metadatas=metadatas, - documents=texts, + api=chroma_collection._client, + ids=ids, + metadatas=metadatas, + documents=texts, ): chroma_collection.add_texts( texts=batch[3] if batch[3] else [], @@ -645,7 +637,9 @@ class Chroma(VectorStore): ids=batch[0], ) else: - chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) + chroma_collection.add_texts(texts=texts, + metadatas=metadatas, + ids=ids) return chroma_collection @classmethod diff --git a/swarms/memory/cosine_similarity.py b/swarms/memory/cosine_similarity.py index 99d47368..9b183834 100644 --- a/swarms/memory/cosine_similarity.py +++ b/swarms/memory/cosine_similarity.py @@ -19,8 +19,7 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: if X.shape[1] != Y.shape[1]: raise ValueError( f"Number of columns in X and Y must be the same. X has shape {X.shape} " - f"and Y has shape {Y.shape}." - ) + f"and Y has shape {Y.shape}.") try: import simsimd as simd @@ -33,8 +32,7 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: except ImportError: logger.info( "Unable to import simsimd, defaulting to NumPy implementation. If you want " - "to use simsimd please install with `pip install simsimd`." - ) + "to use simsimd please install with `pip install simsimd`.") X_norm = np.linalg.norm(X, axis=1) Y_norm = np.linalg.norm(Y, axis=1) # Ignore divide by zero errors run time warnings as those are handled below. diff --git a/swarms/memory/db.py b/swarms/memory/db.py index 9f23b59f..8e6bad12 100644 --- a/swarms/memory/db.py +++ b/swarms/memory/db.py @@ -27,6 +27,7 @@ class NotFoundException(Exception): class TaskDB(ABC): + async def create_task( self, input: Optional[str], @@ -67,9 +68,9 @@ class TaskDB(ABC): async def list_tasks(self) -> List[Task]: raise NotImplementedError - async def list_steps( - self, task_id: str, status: Optional[Status] = None - ) -> List[Step]: + async def list_steps(self, + task_id: str, + status: Optional[Status] = None) -> List[Step]: raise NotImplementedError @@ -136,8 +137,8 @@ class InMemoryTaskDB(TaskDB): async def get_artifact(self, task_id: str, artifact_id: str) -> Artifact: task = await self.get_task(task_id) artifact = next( - filter(lambda a: a.artifact_id == artifact_id, task.artifacts), None - ) + filter(lambda a: a.artifact_id == artifact_id, task.artifacts), + None) if not artifact: raise NotFoundException("Artifact", artifact_id) return artifact @@ -150,9 +151,9 @@ class InMemoryTaskDB(TaskDB): step_id: Optional[str] = None, ) -> Artifact: artifact_id = str(uuid.uuid4()) - artifact = Artifact( - artifact_id=artifact_id, file_name=file_name, relative_path=relative_path - ) + artifact = Artifact(artifact_id=artifact_id, + file_name=file_name, + relative_path=relative_path) task = await self.get_task(task_id) task.artifacts.append(artifact) @@ -165,9 +166,9 @@ class InMemoryTaskDB(TaskDB): async def list_tasks(self) -> List[Task]: return [task for task in self._tasks.values()] - async def list_steps( - self, task_id: str, status: Optional[Status] = None - ) -> List[Step]: + async def list_steps(self, + task_id: str, + status: Optional[Status] = None) -> List[Step]: task = await self.get_task(task_id) steps = task.steps if status: diff --git a/swarms/memory/ocean.py b/swarms/memory/ocean.py index da58c81c..339c3596 100644 --- a/swarms/memory/ocean.py +++ b/swarms/memory/ocean.py @@ -63,8 +63,7 @@ class OceanDB: try: embedding_function = MultiModalEmbeddingFunction(modality=modality) collection = self.client.create_collection( - collection_name, embedding_function=embedding_function - ) + collection_name, embedding_function=embedding_function) return collection except Exception as e: logging.error(f"Failed to create collection. Error {e}") @@ -91,7 +90,8 @@ class OceanDB: try: return collection.add(documents=[document], ids=[id]) except Exception as e: - logging.error(f"Failed to append document to the collection. Error {e}") + logging.error( + f"Failed to append document to the collection. Error {e}") raise def add_documents(self, collection, documents: List[str], ids: List[str]): @@ -137,7 +137,8 @@ class OceanDB: the results of the query """ try: - results = collection.query(query_texts=query_texts, n_results=n_results) + results = collection.query(query_texts=query_texts, + n_results=n_results) return results except Exception as e: logging.error(f"Failed to query the collection. Error {e}") diff --git a/swarms/memory/pg.py b/swarms/memory/pg.py index bd768459..09534cac 100644 --- a/swarms/memory/pg.py +++ b/swarms/memory/pg.py @@ -88,12 +88,12 @@ class PgVectorVectorStore(BaseVectorStore): create_engine_params: dict = field(factory=dict, kw_only=True) engine: Optional[Engine] = field(default=None, kw_only=True) table_name: str = field(kw_only=True) - _model: any = field( - default=Factory(lambda self: self.default_vector_model(), takes_self=True) - ) + _model: any = field(default=Factory( + lambda self: self.default_vector_model(), takes_self=True)) @connection_string.validator - def validate_connection_string(self, _, connection_string: Optional[str]) -> None: + def validate_connection_string(self, _, + connection_string: Optional[str]) -> None: # If an engine is provided, the connection string is not used. if self.engine is not None: return @@ -122,9 +122,8 @@ class PgVectorVectorStore(BaseVectorStore): If not, a connection string is used to create a new database connection here. """ if self.engine is None: - self.engine = create_engine( - self.connection_string, **self.create_engine_params - ) + self.engine = create_engine(self.connection_string, + **self.create_engine_params) def setup( self, @@ -142,14 +141,12 @@ class PgVectorVectorStore(BaseVectorStore): if create_schema: self._model.metadata.create_all(self.engine) - def upsert_vector( - self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs - ) -> str: + def upsert_vector(self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs) -> str: """Inserts or updates a vector in the collection.""" with Session(self.engine) as session: obj = self._model( @@ -164,9 +161,9 @@ class PgVectorVectorStore(BaseVectorStore): return str(obj.id) - def load_entry( - self, vector_id: str, namespace: Optional[str] = None - ) -> BaseVectorStore.Entry: + def load_entry(self, + vector_id: str, + namespace: Optional[str] = None) -> BaseVectorStore.Entry: """Retrieves a specific vector entry from the collection based on its identifier and optional namespace.""" with Session(self.engine) as session: result = session.get(self._model, vector_id) @@ -179,8 +176,8 @@ class PgVectorVectorStore(BaseVectorStore): ) def load_entries( - self, namespace: Optional[str] = None - ) -> list[BaseVectorStore.Entry]: + self, + namespace: Optional[str] = None) -> list[BaseVectorStore.Entry]: """Retrieves all vector entries from the collection, optionally filtering to only those that match the provided namespace. """ @@ -197,19 +194,16 @@ class PgVectorVectorStore(BaseVectorStore): vector=result.vector, namespace=result.namespace, meta=result.meta, - ) - for result in results + ) for result in results ] - def query( - self, - query: str, - count: Optional[int] = BaseVectorStore.DEFAULT_QUERY_COUNT, - namespace: Optional[str] = None, - include_vectors: bool = False, - distance_metric: str = "cosine_distance", - **kwargs - ) -> list[BaseVectorStore.QueryResult]: + def query(self, + query: str, + count: Optional[int] = BaseVectorStore.DEFAULT_QUERY_COUNT, + namespace: Optional[str] = None, + include_vectors: bool = False, + distance_metric: str = "cosine_distance", + **kwargs) -> list[BaseVectorStore.QueryResult]: """Performs a search on the collection to find vectors similar to the provided input vector, optionally filtering to only those that match the provided namespace. """ @@ -245,8 +239,7 @@ class PgVectorVectorStore(BaseVectorStore): score=result[1], meta=result[0].meta, namespace=result[0].namespace, - ) - for result in results + ) for result in results ] def default_vector_model(self) -> any: diff --git a/swarms/memory/pinecone.py b/swarms/memory/pinecone.py index 2374f12a..0269aa38 100644 --- a/swarms/memory/pinecone.py +++ b/swarms/memory/pinecone.py @@ -102,14 +102,12 @@ class PineconeVectorStoreStore(BaseVector): self.index = pinecone.Index(self.index_name) - def upsert_vector( - self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs - ) -> str: + def upsert_vector(self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs) -> str: """Upsert vector""" vector_id = vector_id if vector_id else str_to_hash(str(vector)) @@ -120,10 +118,12 @@ class PineconeVectorStoreStore(BaseVector): return vector_id def load_entry( - self, vector_id: str, namespace: Optional[str] = None - ) -> Optional[BaseVector.Entry]: + self, + vector_id: str, + namespace: Optional[str] = None) -> Optional[BaseVector.Entry]: """Load entry""" - result = self.index.fetch(ids=[vector_id], namespace=namespace).to_dict() + result = self.index.fetch(ids=[vector_id], + namespace=namespace).to_dict() vectors = list(result["vectors"].values()) if len(vectors) > 0: @@ -138,7 +138,8 @@ class PineconeVectorStoreStore(BaseVector): else: return None - def load_entries(self, namespace: Optional[str] = None) -> list[BaseVector.Entry]: + def load_entries(self, + namespace: Optional[str] = None) -> list[BaseVector.Entry]: """Load entries""" # This is a hacky way to query up to 10,000 values from Pinecone. Waiting on an official API for fetching # all values from a namespace: @@ -157,20 +158,18 @@ class PineconeVectorStoreStore(BaseVector): vector=r["values"], meta=r["metadata"], namespace=results["namespace"], - ) - for r in results["matches"] + ) for r in results["matches"] ] def query( - self, - query: str, - count: Optional[int] = None, - namespace: Optional[str] = None, - include_vectors: bool = False, - # PineconeVectorStoreStorageDriver-specific params: - include_metadata=True, - **kwargs - ) -> list[BaseVector.QueryResult]: + self, + query: str, + count: Optional[int] = None, + namespace: Optional[str] = None, + include_vectors: bool = False, + # PineconeVectorStoreStorageDriver-specific params: + include_metadata=True, + **kwargs) -> list[BaseVector.QueryResult]: """Query vectors""" vector = self.embedding_driver.embed_string(query) @@ -190,12 +189,14 @@ class PineconeVectorStoreStore(BaseVector): score=r["score"], meta=r["metadata"], namespace=results["namespace"], - ) - for r in results["matches"] + ) for r in results["matches"] ] def create_index(self, name: str, **kwargs) -> None: """Create index""" - params = {"name": name, "dimension": self.embedding_driver.dimensions} | kwargs + params = { + "name": name, + "dimension": self.embedding_driver.dimensions + } | kwargs pinecone.create_index(**params) diff --git a/swarms/memory/schemas.py b/swarms/memory/schemas.py index bbc71bc2..ce54208d 100644 --- a/swarms/memory/schemas.py +++ b/swarms/memory/schemas.py @@ -20,9 +20,9 @@ class Artifact(BaseModel): description="Id of the artifact", example="b225e278-8b4c-4f99-a696-8facf19f0e56", ) - file_name: str = Field( - ..., description="Filename of the artifact", example="main.py" - ) + file_name: str = Field(..., + description="Filename of the artifact", + example="main.py") relative_path: Optional[str] = Field( None, description="Relative path of the artifact in the agent's workspace", @@ -50,7 +50,8 @@ class StepInput(BaseModel): class StepOutput(BaseModel): __root__: Any = Field( ..., - description="Output that the task step has produced. Any value is allowed.", + description= + "Output that the task step has produced. Any value is allowed.", example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}', ) @@ -81,9 +82,9 @@ class Task(TaskRequestBody): class StepRequestBody(BaseModel): - input: Optional[str] = Field( - None, description="Input prompt for the step.", example="Washington" - ) + input: Optional[str] = Field(None, + description="Input prompt for the step.", + example="Washington") additional_input: Optional[StepInput] = None @@ -104,22 +105,19 @@ class Step(StepRequestBody): description="The ID of the task step.", example="6bb1801a-fd80-45e8-899a-4dd723cc602e", ) - name: Optional[str] = Field( - None, description="The name of the task step.", example="Write to file" - ) + name: Optional[str] = Field(None, + description="The name of the task step.", + example="Write to file") status: Status = Field(..., description="The status of the task step.") output: Optional[str] = Field( None, description="Output of the task step.", - example=( - "I am going to use the write_to_file command and write Washington to a file" - " called output.txt best_score: best_score = equation_score idx_to_add = i @@ -57,8 +56,8 @@ def maximal_marginal_relevance( def filter_complex_metadata( documents: List[Document], *, - allowed_types: Tuple[Type, ...] = (str, bool, int, float) -) -> List[Document]: + allowed_types: Tuple[Type, + ...] = (str, bool, int, float)) -> List[Document]: """Filter out metadata types that are not supported for a vector store.""" updated_documents = [] for document in documents: diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 1f9ae052..6f6ea8ba 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -9,7 +9,6 @@ from swarms.models.huggingface import HuggingfaceLLM from swarms.models.wizard_storytelling import WizardLLMStoryTeller from swarms.models.mpt import MPT7B - # MultiModal Models from swarms.models.idefics import Idefics from swarms.models.kosmos_two import Kosmos @@ -27,7 +26,6 @@ import sys log_file = open("errors.txt", "w") sys.stderr = log_file - __all__ = [ "Anthropic", "Petals", diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 30ec22ce..634fa030 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -41,21 +41,24 @@ def xor_args(*arg_groups: Tuple[str, ...]) -> Callable: """Validate specified keyword args are mutually exclusive.""" def decorator(func: Callable) -> Callable: + @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: """Validate exactly one arg in each group is not None.""" counts = [ - sum(1 for arg in arg_group if kwargs.get(arg) is not None) + sum(1 + for arg in arg_group + if kwargs.get(arg) is not None) for arg_group in arg_groups ] invalid_groups = [i for i, count in enumerate(counts) if count != 1] if invalid_groups: - invalid_group_names = [", ".join(arg_groups[i]) for i in invalid_groups] - raise ValueError( - "Exactly one argument in each of the following" - " groups must be defined:" - f" {', '.join(invalid_group_names)}" - ) + invalid_group_names = [ + ", ".join(arg_groups[i]) for i in invalid_groups + ] + raise ValueError("Exactly one argument in each of the following" + " groups must be defined:" + f" {', '.join(invalid_group_names)}") return func(*args, **kwargs) return wrapper @@ -105,9 +108,10 @@ def mock_now(dt_value): # type: ignore datetime.datetime = real_datetime -def guard_import( - module_name: str, *, pip_name: Optional[str] = None, package: Optional[str] = None -) -> Any: +def guard_import(module_name: str, + *, + pip_name: Optional[str] = None, + package: Optional[str] = None) -> Any: """Dynamically imports a module and raises a helpful exception if the module is not installed.""" try: @@ -115,8 +119,7 @@ def guard_import( except ImportError: raise ImportError( f"Could not import {module_name} python package. " - f"Please install it with `pip install {pip_name or module_name}`." - ) + f"Please install it with `pip install {pip_name or module_name}`.") return module @@ -132,23 +135,19 @@ def check_package_version( if lt_version is not None and imported_version >= parse(lt_version): raise ValueError( f"Expected {package} version to be < {lt_version}. Received " - f"{imported_version}." - ) + f"{imported_version}.") if lte_version is not None and imported_version > parse(lte_version): raise ValueError( f"Expected {package} version to be <= {lte_version}. Received " - f"{imported_version}." - ) + f"{imported_version}.") if gt_version is not None and imported_version <= parse(gt_version): raise ValueError( f"Expected {package} version to be > {gt_version}. Received " - f"{imported_version}." - ) + f"{imported_version}.") if gte_version is not None and imported_version < parse(gte_version): raise ValueError( f"Expected {package} version to be >= {gte_version}. Received " - f"{imported_version}." - ) + f"{imported_version}.") def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]: @@ -180,19 +179,17 @@ def build_extra_kwargs( if field_name in extra_kwargs: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: - warnings.warn( - f"""WARNING! {field_name} is not default parameter. + warnings.warn(f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""" - ) + Please confirm that {field_name} is what you intended.""") extra_kwargs[field_name] = values.pop(field_name) - invalid_model_kwargs = all_required_field_names.intersection(extra_kwargs.keys()) + invalid_model_kwargs = all_required_field_names.intersection( + extra_kwargs.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " - "Instead they were passed in as part of `model_kwargs` parameter." - ) + "Instead they were passed in as part of `model_kwargs` parameter.") return extra_kwargs @@ -241,17 +238,16 @@ class _AnthropicCommon(BaseLanguageModel): def build_extra(cls, values: Dict) -> Dict: extra = values.get("model_kwargs", {}) all_required_field_names = get_pydantic_field_names(cls) - values["model_kwargs"] = build_extra_kwargs( - extra, values, all_required_field_names - ) + values["model_kwargs"] = build_extra_kwargs(extra, values, + all_required_field_names) return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["anthropic_api_key"] = convert_to_secret_str( - get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY") - ) + get_from_dict_or_env(values, "anthropic_api_key", + "ANTHROPIC_API_KEY")) # Get custom api url from environment. values["anthropic_api_url"] = get_from_dict_or_env( values, @@ -281,8 +277,7 @@ class _AnthropicCommon(BaseLanguageModel): except ImportError: raise ImportError( "Could not import anthropic python package. " - "Please it install it with `pip install anthropic`." - ) + "Please it install it with `pip install anthropic`.") return values @property @@ -305,7 +300,8 @@ class _AnthropicCommon(BaseLanguageModel): """Get the identifying parameters.""" return {**{}, **self._default_params} - def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]: + def _get_anthropic_stop(self, + stop: Optional[List[str]] = None) -> List[str]: if not self.HUMAN_PROMPT or not self.AI_PROMPT: raise NameError("Please ensure the anthropic package is loaded") @@ -372,7 +368,8 @@ class Anthropic(LLM, _AnthropicCommon): return prompt # Already wrapped. # Guard against common errors in specifying wrong number of newlines. - corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt) + corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, + prompt) if n_subs == 1: return corrected_prompt @@ -405,9 +402,10 @@ class Anthropic(LLM, _AnthropicCommon): """ if self.streaming: completion = "" - for chunk in self._stream( - prompt=prompt, stop=stop, run_manager=run_manager, **kwargs - ): + for chunk in self._stream(prompt=prompt, + stop=stop, + run_manager=run_manager, + **kwargs): completion += chunk.text return completion @@ -433,9 +431,10 @@ class Anthropic(LLM, _AnthropicCommon): """Call out to Anthropic's completion endpoint asynchronously.""" if self.streaming: completion = "" - async for chunk in self._astream( - prompt=prompt, stop=stop, run_manager=run_manager, **kwargs - ): + async for chunk in self._astream(prompt=prompt, + stop=stop, + run_manager=run_manager, + **kwargs): completion += chunk.text return completion @@ -476,8 +475,10 @@ class Anthropic(LLM, _AnthropicCommon): params = {**self._default_params, **kwargs} for token in self.client.completions.create( - prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params - ): + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + stream=True, + **params): chunk = GenerationChunk(text=token.completion) yield chunk if run_manager: @@ -509,10 +510,10 @@ class Anthropic(LLM, _AnthropicCommon): params = {**self._default_params, **kwargs} async for token in await self.async_client.completions.create( - prompt=self._wrap_prompt(prompt), - stop_sequences=stop, - stream=True, - **params, + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + stream=True, + **params, ): chunk = GenerationChunk(text=token.completion) yield chunk diff --git a/swarms/models/bioclip.py b/swarms/models/bioclip.py index c2b4bfa5..d7052ef3 100644 --- a/swarms/models/bioclip.py +++ b/swarms/models/bioclip.py @@ -97,9 +97,8 @@ class BioClip: self.preprocess_val, ) = open_clip.create_model_and_transforms(model_path) self.tokenizer = open_clip.get_tokenizer(model_path) - self.device = ( - torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - ) + self.device = (torch.device("cuda") + if torch.cuda.is_available() else torch.device("cpu")) self.model.to(self.device) self.model.eval() @@ -110,18 +109,17 @@ class BioClip: template: str = "this is a photo of ", context_length: int = 256, ): - image = torch.stack([self.preprocess_val(Image.open(img_path))]).to(self.device) - texts = self.tokenizer( - [template + l for l in labels], context_length=context_length - ).to(self.device) + image = torch.stack([self.preprocess_val(Image.open(img_path)) + ]).to(self.device) + texts = self.tokenizer([template + l for l in labels], + context_length=context_length).to(self.device) with torch.no_grad(): - image_features, text_features, logit_scale = self.model(image, texts) - logits = ( - (logit_scale * image_features @ text_features.t()) - .detach() - .softmax(dim=-1) - ) + image_features, text_features, logit_scale = self.model( + image, texts) + logits = ((logit_scale * + image_features @ text_features.t()).detach().softmax( + dim=-1)) sorted_indices = torch.argsort(logits, dim=-1, descending=True) logits = logits.cpu().numpy() sorted_indices = sorted_indices.cpu().numpy() @@ -139,11 +137,8 @@ class BioClip: fig, ax = plt.subplots(figsize=(5, 5)) ax.imshow(img) ax.axis("off") - title = ( - metadata["filename"] - + "\n" - + "\n".join([f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()]) - ) + title = (metadata["filename"] + "\n" + "\n".join( + [f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()])) ax.set_title(title, fontsize=14) plt.tight_layout() plt.show() diff --git a/swarms/models/biogpt.py b/swarms/models/biogpt.py index 83c31e55..ebec10b9 100644 --- a/swarms/models/biogpt.py +++ b/swarms/models/biogpt.py @@ -102,9 +102,9 @@ class BioGPT: list[dict]: A list of generated texts. """ set_seed(42) - generator = pipeline( - "text-generation", model=self.model, tokenizer=self.tokenizer - ) + generator = pipeline("text-generation", + model=self.model, + tokenizer=self.tokenizer) out = generator( text, max_length=self.max_length, @@ -149,13 +149,11 @@ class BioGPT: inputs = self.tokenizer(sentence, return_tensors="pt") set_seed(42) with torch.no_grad(): - beam_output = self.model.generate( - **inputs, - min_length=self.min_length, - max_length=self.max_length, - num_beams=num_beams, - early_stopping=early_stopping - ) + beam_output = self.model.generate(**inputs, + min_length=self.min_length, + max_length=self.max_length, + num_beams=num_beams, + early_stopping=early_stopping) return self.tokenizer.decode(beam_output[0], skip_special_tokens=True) # Feature 1: Set a new tokenizer and model diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py index c24f262d..788bae62 100644 --- a/swarms/models/dalle3.py +++ b/swarms/models/dalle3.py @@ -124,13 +124,10 @@ class Dalle3: # Handling exceptions and printing the errors details print( colored( - ( - f"Error running Dalle3: {error} try optimizing your api key and" - " or try again" - ), + (f"Error running Dalle3: {error} try optimizing your api key and" + " or try again"), "red", - ) - ) + )) raise error def create_variations(self, img: str): @@ -157,22 +154,19 @@ class Dalle3: """ try: - response = self.client.images.create_variation( - img=open(img, "rb"), n=self.n, size=self.size - ) + response = self.client.images.create_variation(img=open(img, "rb"), + n=self.n, + size=self.size) img = response.data[0].url return img except (Exception, openai.OpenAIError) as error: print( colored( - ( - f"Error running Dalle3: {error} try optimizing your api key and" - " or try again" - ), + (f"Error running Dalle3: {error} try optimizing your api key and" + " or try again"), "red", - ) - ) + )) print(colored(f"Error running Dalle3: {error.http_status}", "red")) print(colored(f"Error running Dalle3: {error.error}", "red")) raise error diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py index 0a60aaac..8fc5b99a 100644 --- a/swarms/models/distilled_whisperx.py +++ b/swarms/models/distilled_whisperx.py @@ -18,6 +18,7 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1): """ def decorator(func): + @wraps(func) async def wrapper(*args, **kwargs): retries = max_retries @@ -28,7 +29,9 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1): retries -= 1 if retries <= 0: raise - print(f"Retry after exception: {e}, Attempts remaining: {retries}") + print( + f"Retry after exception: {e}, Attempts remaining: {retries}" + ) await asyncio.sleep(delay) return wrapper @@ -62,7 +65,8 @@ class DistilWhisperModel: def __init__(self, model_id="distil-whisper/distil-large-v2"): self.device = "cuda:0" if torch.cuda.is_available() else "cpu" - self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 + self.torch_dtype = torch.float16 if torch.cuda.is_available( + ) else torch.float32 self.model_id = model_id self.model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, @@ -119,14 +123,14 @@ class DistilWhisperModel: try: with torch.no_grad(): # Load the whole audio file, but process and transcribe it in chunks - audio_input = self.processor.audio_file_to_array(audio_file_path) + audio_input = self.processor.audio_file_to_array( + audio_file_path) sample_rate = audio_input.sampling_rate total_duration = len(audio_input.array) / sample_rate chunks = [ - audio_input.array[i : i + sample_rate * chunk_duration] - for i in range( - 0, len(audio_input.array), sample_rate * chunk_duration - ) + audio_input.array[i:i + sample_rate * chunk_duration] + for i in range(0, len(audio_input.array), sample_rate * + chunk_duration) ] print(colored("Starting real-time transcription...", "green")) @@ -139,22 +143,22 @@ class DistilWhisperModel: return_tensors="pt", padding=True, ) - processed_inputs = processed_inputs.input_values.to(self.device) + processed_inputs = processed_inputs.input_values.to( + self.device) # Generate transcription for the chunk logits = self.model.generate(processed_inputs) transcription = self.processor.batch_decode( - logits, skip_special_tokens=True - )[0] + logits, skip_special_tokens=True)[0] # Print the chunk's transcription print( - colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow") - + transcription - ) + colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow") + + transcription) # Wait for the chunk's duration to simulate real-time processing time.sleep(chunk_duration) except Exception as e: - print(colored(f"An error occurred during transcription: {e}", "red")) + print(colored(f"An error occurred during transcription: {e}", + "red")) diff --git a/swarms/models/fastvit.py b/swarms/models/fastvit.py index a2d6bc0a..370569fb 100644 --- a/swarms/models/fastvit.py +++ b/swarms/models/fastvit.py @@ -11,7 +11,8 @@ from pydantic import BaseModel, StrictFloat, StrictInt, validator DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load the classes for image classification -with open(os.path.join(os.path.dirname(__file__), "fast_vit_classes.json")) as f: +with open(os.path.join(os.path.dirname(__file__), + "fast_vit_classes.json")) as f: FASTVIT_IMAGENET_1K_CLASSES = json.load(f) @@ -21,7 +22,8 @@ class ClassificationResult(BaseModel): @validator("class_id", "confidence", pre=True, each_item=True) def check_list_contents(cls, v): - assert isinstance(v, int) or isinstance(v, float), "must be integer or float" + assert isinstance(v, int) or isinstance( + v, float), "must be integer or float" return v @@ -47,16 +49,16 @@ class FastViT: """ def __init__(self): - self.model = timm.create_model( - "hf_hub:timm/fastvit_s12.apple_in1k", pretrained=True - ).to(DEVICE) + self.model = timm.create_model("hf_hub:timm/fastvit_s12.apple_in1k", + pretrained=True).to(DEVICE) data_config = timm.data.resolve_model_data_config(self.model) - self.transforms = timm.data.create_transform(**data_config, is_training=False) + self.transforms = timm.data.create_transform(**data_config, + is_training=False) self.model.eval() - def __call__( - self, img: str, confidence_threshold: float = 0.5 - ) -> ClassificationResult: + def __call__(self, + img: str, + confidence_threshold: float = 0.5) -> ClassificationResult: """classifies the input image and returns the top k classes and their probabilities""" img = Image.open(img).convert("RGB") img_tensor = self.transforms(img).unsqueeze(0).to(DEVICE) @@ -65,9 +67,8 @@ class FastViT: probabilities = torch.nn.functional.softmax(output, dim=1) # Get top k classes and their probabilities - top_probs, top_classes = torch.topk( - probabilities, k=FASTVIT_IMAGENET_1K_CLASSES - ) + top_probs, top_classes = torch.topk(probabilities, + k=FASTVIT_IMAGENET_1K_CLASSES) # Filter by confidence threshold mask = top_probs > confidence_threshold diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index d2d3ebe7..d7148d0e 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -46,9 +46,9 @@ class Fuyu: self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path) self.image_processor = FuyuImageProcessor() - self.processor = FuyuProcessor( - image_processor=self.image_processor, tokenizer=self.tokenizer, **kwargs - ) + self.processor = FuyuProcessor(image_processor=self.image_processor, + tokenizer=self.tokenizer, + **kwargs) self.model = FuyuForCausalLM.from_pretrained( pretrained_path, device_map=device_map, @@ -63,15 +63,17 @@ class Fuyu: def __call__(self, text: str, img: str): """Call the model with text and img paths""" image_pil = Image.open(img) - model_inputs = self.processor( - text=text, images=[image_pil], device=self.device_map - ) + model_inputs = self.processor(text=text, + images=[image_pil], + device=self.device_map) for k, v in model_inputs.items(): model_inputs[k] = v.to(self.device_map) - output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens) - text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) + output = self.model.generate(**model_inputs, + max_new_tokens=self.max_new_tokens) + text = self.processor.batch_decode(output[:, -7:], + skip_special_tokens=True) return print(str(text)) def get_img_from_web(self, img_url: str): diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py index 3fa87443..87393fab 100644 --- a/swarms/models/gpt4v.py +++ b/swarms/models/gpt4v.py @@ -130,19 +130,23 @@ class GPT4Vision: } # Image content - image_content = [ - {"type": "imavge_url", "image_url": img} - if img.startswith("http") - else {"type": "image", "data": img} - for img in img - ] - - messages = [ - { - "role": "user", - "content": image_content + [{"type": "text", "text": q} for q in tasks], - } - ] + image_content = [{ + "type": "imavge_url", + "image_url": img + } if img.startswith("http") else { + "type": "image", + "data": img + } for img in img] + + messages = [{ + "role": + "user", + "content": + image_content + [{ + "type": "text", + "text": q + } for q in tasks], + }] payload = { "model": "gpt-4-vision-preview", @@ -160,7 +164,8 @@ class GPT4Vision: timeout=self.timeout_seconds, ) response.raise_for_status() - answer = response.json()["choices"][0]["message"]["content"]["text"] + answer = response.json( + )["choices"][0]["message"]["content"]["text"] return GPT4VisionResponse(answer=answer) except requests.exceptions.HTTPError as error: self.logger.error( @@ -179,8 +184,7 @@ class GPT4Vision: except Exception as error: self.logger.error( f"Unexpected Error: {error} try optimizing your api key and try" - " again" - ) + " again") raise error from None raise TimeoutError("API Request timed out after multiple retries") @@ -212,18 +216,20 @@ class GPT4Vision: try: response = self.client.chat.completions.create( model=self.model, - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": f"{task}"}, - { - "type": "image_url", - "image_url": f"{img}", - }, - ], - } - ], + messages=[{ + "role": + "user", + "content": [ + { + "type": "text", + "text": f"{task}" + }, + { + "type": "image_url", + "image_url": f"{img}", + }, + ], + }], max_tokens=self.max_tokens, ) @@ -232,13 +238,10 @@ class GPT4Vision: except Exception as error: print( colored( - ( - f"Error when calling GPT4Vision, Error: {error} Try optimizing" - " your key, and try again" - ), + (f"Error when calling GPT4Vision, Error: {error} Try optimizing" + " your key, and try again"), "red", - ) - ) + )) async def arun(self, task: str, img: str) -> str: """ @@ -267,18 +270,20 @@ class GPT4Vision: try: response = await self.client.chat.completions.create( model=self.model, - messages=[ - { - "role": "user", - "content": [ - {"type": "text", "text": f"{task}"}, - { - "type": "image_url", - "image_url": f"{img}", - }, - ], - } - ], + messages=[{ + "role": + "user", + "content": [ + { + "type": "text", + "text": f"{task}" + }, + { + "type": "image_url", + "image_url": f"{img}", + }, + ], + }], max_tokens=self.max_tokens, ) out = response.choices[0].text @@ -286,10 +291,7 @@ class GPT4Vision: except Exception as error: print( colored( - ( - f"Error when calling GPT4Vision, Error: {error} Try optimizing" - " your key, and try again" - ), + (f"Error when calling GPT4Vision, Error: {error} Try optimizing" + " your key, and try again"), "red", - ) - ) + )) diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index 9279fea4..a84cc960 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -47,9 +47,8 @@ class HuggingfaceLLM: **kwargs, ): self.logger = logging.getLogger(__name__) - self.device = ( - device if device else ("cuda" if torch.cuda.is_available() else "cpu") - ) + self.device = (device if device else + ("cuda" if torch.cuda.is_available() else "cpu")) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -58,9 +57,8 @@ class HuggingfaceLLM: self.model, self.tokenizer = None, None if self.distributed: - assert ( - torch.cuda.device_count() > 1 - ), "You need more than 1 gpu for distributed processing" + assert (torch.cuda.device_count() > + 1), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -75,17 +73,17 @@ class HuggingfaceLLM: try: self.tokenizer = AutoTokenizer.from_pretrained( - self.model_id, *args, **kwargs - ) + self.model_id, *args, **kwargs) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config, *args, **kwargs - ) + self.model_id, quantization_config=bnb_config, *args, **kwargs) self.model # .to(self.device) except Exception as e: # self.logger.error(f"Failed to load the model or the tokenizer: {e}") # raise - print(colored(f"Failed to load the model and or the tokenizer: {e}", "red")) + print( + colored(f"Failed to load the model and or the tokenizer: {e}", + "red")) def print_error(self, error: str): """Print error""" @@ -97,20 +95,18 @@ class HuggingfaceLLM: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = ( - BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config - else None - ) + bnb_config = (BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config else None) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config - ).to(self.device) + self.model_id, + quantization_config=bnb_config).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error(f"Failed to load the model or the tokenizer: {error}") + self.logger.error( + f"Failed to load the model or the tokenizer: {error}") raise def run(self, task: str): @@ -131,7 +127,8 @@ class HuggingfaceLLM: self.print_dashboard(task) try: - inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(task, + return_tensors="pt").to(self.device) # self.log.start() @@ -140,39 +137,36 @@ class HuggingfaceLLM: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) except Exception as e: print( colored( - ( - f"HuggingfaceLLM could not generate text because of error: {e}," - " try optimizing your arguments" - ), + (f"HuggingfaceLLM could not generate text because of error: {e}," + " try optimizing your arguments"), "red", - ) - ) + )) raise async def run_async(self, task: str, *args, **kwargs) -> str: @@ -216,7 +210,8 @@ class HuggingfaceLLM: self.print_dashboard(task) try: - inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(task, + return_tensors="pt").to(self.device) # self.log.start() @@ -225,26 +220,26 @@ class HuggingfaceLLM: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs @@ -305,8 +300,7 @@ class HuggingfaceLLM: """, "red", - ) - ) + )) print(dashboard) diff --git a/swarms/models/idefics.py b/swarms/models/idefics.py index 73cb4991..41b8823d 100644 --- a/swarms/models/idefics.py +++ b/swarms/models/idefics.py @@ -65,9 +65,8 @@ class Idefics: torch_dtype=torch.bfloat16, max_length=100, ): - self.device = ( - device if device else ("cuda" if torch.cuda.is_available() else "cpu") - ) + self.device = (device if device else + ("cuda" if torch.cuda.is_available() else "cpu")) self.model = IdeficsForVisionText2Text.from_pretrained( checkpoint, torch_dtype=torch_dtype, @@ -96,21 +95,17 @@ class Idefics: list A list of generated text strings. """ - inputs = ( - self.processor( - prompts, add_end_of_utterance_token=False, return_tensors="pt" - ).to(self.device) - if batched_mode - else self.processor(prompts[0], return_tensors="pt").to(self.device) - ) + inputs = (self.processor( + prompts, add_end_of_utterance_token=False, return_tensors="pt").to( + self.device) if batched_mode else self.processor( + prompts[0], return_tensors="pt").to(self.device)) exit_condition = self.processor.tokenizer( - "", add_special_tokens=False - ).input_ids + "", add_special_tokens=False).input_ids bad_words_ids = self.processor.tokenizer( - ["", "", "", add_special_tokens=False - ).input_ids + "", add_special_tokens=False).input_ids bad_words_ids = self.processor.tokenizer( - ["", "", " 1 - ), "You need more than 1 gpu for distributed processing" + assert (torch.cuda.device_count() > + 1), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -83,8 +81,9 @@ class JinaEmbeddings: try: self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config, trust_remote_code=True - ) + self.model_id, + quantization_config=bnb_config, + trust_remote_code=True) self.model # .to(self.device) except Exception as e: @@ -97,11 +96,8 @@ class JinaEmbeddings: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = ( - BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config - else None - ) + bnb_config = (BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config else None) self.model = AutoModelForCausalLM.from_pretrained( self.model_id, @@ -112,7 +108,8 @@ class JinaEmbeddings: if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error(f"Failed to load the model or the tokenizer: {error}") + self.logger.error( + f"Failed to load the model or the tokenizer: {error}") raise def run(self, task: str): diff --git a/swarms/models/kosmos2.py b/swarms/models/kosmos2.py index 12d5638a..9a1eafba 100644 --- a/swarms/models/kosmos2.py +++ b/swarms/models/kosmos2.py @@ -14,11 +14,8 @@ class Detections(BaseModel): @root_validator def check_length(cls, values): - assert ( - len(values.get("xyxy")) - == len(values.get("class_id")) - == len(values.get("confidence")) - ), "All fields must have the same length." + assert (len(values.get("xyxy")) == len(values.get("class_id")) == len( + values.get("confidence"))), "All fields must have the same length." return values @validator("xyxy", "class_id", "confidence", pre=True, each_item=True) @@ -39,11 +36,9 @@ class Kosmos2(BaseModel): @classmethod def initialize(cls): model = AutoModelForVision2Seq.from_pretrained( - "ydshieh/kosmos-2-patch14-224", trust_remote_code=True - ) + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True) processor = AutoProcessor.from_pretrained( - "ydshieh/kosmos-2-patch14-224", trust_remote_code=True - ) + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True) return cls(model=model, processor=processor) def __call__(self, img: str) -> Detections: @@ -51,11 +46,12 @@ class Kosmos2(BaseModel): prompt = "An image of" inputs = self.processor(text=prompt, images=image, return_tensors="pt") - outputs = self.model.generate(**inputs, use_cache=True, max_new_tokens=64) + outputs = self.model.generate(**inputs, + use_cache=True, + max_new_tokens=64) - generated_text = self.processor.batch_decode(outputs, skip_special_tokens=True)[ - 0 - ] + generated_text = self.processor.batch_decode( + outputs, skip_special_tokens=True)[0] # The actual processing of generated_text to entities would go here # For the purpose of this example, assume a mock function 'extract_entities' exists: @@ -66,8 +62,8 @@ class Kosmos2(BaseModel): return detections def extract_entities( - self, text: str - ) -> List[Tuple[str, Tuple[float, float, float, float]]]: + self, + text: str) -> List[Tuple[str, Tuple[float, float, float, float]]]: # Placeholder function for entity extraction # This should be replaced with the actual method of extracting entities return [] @@ -80,19 +76,19 @@ class Kosmos2(BaseModel): if not entities: return Detections.empty() - class_ids = [0] * len(entities) # Replace with actual class ID extraction logic - xyxys = [ - ( - e[1][0] * image.width, - e[1][1] * image.height, - e[1][2] * image.width, - e[1][3] * image.height, - ) - for e in entities - ] + class_ids = [0] * len( + entities) # Replace with actual class ID extraction logic + xyxys = [( + e[1][0] * image.width, + e[1][1] * image.height, + e[1][2] * image.width, + e[1][3] * image.height, + ) for e in entities] confidences = [1.0] * len(entities) # Placeholder confidence - return Detections(xyxy=xyxys, class_id=class_ids, confidence=confidences) + return Detections(xyxy=xyxys, + class_id=class_ids, + confidence=confidences) # Usage: diff --git a/swarms/models/kosmos_two.py b/swarms/models/kosmos_two.py index 596886f3..402ad73d 100644 --- a/swarms/models/kosmos_two.py +++ b/swarms/models/kosmos_two.py @@ -46,11 +46,9 @@ class Kosmos: model_name="ydshieh/kosmos-2-patch14-224", ): self.model = AutoModelForVision2Seq.from_pretrained( - model_name, trust_remote_code=True - ) - self.processor = AutoProcessor.from_pretrained( - model_name, trust_remote_code=True - ) + model_name, trust_remote_code=True) + self.processor = AutoProcessor.from_pretrained(model_name, + trust_remote_code=True) def get_image(self, url): """Image""" @@ -73,8 +71,7 @@ class Kosmos: skip_special_tokens=True, )[0] processed_text, entities = self.processor.post_process_generation( - generated_texts - ) + generated_texts) def __call__(self, prompt, image): """Run call""" @@ -93,8 +90,7 @@ class Kosmos: skip_special_tokens=True, )[0] processed_text, entities = self.processor.post_process_generation( - generated_texts - ) + generated_texts) # tasks def multimodal_grounding(self, phrase, image_url): @@ -145,12 +141,10 @@ class Kosmos: elif isinstance(image, torch.Tensor): # pdb.set_trace() image_tensor = image.cpu() - reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[ - :, None, None - ] - reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[ - :, None, None - ] + reverse_norm_mean = torch.tensor( + [0.48145466, 0.4578275, 0.40821073])[:, None, None] + reverse_norm_std = torch.tensor( + [0.26862954, 0.26130258, 0.27577711])[:, None, None] image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean pil_img = T.ToPILImage()(image_tensor) image_h = pil_img.height @@ -169,9 +163,9 @@ class Kosmos: # thickness of text text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) box_line = 3 - (c_width, text_height), _ = cv2.getTextSize( - "F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line - ) + (c_width, text_height), _ = cv2.getTextSize("F", + cv2.FONT_HERSHEY_COMPLEX, + text_size, text_line) base_height = int(text_height * 0.675) text_offset_original = text_height - base_height text_spaces = 3 @@ -187,9 +181,8 @@ class Kosmos: # draw bbox # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) - new_image = cv2.rectangle( - new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line - ) + new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), + (orig_x2, orig_y2), color, box_line) l_o, r_o = ( box_line // 2 + box_line % 2, @@ -200,19 +193,15 @@ class Kosmos: y1 = orig_y1 - l_o if y1 < text_height + text_offset_original + 2 * text_spaces: - y1 = ( - orig_y1 - + r_o - + text_height - + text_offset_original - + 2 * text_spaces - ) + y1 = (orig_y1 + r_o + text_height + text_offset_original + + 2 * text_spaces) x1 = orig_x1 + r_o # add text background - (text_width, text_height), _ = cv2.getTextSize( - f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line - ) + (text_width, + text_height), _ = cv2.getTextSize(f" {entity_name}", + cv2.FONT_HERSHEY_COMPLEX, + text_size, text_line) text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = ( x1, y1 - (text_height + text_offset_original + 2 * text_spaces), @@ -222,23 +211,19 @@ class Kosmos: for prev_bbox in previous_bboxes: while is_overlapping( - (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox - ): - text_bg_y1 += ( - text_height + text_offset_original + 2 * text_spaces - ) - text_bg_y2 += ( - text_height + text_offset_original + 2 * text_spaces - ) + (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), + prev_bbox): + text_bg_y1 += (text_height + text_offset_original + + 2 * text_spaces) + text_bg_y2 += (text_height + text_offset_original + + 2 * text_spaces) y1 += text_height + text_offset_original + 2 * text_spaces if text_bg_y2 >= image_h: text_bg_y1 = max( 0, - image_h - - ( - text_height + text_offset_original + 2 * text_spaces - ), + image_h - (text_height + text_offset_original + + 2 * text_spaces), ) text_bg_y2 = image_h y1 = image_h @@ -255,9 +240,9 @@ class Kosmos: # white bg_color = [255, 255, 255] new_image[i, j] = ( - alpha * new_image[i, j] - + (1 - alpha) * np.array(bg_color) - ).astype(np.uint8) + alpha * new_image[i, j] + + (1 - alpha) * np.array(bg_color)).astype( + np.uint8) cv2.putText( new_image, @@ -270,7 +255,8 @@ class Kosmos: cv2.LINE_AA, ) # previous_locations.append((x1, y1)) - previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) + previous_bboxes.append( + (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]]) if save_path: diff --git a/swarms/models/llava.py b/swarms/models/llava.py index 6f8019bc..7f49ad4a 100644 --- a/swarms/models/llava.py +++ b/swarms/models/llava.py @@ -48,9 +48,8 @@ class MultiModalLlava: revision=revision, ).to(self.device) - self.tokenizer = AutoTokenizer.from_pretrained( - model_name_or_path, use_fast=True - ) + self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, + use_fast=True) self.pipe = pipeline( "text-generation", model=self.model, diff --git a/swarms/models/mistral.py b/swarms/models/mistral.py index 7f48a0d6..f14d9e39 100644 --- a/swarms/models/mistral.py +++ b/swarms/models/mistral.py @@ -49,7 +49,8 @@ class Mistral: # Check if the specified device is available if not torch.cuda.is_available() and device == "cuda": - raise ValueError("CUDA is not available. Please choose a different device.") + raise ValueError( + "CUDA is not available. Please choose a different device.") # Load the model and tokenizer self.model = None @@ -70,7 +71,8 @@ class Mistral: """Run the model on a given task.""" try: - model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device) + model_inputs = self.tokenizer([task], + return_tensors="pt").to(self.device) generated_ids = self.model.generate( **model_inputs, max_length=self.max_length, @@ -87,7 +89,8 @@ class Mistral: """Run the model on a given task.""" try: - model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device) + model_inputs = self.tokenizer([task], + return_tensors="pt").to(self.device) generated_ids = self.model.generate( **model_inputs, max_length=self.max_length, diff --git a/swarms/models/mpt.py b/swarms/models/mpt.py index 035e2b54..9fb6c90b 100644 --- a/swarms/models/mpt.py +++ b/swarms/models/mpt.py @@ -26,7 +26,10 @@ class MPT7B: """ - def __init__(self, model_name: str, tokenizer_name: str, max_tokens: int = 100): + def __init__(self, + model_name: str, + tokenizer_name: str, + max_tokens: int = 100): # Loading model and tokenizer details self.model_name = model_name self.tokenizer_name = tokenizer_name @@ -37,11 +40,9 @@ class MPT7B: self.logger = logging.getLogger(__name__) config = AutoModelForCausalLM.from_pretrained( - model_name, trust_remote_code=True - ).config + model_name, trust_remote_code=True).config self.model = AutoModelForCausalLM.from_pretrained( - model_name, config=config, trust_remote_code=True - ) + model_name, config=config, trust_remote_code=True) # Initializing a text-generation pipeline self.pipe = pipeline( @@ -114,9 +115,10 @@ class MPT7B: """ with torch.autocast("cuda", dtype=torch.bfloat16): - return self.pipe( - prompt, max_new_tokens=self.max_tokens, do_sample=True, use_cache=True - )[0]["generated_text"] + return self.pipe(prompt, + max_new_tokens=self.max_tokens, + do_sample=True, + use_cache=True)[0]["generated_text"] async def generate_async(self, prompt: str) -> str: """Generate Async""" diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index 34465c73..a362f94f 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -41,8 +41,10 @@ class Nougat: self.min_length = min_length self.max_new_tokens = max_new_tokens - self.processor = NougatProcessor.from_pretrained(self.model_name_or_path) - self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name_or_path) + self.processor = NougatProcessor.from_pretrained( + self.model_name_or_path) + self.model = VisionEncoderDecoderModel.from_pretrained( + self.model_name_or_path) self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model.to(self.device) @@ -63,8 +65,10 @@ class Nougat: max_new_tokens=self.max_new_tokens, ) - sequence = self.processor.batch_decode(outputs, skip_special_tokens=True)[0] - sequence = self.processor.post_process_generation(sequence, fix_markdown=False) + sequence = self.processor.batch_decode(outputs, + skip_special_tokens=True)[0] + sequence = self.processor.post_process_generation(sequence, + fix_markdown=False) out = print(repr(sequence)) return out diff --git a/swarms/models/openai_assistant.py b/swarms/models/openai_assistant.py index 6d0c518f..37b41191 100644 --- a/swarms/models/openai_assistant.py +++ b/swarms/models/openai_assistant.py @@ -55,9 +55,9 @@ class OpenAIAssistant: return thread def add_message_to_thread(self, thread_id: str, message: str): - message = self.client.beta.threads.add_message( - thread_id=thread_id, role=self.user, content=message - ) + message = self.client.beta.threads.add_message(thread_id=thread_id, + role=self.user, + content=message) return message def run(self, task: str): @@ -67,8 +67,7 @@ class OpenAIAssistant: instructions=self.instructions, ) - out = self.client.beta.threads.runs.retrieve( - thread_id=run.thread_id, run_id=run.id - ) + out = self.client.beta.threads.runs.retrieve(thread_id=run.thread_id, + run_id=run.id) return out diff --git a/swarms/models/openai_embeddings.py b/swarms/models/openai_embeddings.py index 81dea550..8eeb009d 100644 --- a/swarms/models/openai_embeddings.py +++ b/swarms/models/openai_embeddings.py @@ -28,9 +28,10 @@ from tenacity import ( from swarms.models.embeddings_base import Embeddings -def get_from_dict_or_env( - values: dict, key: str, env_key: str, default: Any = None -) -> Any: +def get_from_dict_or_env(values: dict, + key: str, + env_key: str, + default: Any = None) -> Any: import os return values.get(key) or os.getenv(env_key) or default @@ -43,7 +44,8 @@ def get_pydantic_field_names(cls: Any) -> Set[str]: logger = logging.getLogger(__name__) -def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: +def _create_retry_decorator( + embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: import llm min_seconds = 4 @@ -54,13 +56,11 @@ def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any reraise=True, stop=stop_after_attempt(embeddings.max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), - retry=( - retry_if_exception_type(llm.error.Timeout) - | retry_if_exception_type(llm.error.APIError) - | retry_if_exception_type(llm.error.APIConnectionError) - | retry_if_exception_type(llm.error.RateLimitError) - | retry_if_exception_type(llm.error.ServiceUnavailableError) - ), + retry=(retry_if_exception_type(llm.error.Timeout) | + retry_if_exception_type(llm.error.APIError) | + retry_if_exception_type(llm.error.APIConnectionError) | + retry_if_exception_type(llm.error.RateLimitError) | + retry_if_exception_type(llm.error.ServiceUnavailableError)), before_sleep=before_sleep_log(logger, logging.WARNING), ) @@ -76,17 +76,16 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any: reraise=True, stop=stop_after_attempt(embeddings.max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), - retry=( - retry_if_exception_type(llm.error.Timeout) - | retry_if_exception_type(llm.error.APIError) - | retry_if_exception_type(llm.error.APIConnectionError) - | retry_if_exception_type(llm.error.RateLimitError) - | retry_if_exception_type(llm.error.ServiceUnavailableError) - ), + retry=(retry_if_exception_type(llm.error.Timeout) | + retry_if_exception_type(llm.error.APIError) | + retry_if_exception_type(llm.error.APIConnectionError) | + retry_if_exception_type(llm.error.RateLimitError) | + retry_if_exception_type(llm.error.ServiceUnavailableError)), before_sleep=before_sleep_log(logger, logging.WARNING), ) def wrap(func: Callable) -> Callable: + async def wrapped_f(*args: Any, **kwargs: Any) -> Callable: async for _ in async_retrying: return await func(*args, **kwargs) @@ -118,7 +117,8 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: return _embed_with_retry(**kwargs) -async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: +async def async_embed_with_retry(embeddings: OpenAIEmbeddings, + **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" @_async_retry_decorator(embeddings) @@ -225,11 +225,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): warnings.warn( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""" - ) + Please confirm that {field_name} is what you intended.""") extra[field_name] = values.pop(field_name) - invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) + invalid_model_kwargs = all_required_field_names.intersection( + extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " @@ -242,9 +242,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env( - values, "openai_api_key", "OPENAI_API_KEY" - ) + values["openai_api_key"] = get_from_dict_or_env(values, + "openai_api_key", + "OPENAI_API_KEY") values["openai_api_base"] = get_from_dict_or_env( values, "openai_api_base", @@ -284,10 +284,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): values["client"] = llm.Embedding except ImportError: - raise ImportError( - "Could not import openai python package. " - "Please install it with `pip install openai`." - ) + raise ImportError("Could not import openai python package. " + "Please install it with `pip install openai`.") return values @property @@ -315,8 +313,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): return openai_args def _get_len_safe_embeddings( - self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None - ) -> List[List[float]]: + self, + texts: List[str], + *, + engine: str, + chunk_size: Optional[int] = None) -> List[List[float]]: embeddings: List[List[float]] = [[] for _ in range(len(texts))] try: import tiktoken @@ -324,8 +325,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to for OpenAIEmbeddings. " - "Please install it with `pip install tiktoken`." - ) + "Please install it with `pip install tiktoken`.") tokens = [] indices = [] @@ -333,7 +333,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") + logger.warning( + "Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) for i, text in enumerate(texts): @@ -347,7 +348,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): disallowed_special=self.disallowed_special, ) for j in range(0, len(token), self.embedding_ctx_length): - tokens.append(token[j : j + self.embedding_ctx_length]) + tokens.append(token[j:j + self.embedding_ctx_length]) indices.append(i) batched_embeddings: List[List[float]] = [] @@ -366,7 +367,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in _iter: response = embed_with_retry( self, - input=tokens[i : i + _chunk_size], + input=tokens[i:i + _chunk_size], **self._invocation_params, ) batched_embeddings.extend(r["embedding"] for r in response["data"]) @@ -384,11 +385,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): self, input="", **self._invocation_params, - )[ - "data" - ][0]["embedding"] + )["data"][0]["embedding"] else: - average = np.average(_result, axis=0, weights=num_tokens_in_batch[i]) + average = np.average(_result, + axis=0, + weights=num_tokens_in_batch[i]) embeddings[i] = (average / np.linalg.norm(average)).tolist() return embeddings @@ -396,8 +397,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): # please refer to # https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb async def _aget_len_safe_embeddings( - self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None - ) -> List[List[float]]: + self, + texts: List[str], + *, + engine: str, + chunk_size: Optional[int] = None) -> List[List[float]]: embeddings: List[List[float]] = [[] for _ in range(len(texts))] try: import tiktoken @@ -405,8 +409,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to for OpenAIEmbeddings. " - "Please install it with `pip install tiktoken`." - ) + "Please install it with `pip install tiktoken`.") tokens = [] indices = [] @@ -414,7 +417,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") + logger.warning( + "Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) for i, text in enumerate(texts): @@ -428,7 +432,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): disallowed_special=self.disallowed_special, ) for j in range(0, len(token), self.embedding_ctx_length): - tokens.append(token[j : j + self.embedding_ctx_length]) + tokens.append(token[j:j + self.embedding_ctx_length]) indices.append(i) batched_embeddings: List[List[float]] = [] @@ -436,7 +440,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in range(0, len(tokens), _chunk_size): response = await async_embed_with_retry( self, - input=tokens[i : i + _chunk_size], + input=tokens[i:i + _chunk_size], **self._invocation_params, ) batched_embeddings.extend(r["embedding"] for r in response["data"]) @@ -450,22 +454,22 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in range(len(texts)): _result = results[i] if len(_result) == 0: - average = ( - await async_embed_with_retry( - self, - input="", - **self._invocation_params, - ) - )["data"][0]["embedding"] + average = (await async_embed_with_retry( + self, + input="", + **self._invocation_params, + ))["data"][0]["embedding"] else: - average = np.average(_result, axis=0, weights=num_tokens_in_batch[i]) + average = np.average(_result, + axis=0, + weights=num_tokens_in_batch[i]) embeddings[i] = (average / np.linalg.norm(average)).tolist() return embeddings - def embed_documents( - self, texts: List[str], chunk_size: Optional[int] = 0 - ) -> List[List[float]]: + def embed_documents(self, + texts: List[str], + chunk_size: Optional[int] = 0) -> List[List[float]]: """Call out to OpenAI's embedding endpoint for embedding search docs. Args: @@ -481,8 +485,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings): return self._get_len_safe_embeddings(texts, engine=self.deployment) async def aembed_documents( - self, texts: List[str], chunk_size: Optional[int] = 0 - ) -> List[List[float]]: + self, + texts: List[str], + chunk_size: Optional[int] = 0) -> List[List[float]]: """Call out to OpenAI's embedding endpoint async for embedding search docs. Args: @@ -495,7 +500,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): """ # NOTE: to keep things simple, we assume the list may contain texts longer # than the maximum context and use length-safe embedding function. - return await self._aget_len_safe_embeddings(texts, engine=self.deployment) + return await self._aget_len_safe_embeddings(texts, + engine=self.deployment) def embed_query(self, text: str) -> List[float]: """Call out to OpenAI's embedding endpoint for embedding query text. diff --git a/swarms/models/openai_models.py b/swarms/models/openai_models.py index 0c803755..e1a327b5 100644 --- a/swarms/models/openai_models.py +++ b/swarms/models/openai_models.py @@ -33,9 +33,8 @@ from langchain.utils.utils import build_extra_kwargs logger = logging.getLogger(__name__) -def update_token_usage( - keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any] -) -> None: +def update_token_usage(keys: Set[str], response: Dict[str, Any], + token_usage: Dict[str, Any]) -> None: """Update token usage.""" _keys_to_use = keys.intersection(response["usage"]) for _key in _keys_to_use: @@ -46,44 +45,42 @@ def update_token_usage( def _stream_response_to_generation_chunk( - stream_response: Dict[str, Any], -) -> GenerationChunk: + stream_response: Dict[str, Any],) -> GenerationChunk: """Convert a stream response to a generation chunk.""" return GenerationChunk( text=stream_response["choices"][0]["text"], generation_info=dict( - finish_reason=stream_response["choices"][0].get("finish_reason", None), + finish_reason=stream_response["choices"][0].get( + "finish_reason", None), logprobs=stream_response["choices"][0].get("logprobs", None), ), ) -def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None: +def _update_response(response: Dict[str, Any], + stream_response: Dict[str, Any]) -> None: """Update response from the stream response.""" response["choices"][0]["text"] += stream_response["choices"][0]["text"] response["choices"][0]["finish_reason"] = stream_response["choices"][0].get( - "finish_reason", None - ) - response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"] + "finish_reason", None) + response["choices"][0]["logprobs"] = stream_response["choices"][0][ + "logprobs"] def _streaming_response_template() -> Dict[str, Any]: return { - "choices": [ - { - "text": "", - "finish_reason": None, - "logprobs": None, - } - ] + "choices": [{ + "text": "", + "finish_reason": None, + "logprobs": None, + }] } def _create_retry_decorator( llm: Union[BaseOpenAI, OpenAIChat], - run_manager: Optional[ - Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] - ] = None, + run_manager: Optional[Union[AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun]] = None, ) -> Callable[[Any], Any]: import openai @@ -94,9 +91,9 @@ def _create_retry_decorator( openai.error.RateLimitError, openai.error.ServiceUnavailableError, ] - return create_base_retry_decorator( - error_types=errors, max_retries=llm.max_retries, run_manager=run_manager - ) + return create_base_retry_decorator(error_types=errors, + max_retries=llm.max_retries, + run_manager=run_manager) def completion_with_retry( @@ -206,7 +203,8 @@ class BaseOpenAI(BaseLLM): API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.""" - def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore + def __new__(cls, + **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore """Initialize the OpenAI object.""" data.get("model_name", "") return super().__new__(cls) @@ -221,17 +219,16 @@ class BaseOpenAI(BaseLLM): """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) - values["model_kwargs"] = build_extra_kwargs( - extra, values, all_required_field_names - ) + values["model_kwargs"] = build_extra_kwargs(extra, values, + all_required_field_names) return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env( - values, "openai_api_key", "OPENAI_API_KEY" - ) + values["openai_api_key"] = get_from_dict_or_env(values, + "openai_api_key", + "OPENAI_API_KEY") values["openai_api_base"] = get_from_dict_or_env( values, "openai_api_base", @@ -255,10 +252,8 @@ class BaseOpenAI(BaseLLM): values["client"] = openai.Completion except ImportError: - raise ImportError( - "Could not import openai python package. " - "Please install it with `pip install openai`." - ) + raise ImportError("Could not import openai python package. " + "Please install it with `pip install openai`.") if values["streaming"] and values["n"] > 1: raise ValueError("Cannot stream results when n > 1.") if values["streaming"] and values["best_of"] > 1: @@ -295,9 +290,10 @@ class BaseOpenAI(BaseLLM): ) -> Iterator[GenerationChunk]: params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutates params - for stream_resp in completion_with_retry( - self, prompt=prompt, run_manager=run_manager, **params - ): + for stream_resp in completion_with_retry(self, + prompt=prompt, + run_manager=run_manager, + **params): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk if run_manager: @@ -306,8 +302,7 @@ class BaseOpenAI(BaseLLM): chunk=chunk, verbose=self.verbose, logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info - else None, + if chunk.generation_info else None, ) async def _astream( @@ -320,8 +315,7 @@ class BaseOpenAI(BaseLLM): params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutate params async for stream_resp in await acompletion_with_retry( - self, prompt=prompt, run_manager=run_manager, **params - ): + self, prompt=prompt, run_manager=run_manager, **params): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk if run_manager: @@ -330,8 +324,7 @@ class BaseOpenAI(BaseLLM): chunk=chunk, verbose=self.verbose, logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info - else None, + if chunk.generation_info else None, ) def _generate( @@ -367,30 +360,32 @@ class BaseOpenAI(BaseLLM): for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: - raise ValueError("Cannot stream results with multiple prompts.") + raise ValueError( + "Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None - for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs): + for chunk in self._stream(_prompts[0], stop, run_manager, + **kwargs): if generation is None: generation = chunk else: generation += chunk assert generation is not None - choices.append( - { - "text": generation.text, - "finish_reason": generation.generation_info.get("finish_reason") - if generation.generation_info - else None, - "logprobs": generation.generation_info.get("logprobs") - if generation.generation_info - else None, - } - ) + choices.append({ + "text": + generation.text, + "finish_reason": + generation.generation_info.get("finish_reason") + if generation.generation_info else None, + "logprobs": + generation.generation_info.get("logprobs") + if generation.generation_info else None, + }) else: - response = completion_with_retry( - self, prompt=_prompts, run_manager=run_manager, **params - ) + response = completion_with_retry(self, + prompt=_prompts, + run_manager=run_manager, + **params) choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) return self.create_llm_result(choices, prompts, token_usage) @@ -414,32 +409,32 @@ class BaseOpenAI(BaseLLM): for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: - raise ValueError("Cannot stream results with multiple prompts.") + raise ValueError( + "Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None - async for chunk in self._astream( - _prompts[0], stop, run_manager, **kwargs - ): + async for chunk in self._astream(_prompts[0], stop, run_manager, + **kwargs): if generation is None: generation = chunk else: generation += chunk assert generation is not None - choices.append( - { - "text": generation.text, - "finish_reason": generation.generation_info.get("finish_reason") - if generation.generation_info - else None, - "logprobs": generation.generation_info.get("logprobs") - if generation.generation_info - else None, - } - ) + choices.append({ + "text": + generation.text, + "finish_reason": + generation.generation_info.get("finish_reason") + if generation.generation_info else None, + "logprobs": + generation.generation_info.get("logprobs") + if generation.generation_info else None, + }) else: - response = await acompletion_with_retry( - self, prompt=_prompts, run_manager=run_manager, **params - ) + response = await acompletion_with_retry(self, + prompt=_prompts, + run_manager=run_manager, + **params) choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) return self.create_llm_result(choices, prompts, token_usage) @@ -453,39 +448,35 @@ class BaseOpenAI(BaseLLM): """Get the sub prompts for llm call.""" if stop is not None: if "stop" in params: - raise ValueError("`stop` found in both the input and default params.") + raise ValueError( + "`stop` found in both the input and default params.") params["stop"] = stop if params["max_tokens"] == -1: if len(prompts) != 1: raise ValueError( - "max_tokens set to -1 not supported for multiple inputs." - ) + "max_tokens set to -1 not supported for multiple inputs.") params["max_tokens"] = self.max_tokens_for_prompt(prompts[0]) sub_prompts = [ - prompts[i : i + self.batch_size] + prompts[i:i + self.batch_size] for i in range(0, len(prompts), self.batch_size) ] return sub_prompts - def create_llm_result( - self, choices: Any, prompts: List[str], token_usage: Dict[str, int] - ) -> LLMResult: + def create_llm_result(self, choices: Any, prompts: List[str], + token_usage: Dict[str, int]) -> LLMResult: """Create the LLMResult from the choices and prompts.""" generations = [] for i, _ in enumerate(prompts): - sub_choices = choices[i * self.n : (i + 1) * self.n] - generations.append( - [ - Generation( - text=choice["text"], - generation_info=dict( - finish_reason=choice.get("finish_reason"), - logprobs=choice.get("logprobs"), - ), - ) - for choice in sub_choices - ] - ) + sub_choices = choices[i * self.n:(i + 1) * self.n] + generations.append([ + Generation( + text=choice["text"], + generation_info=dict( + finish_reason=choice.get("finish_reason"), + logprobs=choice.get("logprobs"), + ), + ) for choice in sub_choices + ]) llm_output = {"token_usage": token_usage, "model_name": self.model_name} return LLMResult(generations=generations, llm_output=llm_output) @@ -500,7 +491,10 @@ class BaseOpenAI(BaseLLM): if self.openai_proxy: import openai - openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501 + openai.proxy = { + "http": self.openai_proxy, + "https": self.openai_proxy + } # type: ignore[assignment] # noqa: E501 return {**openai_creds, **self._default_params} @property @@ -524,14 +518,14 @@ class BaseOpenAI(BaseLLM): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to calculate get_num_tokens. " - "Please install it with `pip install tiktoken`." - ) + "Please install it with `pip install tiktoken`.") model_name = self.tiktoken_model_name or self.model_name try: enc = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") + logger.warning( + "Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" enc = tiktoken.get_encoding(model) @@ -593,9 +587,7 @@ class BaseOpenAI(BaseLLM): if context_size is None: raise ValueError( f"Unknown model: {modelname}. Please provide a valid OpenAI model name." - "Known models are: " - + ", ".join(model_token_mapping.keys()) - ) + "Known models are: " + ", ".join(model_token_mapping.keys())) return context_size @@ -673,14 +665,15 @@ class AzureOpenAI(BaseOpenAI): "OPENAI_API_VERSION", ) values["openai_api_type"] = get_from_dict_or_env( - values, "openai_api_type", "OPENAI_API_TYPE", "azure" - ) + values, "openai_api_type", "OPENAI_API_TYPE", "azure") return values @property def _identifying_params(self) -> Mapping[str, Any]: return { - **{"deployment_name": self.deployment_name}, + **{ + "deployment_name": self.deployment_name + }, **super()._identifying_params, } @@ -745,7 +738,9 @@ class OpenAIChat(BaseLLM): @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in cls.__fields__.values()} + all_required_field_names = { + field.alias for field in cls.__fields__.values() + } extra = values.get("model_kwargs", {}) for field_name in list(values): @@ -759,9 +754,8 @@ class OpenAIChat(BaseLLM): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - openai_api_key = get_from_dict_or_env( - values, "openai_api_key", "OPENAI_API_KEY" - ) + openai_api_key = get_from_dict_or_env(values, "openai_api_key", + "OPENAI_API_KEY") openai_api_base = get_from_dict_or_env( values, "openai_api_base", @@ -774,9 +768,10 @@ class OpenAIChat(BaseLLM): "OPENAI_PROXY", default="", ) - openai_organization = get_from_dict_or_env( - values, "openai_organization", "OPENAI_ORGANIZATION", default="" - ) + openai_organization = get_from_dict_or_env(values, + "openai_organization", + "OPENAI_ORGANIZATION", + default="") try: import openai @@ -786,20 +781,20 @@ class OpenAIChat(BaseLLM): if openai_organization: openai.organization = openai_organization if openai_proxy: - openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501 + openai.proxy = { + "http": openai_proxy, + "https": openai_proxy + } # type: ignore[assignment] # noqa: E501 except ImportError: - raise ImportError( - "Could not import openai python package. " - "Please install it with `pip install openai`." - ) + raise ImportError("Could not import openai python package. " + "Please install it with `pip install openai`.") try: values["client"] = openai.ChatCompletion except AttributeError: raise ValueError( "`openai` has no `ChatCompletion` attribute, this is likely " "due to an old version of the openai package. Try upgrading it " - "with `pip install --upgrade openai`." - ) + "with `pip install --upgrade openai`.") return values @property @@ -807,18 +802,27 @@ class OpenAIChat(BaseLLM): """Get the default parameters for calling OpenAI API.""" return self.model_kwargs - def _get_chat_params( - self, prompts: List[str], stop: Optional[List[str]] = None - ) -> Tuple: + def _get_chat_params(self, + prompts: List[str], + stop: Optional[List[str]] = None) -> Tuple: if len(prompts) > 1: raise ValueError( f"OpenAIChat currently only supports single prompt, got {prompts}" ) - messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}] - params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params} + messages = self.prefix_messages + [{ + "role": "user", + "content": prompts[0] + }] + params: Dict[str, Any] = { + **{ + "model": self.model_name + }, + **self._default_params + } if stop is not None: if "stop" in params: - raise ValueError("`stop` found in both the input and default params.") + raise ValueError( + "`stop` found in both the input and default params.") params["stop"] = stop if params.get("max_tokens") == -1: # for ChatGPT api, omitting max_tokens is equivalent to having no limit @@ -834,9 +838,10 @@ class OpenAIChat(BaseLLM): ) -> Iterator[GenerationChunk]: messages, params = self._get_chat_params([prompt], stop) params = {**params, **kwargs, "stream": True} - for stream_resp in completion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ): + for stream_resp in completion_with_retry(self, + messages=messages, + run_manager=run_manager, + **params): token = stream_resp["choices"][0]["delta"].get("content", "") chunk = GenerationChunk(text=token) yield chunk @@ -853,8 +858,7 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params([prompt], stop) params = {**params, **kwargs, "stream": True} async for stream_resp in await acompletion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ): + self, messages=messages, run_manager=run_manager, **params): token = stream_resp["choices"][0]["delta"].get("content", "") chunk = GenerationChunk(text=token) yield chunk @@ -880,17 +884,19 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = completion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ) + full_response = completion_with_retry(self, + messages=messages, + run_manager=run_manager, + **params) llm_output = { "token_usage": full_response["usage"], "model_name": self.model_name, } return LLMResult( - generations=[ - [Generation(text=full_response["choices"][0]["message"]["content"])] - ], + generations=[[ + Generation( + text=full_response["choices"][0]["message"]["content"]) + ]], llm_output=llm_output, ) @@ -903,7 +909,8 @@ class OpenAIChat(BaseLLM): ) -> LLMResult: if self.streaming: generation: Optional[GenerationChunk] = None - async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs): + async for chunk in self._astream(prompts[0], stop, run_manager, + **kwargs): if generation is None: generation = chunk else: @@ -913,17 +920,19 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = await acompletion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ) + full_response = await acompletion_with_retry(self, + messages=messages, + run_manager=run_manager, + **params) llm_output = { "token_usage": full_response["usage"], "model_name": self.model_name, } return LLMResult( - generations=[ - [Generation(text=full_response["choices"][0]["message"]["content"])] - ], + generations=[[ + Generation( + text=full_response["choices"][0]["message"]["content"]) + ]], llm_output=llm_output, ) @@ -948,8 +957,7 @@ class OpenAIChat(BaseLLM): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to calculate get_num_tokens. " - "Please install it with `pip install tiktoken`." - ) + "Please install it with `pip install tiktoken`.") enc = tiktoken.encoding_for_model(self.model_name) return enc.encode( diff --git a/swarms/models/openai_tokenizer.py b/swarms/models/openai_tokenizer.py index 9ff1fa08..26ec9221 100644 --- a/swarms/models/openai_tokenizer.py +++ b/swarms/models/openai_tokenizer.py @@ -71,16 +71,15 @@ class OpenAITokenizer(BaseTokenizer): @property def max_tokens(self) -> int: - tokens = next( - v - for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() - if self.model.startswith(k) - ) + tokens = next(v for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() + if self.model.startswith(k)) offset = 0 if self.model in self.EMBEDDING_MODELS else self.TOKEN_OFFSET return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset - def count_tokens(self, text: str | list, model: Optional[str] = None) -> int: + def count_tokens(self, + text: str | list, + model: Optional[str] = None) -> int: """ Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb @@ -96,12 +95,12 @@ class OpenAITokenizer(BaseTokenizer): encoding = tiktoken.get_encoding("cl100k_base") if model in { - "gpt-3.5-turbo-0613", - "gpt-3.5-turbo-16k-0613", - "gpt-4-0314", - "gpt-4-32k-0314", - "gpt-4-0613", - "gpt-4-32k-0613", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", }: tokens_per_message = 3 tokens_per_name = 1 @@ -113,21 +112,18 @@ class OpenAITokenizer(BaseTokenizer): elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: logging.info( "gpt-3.5-turbo may update over time. Returning num tokens assuming" - " gpt-3.5-turbo-0613." - ) + " gpt-3.5-turbo-0613.") return self.count_tokens(text, model="gpt-3.5-turbo-0613") elif "gpt-4" in model: logging.info( "gpt-4 may update over time. Returning num tokens assuming" - " gpt-4-0613." - ) + " gpt-4-0613.") return self.count_tokens(text, model="gpt-4-0613") else: raise NotImplementedError( f"""token_count() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for - information on how messages are converted to tokens.""" - ) + information on how messages are converted to tokens.""") num_tokens = 0 @@ -144,5 +140,5 @@ class OpenAITokenizer(BaseTokenizer): return num_tokens else: return len( - self.encoding.encode(text, allowed_special=set(self.stop_sequences)) - ) + self.encoding.encode(text, + allowed_special=set(self.stop_sequences))) diff --git a/swarms/models/palm.py b/swarms/models/palm.py index ec8aafd6..c551c288 100644 --- a/swarms/models/palm.py +++ b/swarms/models/palm.py @@ -26,8 +26,7 @@ def _create_retry_decorator() -> Callable[[Any], Any]: except ImportError: raise ImportError( "Could not import google-api-core python package. " - "Please install it with `pip install google-api-core`." - ) + "Please install it with `pip install google-api-core`.") multiplier = 2 min_seconds = 1 @@ -37,12 +36,15 @@ def _create_retry_decorator() -> Callable[[Any], Any]: return retry( reraise=True, stop=stop_after_attempt(max_retries), - wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds), - retry=( - retry_if_exception_type(google.api_core.exceptions.ResourceExhausted) - | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable) - | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError) - ), + wait=wait_exponential(multiplier=multiplier, + min=min_seconds, + max=max_seconds), + retry=(retry_if_exception_type( + google.api_core.exceptions.ResourceExhausted) | + retry_if_exception_type( + google.api_core.exceptions.ServiceUnavailable) | + retry_if_exception_type( + google.api_core.exceptions.GoogleAPIError)), before_sleep=before_sleep_log(logger, logging.WARNING), ) @@ -64,7 +66,8 @@ def _strip_erroneous_leading_spaces(text: str) -> str: The PaLM API will sometimes erroneously return a single leading space in all lines > 1. This function strips that space. """ - has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:]) + has_leading_space = all( + not line or line[0] == " " for line in text.split("\n")[1:]) if has_leading_space: return text.replace("\n ", "\n") else: @@ -97,9 +100,8 @@ class GooglePalm(BaseLLM, BaseModel): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate api key, python package exists.""" - google_api_key = get_from_dict_or_env( - values, "google_api_key", "GOOGLE_API_KEY" - ) + google_api_key = get_from_dict_or_env(values, "google_api_key", + "GOOGLE_API_KEY") try: import google.generativeai as genai @@ -107,12 +109,12 @@ class GooglePalm(BaseLLM, BaseModel): except ImportError: raise ImportError( "Could not import google-generativeai python package. " - "Please install it with `pip install google-generativeai`." - ) + "Please install it with `pip install google-generativeai`.") values["client"] = genai - if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: + if values["temperature"] is not None and not 0 <= values[ + "temperature"] <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: @@ -121,7 +123,8 @@ class GooglePalm(BaseLLM, BaseModel): if values["top_k"] is not None and values["top_k"] <= 0: raise ValueError("top_k must be positive") - if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0: + if values["max_output_tokens"] is not None and values[ + "max_output_tokens"] <= 0: raise ValueError("max_output_tokens must be greater than zero") return values diff --git a/swarms/models/pegasus.py b/swarms/models/pegasus.py index e388d40c..c2571f72 100644 --- a/swarms/models/pegasus.py +++ b/swarms/models/pegasus.py @@ -33,9 +33,10 @@ class PegasusEmbedding: """ - def __init__( - self, modality: str, multi_process: bool = False, n_processes: int = 4 - ): + def __init__(self, + modality: str, + multi_process: bool = False, + n_processes: int = 4): self.modality = modality self.multi_process = multi_process self.n_processes = n_processes @@ -43,8 +44,7 @@ class PegasusEmbedding: self.pegasus = Pegasus(modality, multi_process, n_processes) except Exception as e: logging.error( - f"Failed to initialize Pegasus with modality: {modality}: {e}" - ) + f"Failed to initialize Pegasus with modality: {modality}: {e}") raise def embed(self, data: Union[str, list[str]]): diff --git a/swarms/models/simple_ada.py b/swarms/models/simple_ada.py index 7eb923b4..fbb7c066 100644 --- a/swarms/models/simple_ada.py +++ b/swarms/models/simple_ada.py @@ -21,6 +21,4 @@ def get_ada_embeddings(text: str, model: str = "text-embedding-ada-002"): return openai.Embedding.create( input=[text], model=model, - )["data"][ - 0 - ]["embedding"] + )["data"][0]["embedding"] diff --git a/swarms/models/speecht5.py b/swarms/models/speecht5.py index e98036ac..d1b476b9 100644 --- a/swarms/models/speecht5.py +++ b/swarms/models/speecht5.py @@ -90,17 +90,17 @@ class SpeechT5: self.processor = SpeechT5Processor.from_pretrained(self.model_name) self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name) self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name) - self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") + self.embeddings_dataset = load_dataset(self.dataset_name, + split="validation") def __call__(self, text: str, speaker_id: float = 7306): """Call the model on some text and return the speech.""" speaker_embedding = torch.tensor( - self.embeddings_dataset[speaker_id]["xvector"] - ).unsqueeze(0) + self.embeddings_dataset[speaker_id]["xvector"]).unsqueeze(0) inputs = self.processor(text=text, return_tensors="pt") - speech = self.model.generate_speech( - inputs["input_ids"], speaker_embedding, vocoder=self.vocoder - ) + speech = self.model.generate_speech(inputs["input_ids"], + speaker_embedding, + vocoder=self.vocoder) return speech def save_speech(self, speech, filename="speech.wav"): @@ -121,7 +121,8 @@ class SpeechT5: def set_embeddings_dataset(self, dataset_name): """Set the embeddings dataset to a new dataset.""" self.dataset_name = dataset_name - self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") + self.embeddings_dataset = load_dataset(self.dataset_name, + split="validation") # Feature 1: Get sampling rate def get_sampling_rate(self): diff --git a/swarms/models/timm.py b/swarms/models/timm.py index 5d9b965a..5b17c76c 100644 --- a/swarms/models/timm.py +++ b/swarms/models/timm.py @@ -50,9 +50,8 @@ class TimmModel: in_chans=model_info.in_chans, ) - def __call__( - self, model_info: TimmModelInfo, input_tensor: torch.Tensor - ) -> torch.Size: + def __call__(self, model_info: TimmModelInfo, + input_tensor: torch.Tensor) -> torch.Size: """ Create and run a model specified by `model_info` on `input_tensor`. diff --git a/swarms/models/trocr.py b/swarms/models/trocr.py index f4a4156d..1b9e72e7 100644 --- a/swarms/models/trocr.py +++ b/swarms/models/trocr.py @@ -10,9 +10,8 @@ import requests class TrOCR: - def __init__( - self, - ): + + def __init__(self,): pass def __call__(self): diff --git a/swarms/models/vilt.py b/swarms/models/vilt.py index f95d265c..4725a317 100644 --- a/swarms/models/vilt.py +++ b/swarms/models/vilt.py @@ -23,11 +23,9 @@ class Vilt: def __init__(self): self.processor = ViltProcessor.from_pretrained( - "dandelin/vilt-b32-finetuned-vqa" - ) + "dandelin/vilt-b32-finetuned-vqa") self.model = ViltForQuestionAnswering.from_pretrained( - "dandelin/vilt-b32-finetuned-vqa" - ) + "dandelin/vilt-b32-finetuned-vqa") def __call__(self, text: str, image_url: str): """ diff --git a/swarms/models/wizard_storytelling.py b/swarms/models/wizard_storytelling.py index 49ffb70d..929fe10e 100644 --- a/swarms/models/wizard_storytelling.py +++ b/swarms/models/wizard_storytelling.py @@ -33,7 +33,8 @@ class WizardLLMStoryTeller: def __init__( self, - model_id: str = "TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", + model_id: + str = "TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", device: str = None, max_length: int = 500, quantize: bool = False, @@ -44,9 +45,8 @@ class WizardLLMStoryTeller: decoding=False, ): self.logger = logging.getLogger(__name__) - self.device = ( - device if device else ("cuda" if torch.cuda.is_available() else "cpu") - ) + self.device = (device if device else + ("cuda" if torch.cuda.is_available() else "cpu")) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -56,9 +56,8 @@ class WizardLLMStoryTeller: # self.log = Logging() if self.distributed: - assert ( - torch.cuda.device_count() > 1 - ), "You need more than 1 gpu for distributed processing" + assert (torch.cuda.device_count() > + 1), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -74,8 +73,7 @@ class WizardLLMStoryTeller: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config - ) + self.model_id, quantization_config=bnb_config) self.model # .to(self.device) except Exception as e: @@ -88,20 +86,18 @@ class WizardLLMStoryTeller: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = ( - BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config - else None - ) + bnb_config = (BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config else None) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config - ).to(self.device) + self.model_id, + quantization_config=bnb_config).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error(f"Failed to load the model or the tokenizer: {error}") + self.logger.error( + f"Failed to load the model or the tokenizer: {error}") raise def run(self, prompt_text: str): @@ -120,9 +116,8 @@ class WizardLLMStoryTeller: max_length = self.max_length try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(prompt_text, + return_tensors="pt").to(self.device) # self.log.start() @@ -131,26 +126,26 @@ class WizardLLMStoryTeller: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) @@ -174,9 +169,8 @@ class WizardLLMStoryTeller: max_length = self.max_ try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(prompt_text, + return_tensors="pt").to(self.device) # self.log.start() @@ -185,26 +179,26 @@ class WizardLLMStoryTeller: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs diff --git a/swarms/models/yarn_mistral.py b/swarms/models/yarn_mistral.py index ebe107a2..e3120e20 100644 --- a/swarms/models/yarn_mistral.py +++ b/swarms/models/yarn_mistral.py @@ -44,9 +44,8 @@ class YarnMistral128: decoding=False, ): self.logger = logging.getLogger(__name__) - self.device = ( - device if device else ("cuda" if torch.cuda.is_available() else "cpu") - ) + self.device = (device if device else + ("cuda" if torch.cuda.is_available() else "cpu")) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -56,9 +55,8 @@ class YarnMistral128: # self.log = Logging() if self.distributed: - assert ( - torch.cuda.device_count() > 1 - ), "You need more than 1 gpu for distributed processing" + assert (torch.cuda.device_count() > + 1), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -93,20 +91,18 @@ class YarnMistral128: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = ( - BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config - else None - ) + bnb_config = (BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config else None) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config - ).to(self.device) + self.model_id, + quantization_config=bnb_config).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error(f"Failed to load the model or the tokenizer: {error}") + self.logger.error( + f"Failed to load the model or the tokenizer: {error}") raise def run(self, prompt_text: str): @@ -125,9 +121,8 @@ class YarnMistral128: max_length = self.max_length try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(prompt_text, + return_tensors="pt").to(self.device) # self.log.start() @@ -136,26 +131,26 @@ class YarnMistral128: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) @@ -202,9 +197,8 @@ class YarnMistral128: max_length = self.max_ try: - inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( - self.device - ) + inputs = self.tokenizer.encode(prompt_text, + return_tensors="pt").to(self.device) # self.log.start() @@ -213,26 +207,26 @@ class YarnMistral128: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate( - inputs, max_length=len(inputs) + 1, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=len(inputs) + + 1, + do_sample=True) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode( - [output_tokens], skip_special_tokens=True - ), + self.tokenizer.decode([output_tokens], + skip_special_tokens=True), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate( - inputs, max_length=max_length, do_sample=True - ) + outputs = self.model.generate(inputs, + max_length=max_length, + do_sample=True) del inputs diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index f75945ea..0ed23f19 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -28,7 +28,8 @@ class Zephyr: model_name: str = "HuggingFaceH4/zephyr-7b-alpha", tokenize: bool = False, add_generation_prompt: bool = True, - system_prompt: str = "You are a friendly chatbot who always responds in the style of a pirate", + system_prompt: + str = "You are a friendly chatbot who always responds in the style of a pirate", max_new_tokens: int = 300, temperature: float = 0.5, top_k: float = 50, @@ -70,7 +71,7 @@ class Zephyr: ) outputs = self.pipe(prompt) # max_new_token=self.max_new_tokens) print(outputs[0]["generated_text"]) - + def chat(self, message: str): """ Adds a user message to the conversation and generates a chatbot response. diff --git a/swarms/prompts/agent_output_parser.py b/swarms/prompts/agent_output_parser.py index 27f8ac24..e00db22d 100644 --- a/swarms/prompts/agent_output_parser.py +++ b/swarms/prompts/agent_output_parser.py @@ -24,9 +24,8 @@ class AgentOutputParser(BaseAgentOutputParser): @staticmethod def _preprocess_json_input(input_str: str) -> str: - corrected_str = re.sub( - r'(? dict: diff --git a/swarms/prompts/agent_prompt.py b/swarms/prompts/agent_prompt.py index c4897193..aa84ebf8 100644 --- a/swarms/prompts/agent_prompt.py +++ b/swarms/prompts/agent_prompt.py @@ -13,13 +13,23 @@ class PromptGenerator: self.performance_evaluation: List[str] = [] self.response_format = { "thoughts": { - "text": "thought", - "reasoning": "reasoning", - "plan": "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": "constructive self-criticism", - "speak": "thoughts summary to say to user", + "text": + "thought", + "reasoning": + "reasoning", + "plan": + "- short bulleted\n- list that conveys\n- long-term plan", + "criticism": + "constructive self-criticism", + "speak": + "thoughts summary to say to user", + }, + "command": { + "name": "command name", + "args": { + "arg name": "value" + } }, - "command": {"name": "command name", "args": {"arg name": "value"}}, } def add_constraint(self, constraint: str) -> None: @@ -72,7 +82,6 @@ class PromptGenerator: f"Performance Evaluation:\n{''.join(self.performance_evaluation)}\n\n" "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - "\nEnsure the response can be parsed by Python json.loads" - ) + "\nEnsure the response can be parsed by Python json.loads") return prompt_string diff --git a/swarms/prompts/agent_prompts.py b/swarms/prompts/agent_prompts.py index 8d145fc0..3de5bcb2 100644 --- a/swarms/prompts/agent_prompts.py +++ b/swarms/prompts/agent_prompts.py @@ -7,25 +7,21 @@ def generate_agent_role_prompt(agent): "Finance Agent": ( "You are a seasoned finance analyst AI assistant. Your primary goal is to" " compose comprehensive, astute, impartial, and methodically arranged" - " financial reports based on provided data and trends." - ), + " financial reports based on provided data and trends."), "Travel Agent": ( "You are a world-travelled AI tour guide assistant. Your main purpose is to" " draft engaging, insightful, unbiased, and well-structured travel reports" " on given locations, including history, attractions, and cultural" - " insights." - ), + " insights."), "Academic Research Agent": ( "You are an AI academic research assistant. Your primary responsibility is" " to create thorough, academically rigorous, unbiased, and systematically" " organized reports on a given research topic, following the standards of" - " scholarly work." - ), + " scholarly work."), "Default Agent": ( "You are an AI critical thinker research assistant. Your sole purpose is to" " write well written, critically acclaimed, objective and structured" - " reports on given text." - ), + " reports on given text."), } return prompts.get(agent, "No such agent") @@ -44,8 +40,7 @@ def generate_report_prompt(question, research_summary): " focus on the answer to the question, should be well structured, informative," " in depth, with facts and numbers if available, a minimum of 1,200 words and" " with markdown syntax and apa format. Write all source urls at the end of the" - " report in apa format" - ) + " report in apa format") def generate_search_queries_prompt(question): @@ -57,8 +52,7 @@ def generate_search_queries_prompt(question): return ( "Write 4 google search queries to search online that form an objective opinion" f' from the following: "{question}"You must respond with a list of strings in' - ' the following format: ["query 1", "query 2", "query 3", "query 4"]' - ) + ' the following format: ["query 1", "query 2", "query 3", "query 4"]') def generate_resource_report_prompt(question, research_summary): @@ -80,8 +74,7 @@ def generate_resource_report_prompt(question, research_summary): " significance of each source. Ensure that the report is well-structured," " informative, in-depth, and follows Markdown syntax. Include relevant facts," " figures, and numbers whenever available. The report should have a minimum" - " length of 1,200 words." - ) + " length of 1,200 words.") def generate_outline_report_prompt(question, research_summary): @@ -98,8 +91,7 @@ def generate_outline_report_prompt(question, research_summary): " research report, including the main sections, subsections, and key points to" " be covered. The research report should be detailed, informative, in-depth," " and a minimum of 1,200 words. Use appropriate Markdown syntax to format the" - " outline and ensure readability." - ) + " outline and ensure readability.") def generate_concepts_prompt(question, research_summary): @@ -114,8 +106,7 @@ def generate_concepts_prompt(question, research_summary): " main concepts to learn for a research report on the following question or" f' topic: "{question}". The outline should provide a well-structured' " frameworkYou must respond with a list of strings in the following format:" - ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]' - ) + ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]') def generate_lesson_prompt(concept): @@ -131,8 +122,7 @@ def generate_lesson_prompt(concept): f"generate a comprehensive lesson about {concept} in Markdown syntax. This" f" should include the definitionof {concept}, its historical background and" " development, its applications or uses in differentfields, and notable events" - f" or facts related to {concept}." - ) + f" or facts related to {concept}.") return prompt diff --git a/swarms/prompts/base.py b/swarms/prompts/base.py index 54a0bc3f..8bb77236 100644 --- a/swarms/prompts/base.py +++ b/swarms/prompts/base.py @@ -11,9 +11,9 @@ if TYPE_CHECKING: from langchain.prompts.chat import ChatPromptTemplate -def get_buffer_string( - messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI" -) -> str: +def get_buffer_string(messages: Sequence[BaseMessage], + human_prefix: str = "Human", + ai_prefix: str = "AI") -> str: """Convert sequence of Messages to strings and concatenate them into one string. Args: @@ -88,9 +88,9 @@ class BaseMessage(Serializable): class BaseMessageChunk(BaseMessage): - def _merge_kwargs_dict( - self, left: Dict[str, Any], right: Dict[str, Any] - ) -> Dict[str, Any]: + + def _merge_kwargs_dict(self, left: Dict[str, Any], + right: Dict[str, Any]) -> Dict[str, Any]: """Merge additional_kwargs from another BaseMessageChunk into this one.""" merged = left.copy() for k, v in right.items(): @@ -99,8 +99,7 @@ class BaseMessageChunk(BaseMessage): elif not isinstance(merged[k], type(v)): raise ValueError( f'additional_kwargs["{k}"] already exists in this message,' - " but with a different type." - ) + " but with a different type.") elif isinstance(merged[k], str): merged[k] += v elif isinstance(merged[k], dict): @@ -119,15 +118,12 @@ class BaseMessageChunk(BaseMessage): return self.__class__( content=self.content + other.content, additional_kwargs=self._merge_kwargs_dict( - self.additional_kwargs, other.additional_kwargs - ), + self.additional_kwargs, other.additional_kwargs), ) else: - raise TypeError( - 'unsupported operand type(s) for +: "' - f"{self.__class__.__name__}" - f'" and "{other.__class__.__name__}"' - ) + raise TypeError('unsupported operand type(s) for +: "' + f"{self.__class__.__name__}" + f'" and "{other.__class__.__name__}"') class HumanMessage(BaseMessage): diff --git a/swarms/prompts/chat_prompt.py b/swarms/prompts/chat_prompt.py index b0330e24..5f48488f 100644 --- a/swarms/prompts/chat_prompt.py +++ b/swarms/prompts/chat_prompt.py @@ -66,9 +66,10 @@ class SystemMessage(Message): of input messages. """ - def __init__( - self, content: str, role: str = "System", additional_kwargs: Dict = None - ): + def __init__(self, + content: str, + role: str = "System", + additional_kwargs: Dict = None): super().__init__(content, role, additional_kwargs) def get_type(self) -> str: @@ -106,9 +107,9 @@ class ChatMessage(Message): return "chat" -def get_buffer_string( - messages: Sequence[Message], human_prefix: str = "Human", ai_prefix: str = "AI" -) -> str: +def get_buffer_string(messages: Sequence[Message], + human_prefix: str = "Human", + ai_prefix: str = "AI") -> str: string_messages = [] for m in messages: message = f"{m.role}: {m.content}" diff --git a/swarms/prompts/debate.py b/swarms/prompts/debate.py index a11c7af4..5a6be762 100644 --- a/swarms/prompts/debate.py +++ b/swarms/prompts/debate.py @@ -38,7 +38,6 @@ def debate_monitor(game_description, word_limit, character_names): return prompt -def generate_character_header( - game_description, topic, character_name, character_description -): +def generate_character_header(game_description, topic, character_name, + character_description): pass diff --git a/swarms/prompts/multi_modal_prompts.py b/swarms/prompts/multi_modal_prompts.py index f558c3c4..dc2bccd5 100644 --- a/swarms/prompts/multi_modal_prompts.py +++ b/swarms/prompts/multi_modal_prompts.py @@ -1,7 +1,6 @@ ERROR_PROMPT = ( "An error has occurred for the following text: \n{promptedQuery} Please explain" - " this error.\n {e}" -) + " this error.\n {e}") IMAGE_PROMPT = """ provide a figure named {filename}. The description is: {description}. @@ -12,7 +11,6 @@ USER INPUT ============ """ - AUDIO_PROMPT = """ provide a audio named {filename}. The description is: {description}. @@ -41,7 +39,6 @@ USER INPUT ============ """ - EVAL_PREFIX = """{bot_name} can execute any user's request. {bot_name} has permission to handle one instance and can handle the environment in it at will. diff --git a/swarms/prompts/python.py b/swarms/prompts/python.py index 9d1f4a1e..cd34e9bd 100644 --- a/swarms/prompts/python.py +++ b/swarms/prompts/python.py @@ -3,30 +3,25 @@ PY_REFLEXION_COMPLETION_INSTRUCTION = ( "You are a Python writing assistant. You will be given your past function" " implementation, a series of unit tests, and a hint to change the implementation" " appropriately. Write your full implementation (restate the function" - " signature).\n\n-----" -) + " signature).\n\n-----") PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = ( "You are a Python writing assistant. You will be given a function implementation" " and a series of unit tests. Your goal is to write a few sentences to explain why" " your implementation is wrong as indicated by the tests. You will need this as a" " hint when you try again later. Only provide the few sentence description in your" - " answer, not the implementation.\n\n-----" -) + " answer, not the implementation.\n\n-----") USE_PYTHON_CODEBLOCK_INSTRUCTION = ( "Use a Python code block to write your response. For" - " example:\n```python\nprint('Hello world!')\n```" -) + " example:\n```python\nprint('Hello world!')\n```") PY_SIMPLE_CHAT_INSTRUCTION = ( "You are an AI that only responds with python code, NOT ENGLISH. You will be given" " a function signature and its docstring by the user. Write your full" - " implementation (restate the function signature)." -) + " implementation (restate the function signature).") PY_SIMPLE_CHAT_INSTRUCTION_V2 = ( "You are an AI that only responds with only python code. You will be given a" " function signature and its docstring by the user. Write your full implementation" - " (restate the function signature)." -) + " (restate the function signature).") PY_REFLEXION_CHAT_INSTRUCTION = ( "You are an AI Python assistant. You will be given your past function" " implementation, a series of unit tests, and a hint to change the implementation" @@ -36,8 +31,7 @@ PY_REFLEXION_CHAT_INSTRUCTION_V2 = ( "You are an AI Python assistant. You will be given your previous implementation of" " a function, a series of unit tests results, and your self-reflection on your" " previous implementation. Write your full implementation (restate the function" - " signature)." -) + " signature).") PY_REFLEXION_FEW_SHOT_ADD = '''Example 1: [previous impl]: ```python @@ -175,16 +169,14 @@ PY_SELF_REFLECTION_CHAT_INSTRUCTION = ( " implementation and a series of unit tests. Your goal is to write a few sentences" " to explain why your implementation is wrong as indicated by the tests. You will" " need this as a hint when you try again later. Only provide the few sentence" - " description in your answer, not the implementation." -) + " description in your answer, not the implementation.") PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = ( "You are a Python programming assistant. You will be given a function" " implementation and a series of unit test results. Your goal is to write a few" " sentences to explain why your implementation is wrong as indicated by the tests." " You will need this as guidance when you try again later. Only provide the few" " sentence description in your answer, not the implementation. You will be given a" - " few examples by the user." -) + " few examples by the user.") PY_SELF_REFLECTION_FEW_SHOT = """Example 1: [function impl]: ```python diff --git a/swarms/prompts/sales.py b/swarms/prompts/sales.py index 6c945332..6660e084 100644 --- a/swarms/prompts/sales.py +++ b/swarms/prompts/sales.py @@ -3,39 +3,31 @@ conversation_stages = { "Introduction: Start the conversation by introducing yourself and your company." " Be polite and respectful while keeping the tone of the conversation" " professional. Your greeting should be welcoming. Always clarify in your" - " greeting the reason why you are contacting the prospect." - ), + " greeting the reason why you are contacting the prospect."), "2": ( "Qualification: Qualify the prospect by confirming if they are the right person" " to talk to regarding your product/service. Ensure that they have the" - " authority to make purchasing decisions." - ), + " authority to make purchasing decisions."), "3": ( "Value proposition: Briefly explain how your product/service can benefit the" " prospect. Focus on the unique selling points and value proposition of your" - " product/service that sets it apart from competitors." - ), + " product/service that sets it apart from competitors."), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" - " pain points. Listen carefully to their responses and take notes." - ), - "5": ( - "Solution presentation: Based on the prospect's needs, present your" - " product/service as the solution that can address their pain points." - ), - "6": ( - "Objection handling: Address any objections that the prospect may have" - " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims." - ), + " pain points. Listen carefully to their responses and take notes."), + "5": ("Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": + ("Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims."), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has been" - " discussed and reiterate the benefits." - ), + " discussed and reiterate the benefits."), } - SALES_AGENT_TOOLS_PROMPT = """ Never forget your name is {salesperson_name}. You work as a {salesperson_role}. You work at company named {company_name}. {company_name}'s business is the following: {company_business}. diff --git a/swarms/prompts/sales_prompts.py b/swarms/prompts/sales_prompts.py index ec4ef168..ce5303b3 100644 --- a/swarms/prompts/sales_prompts.py +++ b/swarms/prompts/sales_prompts.py @@ -20,7 +20,6 @@ The answer needs to be one number only, no words. If there is no conversation history, output 1. Do not answer anything else nor add anything to you answer.""" - SALES = """Never forget your name is {salesperson_name}. You work as a {salesperson_role}. You work at company named {company_name}. {company_name}'s business is the following: {company_business} Company values are the following. {company_values} @@ -50,34 +49,27 @@ conversation_stages = { "Introduction: Start the conversation by introducing yourself and your company." " Be polite and respectful while keeping the tone of the conversation" " professional. Your greeting should be welcoming. Always clarify in your" - " greeting the reason why you are contacting the prospect." - ), + " greeting the reason why you are contacting the prospect."), "2": ( "Qualification: Qualify the prospect by confirming if they are the right person" " to talk to regarding your product/service. Ensure that they have the" - " authority to make purchasing decisions." - ), + " authority to make purchasing decisions."), "3": ( "Value proposition: Briefly explain how your product/service can benefit the" " prospect. Focus on the unique selling points and value proposition of your" - " product/service that sets it apart from competitors." - ), + " product/service that sets it apart from competitors."), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" - " pain points. Listen carefully to their responses and take notes." - ), - "5": ( - "Solution presentation: Based on the prospect's needs, present your" - " product/service as the solution that can address their pain points." - ), - "6": ( - "Objection handling: Address any objections that the prospect may have" - " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims." - ), + " pain points. Listen carefully to their responses and take notes."), + "5": ("Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": + ("Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims."), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has been" - " discussed and reiterate the benefits." - ), + " discussed and reiterate the benefits."), } diff --git a/swarms/prompts/summaries_prompts.py b/swarms/prompts/summaries_prompts.py index 01c4c502..646d1ba0 100644 --- a/swarms/prompts/summaries_prompts.py +++ b/swarms/prompts/summaries_prompts.py @@ -10,7 +10,6 @@ summary. Pick a suitable emoji for every bullet point. Your response should be i a YouTube video, use the following text: {{CONTENT}}. """ - SUMMARIZE_PROMPT_2 = """ Provide a very short summary, no more than three sentences, for the following article: @@ -25,7 +24,6 @@ Summary: """ - SUMMARIZE_PROMPT_3 = """ Provide a TL;DR for the following article: @@ -39,7 +37,6 @@ Instead of computing on the individual qubits themselves, we will then compute o TL;DR: """ - SUMMARIZE_PROMPT_4 = """ Provide a very short summary in four bullet points for the following article: @@ -54,7 +51,6 @@ Bulletpoints: """ - SUMMARIZE_PROMPT_5 = """ Please generate a summary of the following conversation and at the end summarize the to-do's for the support Agent: diff --git a/swarms/schemas/typings.py b/swarms/schemas/typings.py index d281a870..f59b16f7 100644 --- a/swarms/schemas/typings.py +++ b/swarms/schemas/typings.py @@ -7,7 +7,6 @@ import platform from enum import Enum from typing import Union - python_version = list(platform.python_version_tuple()) SUPPORT_ADD_NOTES = int(python_version[0]) >= 3 and int(python_version[1]) >= 11 @@ -19,13 +18,11 @@ class ChatbotError(Exception): def __init__(self, *args: object) -> None: if SUPPORT_ADD_NOTES: + super().add_note(( + "Please check that the input is correct, or you can resolve this" + " issue by filing an issue"),) super().add_note( - ( - "Please check that the input is correct, or you can resolve this" - " issue by filing an issue" - ), - ) - super().add_note("Project URL: https://github.com/acheong08/ChatGPT") + "Project URL: https://github.com/acheong08/ChatGPT") super().__init__(*args) diff --git a/swarms/structs/document.py b/swarms/structs/document.py index b87d3d91..505df6ae 100644 --- a/swarms/structs/document.py +++ b/swarms/structs/document.py @@ -63,9 +63,8 @@ class BaseDocumentTransformer(ABC): """ # noqa: E501 @abstractmethod - def transform_documents( - self, documents: Sequence[Document], **kwargs: Any - ) -> Sequence[Document]: + def transform_documents(self, documents: Sequence[Document], + **kwargs: Any) -> Sequence[Document]: """Transform a list of documents. Args: @@ -75,9 +74,8 @@ class BaseDocumentTransformer(ABC): A list of transformed Documents. """ - async def atransform_documents( - self, documents: Sequence[Document], **kwargs: Any - ) -> Sequence[Document]: + async def atransform_documents(self, documents: Sequence[Document], + **kwargs: Any) -> Sequence[Document]: """Asynchronously transform a list of documents. Args: @@ -87,5 +85,4 @@ class BaseDocumentTransformer(ABC): A list of transformed Documents. """ return await asyncio.get_running_loop().run_in_executor( - None, partial(self.transform_documents, **kwargs), documents - ) + None, partial(self.transform_documents, **kwargs), documents) diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index 7be03036..a7a19258 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -19,14 +19,12 @@ from termcolor import colored import inspect import random - # Prompts DYNAMIC_STOP_PROMPT = """ When you have finished the task from the Human, output a special token: This will enable you to leave the autonomous loop. """ - # Constants FLOW_SYSTEM_PROMPT = f""" You are an autonomous agent granted autonomy from a Flow structure. @@ -40,7 +38,6 @@ to aid in these complex tasks. Your responses should be coherent, contextually r """ - # Utility functions @@ -184,8 +181,7 @@ class Flow: value = self.llm.__dict__.get(name, "Unknown") params_str_list.append( - f" {name.capitalize().replace('_', ' ')}: {value}" - ) + f" {name.capitalize().replace('_', ' ')}: {value}") return "\n".join(params_str_list) @@ -193,7 +189,7 @@ class Flow: """ Take the history and truncate it to fit into the model context length """ - truncated_history = self.memory[-1][-self.context_length :] + truncated_history = self.memory[-1][-self.context_length:] self.memory[-1] = truncated_history def add_task_to_memory(self, task: str): @@ -243,8 +239,7 @@ class Flow: ---------------------------------------- """, "green", - ) - ) + )) # print(dashboard) @@ -254,18 +249,17 @@ class Flow: print(colored("Initializing Autonomous Agent...", "yellow")) # print(colored("Loading modules...", "yellow")) # print(colored("Modules loaded successfully.", "green")) - print(colored("Autonomous Agent Activated.", "cyan", attrs=["bold"])) - print(colored("All systems operational. Executing task...", "green")) + print(colored("Autonomous Agent Activated.", "cyan", + attrs=["bold"])) + print(colored("All systems operational. Executing task...", + "green")) except Exception as error: print( colored( - ( - "Error activating autonomous agent. Try optimizing your" - " parameters..." - ), + ("Error activating autonomous agent. Try optimizing your" + " parameters..."), "red", - ) - ) + )) print(error) def run(self, task: str, **kwargs): @@ -307,7 +301,8 @@ class Flow: for i in range(self.max_loops): print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) print("\n") - if self._check_stopping_condition(response) or parse_done_token(response): + if self._check_stopping_condition(response) or parse_done_token( + response): break # Adjust temperature, comment if no work @@ -351,7 +346,6 @@ class Flow: async def arun(self, task: str, **kwargs): """Async run""" pass - """ Run the autonomous agent loop @@ -387,7 +381,8 @@ class Flow: for i in range(self.max_loops): print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) print("\n") - if self._check_stopping_condition(response) or parse_done_token(response): + if self._check_stopping_condition(response) or parse_done_token( + response): break # Adjust temperature, comment if no work @@ -565,7 +560,9 @@ class Flow: import boto3 s3 = boto3.client("s3") - s3.put_object(Bucket=bucket_name, Key=object_name, Body=json.dumps(self.memory)) + s3.put_object(Bucket=bucket_name, + Key=object_name, + Body=json.dumps(self.memory)) print(f"Backed up memory to S3: {bucket_name}/{object_name}") def analyze_feedback(self): @@ -684,8 +681,8 @@ class Flow: if hasattr(self.llm, name): value = getattr(self.llm, name) if isinstance( - value, (str, int, float, bool, list, dict, tuple, type(None)) - ): + value, + (str, int, float, bool, list, dict, tuple, type(None))): llm_params[name] = value else: llm_params[name] = str( @@ -745,7 +742,10 @@ class Flow: print(f"Flow state loaded from {file_path}") - def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1): + def retry_on_failure(self, + function, + retries: int = 3, + retry_delay: int = 1): """Retry wrapper for LLM calls.""" attempt = 0 while attempt < retries: diff --git a/swarms/structs/nonlinear_workflow.py b/swarms/structs/nonlinear_workflow.py index 2357f614..140c0d7b 100644 --- a/swarms/structs/nonlinear_workflow.py +++ b/swarms/structs/nonlinear_workflow.py @@ -8,9 +8,10 @@ class Task: Task is a unit of work that can be executed by an agent """ - def __init__( - self, id: str, parents: List["Task"] = None, children: List["Task"] = None - ): + def __init__(self, + id: str, + parents: List["Task"] = None, + children: List["Task"] = None): self.id = id self.parents = parents self.children = children @@ -79,7 +80,8 @@ class NonLinearWorkflow: for task in ordered_tasks: if task.can_execute: - future = self.executor.submit(self.agents.run, task.task_string) + future = self.executor.submit(self.agents.run, + task.task_string) futures_list[future] = task for future in as_completed(futures_list): @@ -95,7 +97,8 @@ class NonLinearWorkflow: def to_graph(self) -> Dict[str, set[str]]: """Convert the workflow to a graph""" graph = { - task.id: set(child.id for child in task.children) for task in self.tasks + task.id: set(child.id for child in task.children) + for task in self.tasks } return graph diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index 8c7d9760..8dd5abbd 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -61,13 +61,12 @@ class Task: if isinstance(self.flow, Flow): # Add a prompt to notify the Flow of the sequential workflow if "prompt" in self.kwargs: - self.kwargs["prompt"] += ( - f"\n\nPrevious output: {self.result}" if self.result else "" - ) + self.kwargs["prompt"] += (f"\n\nPrevious output: {self.result}" + if self.result else "") else: self.kwargs["prompt"] = f"Main task: {self.description}" + ( - f"\n\nPrevious output: {self.result}" if self.result else "" - ) + f"\n\nPrevious output: {self.result}" + if self.result else "") self.result = self.flow.run(*self.args, **self.kwargs) else: self.result = self.flow(*self.args, **self.kwargs) @@ -111,7 +110,8 @@ class SequentialWorkflow: restore_state_filepath: Optional[str] = None dashboard: bool = False - def add(self, task: str, flow: Union[Callable, Flow], *args, **kwargs) -> None: + def add(self, task: str, flow: Union[Callable, Flow], *args, + **kwargs) -> None: """ Add a task to the workflow. @@ -127,8 +127,7 @@ class SequentialWorkflow: # Append the task to the tasks list self.tasks.append( - Task(description=task, flow=flow, args=list(args), kwargs=kwargs) - ) + Task(description=task, flow=flow, args=list(args), kwargs=kwargs)) def reset_workflow(self) -> None: """Resets the workflow by clearing the results of each task.""" @@ -180,8 +179,9 @@ class SequentialWorkflow: raise ValueError(f"Task {task_description} not found in workflow.") def save_workflow_state( - self, filepath: Optional[str] = "sequential_workflow_state.json", **kwargs - ) -> None: + self, + filepath: Optional[str] = "sequential_workflow_state.json", + **kwargs) -> None: """ Saves the workflow state to a json file. @@ -202,16 +202,13 @@ class SequentialWorkflow: with open(filepath, "w") as f: # Saving the state as a json for simplicuty state = { - "tasks": [ - { - "description": task.description, - "args": task.args, - "kwargs": task.kwargs, - "result": task.result, - "history": task.history, - } - for task in self.tasks - ], + "tasks": [{ + "description": task.description, + "args": task.args, + "kwargs": task.kwargs, + "result": task.result, + "history": task.history, + } for task in self.tasks], "max_loops": self.max_loops, } json.dump(state, f, indent=4) @@ -223,8 +220,7 @@ class SequentialWorkflow: Sequential Workflow Initializing...""", "green", attrs=["bold", "underline"], - ) - ) + )) def workflow_dashboard(self, **kwargs) -> None: """ @@ -263,8 +259,7 @@ class SequentialWorkflow: """, "cyan", attrs=["bold", "underline"], - ) - ) + )) def workflow_shutdown(self, **kwargs) -> None: print( @@ -273,8 +268,7 @@ class SequentialWorkflow: Sequential Workflow Shutdown...""", "red", attrs=["bold", "underline"], - ) - ) + )) def add_objective_to_workflow(self, task: str, **kwargs) -> None: print( @@ -283,8 +277,7 @@ class SequentialWorkflow: Adding Objective to Workflow...""", "green", attrs=["bold", "underline"], - ) - ) + )) task = Task( description=task, @@ -349,13 +342,12 @@ class SequentialWorkflow: if "task" not in task.kwargs: raise ValueError( "The 'task' argument is required for the Flow flow" - f" execution in '{task.description}'" - ) + f" execution in '{task.description}'") # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") - task.result = task.flow.run( - flow_task_arg, *task.args, **task.kwargs - ) + task.result = task.flow.run(flow_task_arg, + *task.args, + **task.kwargs) else: # If it's not a Flow instance, call the flow directly task.result = task.flow(*task.args, **task.kwargs) @@ -373,19 +365,17 @@ class SequentialWorkflow: # Autosave the workflow state if self.autosave: - self.save_workflow_state("sequential_workflow_state.json") + self.save_workflow_state( + "sequential_workflow_state.json") except Exception as e: print( colored( - ( - f"Error initializing the Sequential workflow: {e} try" - " optimizing your inputs like the flow class and task" - " description" - ), + (f"Error initializing the Sequential workflow: {e} try" + " optimizing your inputs like the flow class and task" + " description"), "red", attrs=["bold", "underline"], - ) - ) + )) async def arun(self) -> None: """ @@ -405,13 +395,11 @@ class SequentialWorkflow: if "task" not in task.kwargs: raise ValueError( "The 'task' argument is required for the Flow flow" - f" execution in '{task.description}'" - ) + f" execution in '{task.description}'") # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") task.result = await task.flow.arun( - flow_task_arg, *task.args, **task.kwargs - ) + flow_task_arg, *task.args, **task.kwargs) else: # If it's not a Flow instance, call the flow directly task.result = await task.flow(*task.args, **task.kwargs) @@ -429,4 +417,5 @@ class SequentialWorkflow: # Autosave the workflow state if self.autosave: - self.save_workflow_state("sequential_workflow_state.json") + self.save_workflow_state( + "sequential_workflow_state.json") diff --git a/swarms/structs/task.py b/swarms/structs/task.py index 80f95d4d..6824bf0e 100644 --- a/swarms/structs/task.py +++ b/swarms/structs/task.py @@ -13,6 +13,7 @@ from swarms.artifacts.error_artifact import ErrorArtifact class BaseTask(ABC): + class State(Enum): PENDING = 1 EXECUTING = 2 @@ -33,11 +34,15 @@ class BaseTask(ABC): @property def parents(self) -> List[BaseTask]: - return [self.structure.find_task(parent_id) for parent_id in self.parent_ids] + return [ + self.structure.find_task(parent_id) for parent_id in self.parent_ids + ] @property def children(self) -> List[BaseTask]: - return [self.structure.find_task(child_id) for child_id in self.child_ids] + return [ + self.structure.find_task(child_id) for child_id in self.child_ids + ] def __rshift__(self, child: BaseTask) -> BaseTask: return self.add_child(child) @@ -118,8 +123,7 @@ class BaseTask(ABC): def can_execute(self) -> bool: return self.state == self.State.PENDING and all( - parent.is_finished() for parent in self.parents - ) + parent.is_finished() for parent in self.parents) def reset(self) -> BaseTask: self.state = self.State.PENDING @@ -132,10 +136,10 @@ class BaseTask(ABC): class Task(BaseModel): - input: Optional[StrictStr] = Field(None, description="Input prompt for the task") + input: Optional[StrictStr] = Field(None, + description="Input prompt for the task") additional_input: Optional[Any] = Field( - None, description="Input parameters for the task. Any value is allowed" - ) + None, description="Input parameters for the task. Any value is allowed") task_id: StrictStr = Field(..., description="ID of the task") class Config: diff --git a/swarms/structs/workflow.py b/swarms/structs/workflow.py index 762ee6cc..e4a841ed 100644 --- a/swarms/structs/workflow.py +++ b/swarms/structs/workflow.py @@ -65,11 +65,13 @@ class Workflow: def context(self, task: Task) -> Dict[str, Any]: """Context in tasks""" return { - "parent_output": task.parents[0].output - if task.parents and task.parents[0].output - else None, - "parent": task.parents[0] if task.parents else None, - "child": task.children[0] if task.children else None, + "parent_output": + task.parents[0].output + if task.parents and task.parents[0].output else None, + "parent": + task.parents[0] if task.parents else None, + "child": + task.children[0] if task.children else None, } def __run_from_task(self, task: Optional[Task]) -> None: diff --git a/swarms/swarms/autoscaler.py b/swarms/swarms/autoscaler.py index 5f6bedde..d0aaa598 100644 --- a/swarms/swarms/autoscaler.py +++ b/swarms/swarms/autoscaler.py @@ -87,7 +87,8 @@ class AutoScaler: while True: sleep(60) # check minute pending_tasks = self.task_queue.qsize() - active_agents = sum([1 for agent in self.agents_pool if agent.is_busy()]) + active_agents = sum( + [1 for agent in self.agents_pool if agent.is_busy()]) if pending_tasks / len(self.agents_pool) > self.busy_threshold: self.scale_up() diff --git a/swarms/swarms/base.py b/swarms/swarms/base.py index e99c9b38..6d8e0163 100644 --- a/swarms/swarms/base.py +++ b/swarms/swarms/base.py @@ -117,7 +117,9 @@ class AbstractSwarm(ABC): pass @abstractmethod - def broadcast(self, message: str, sender: Optional["AbstractWorker"] = None): + def broadcast(self, + message: str, + sender: Optional["AbstractWorker"] = None): """Broadcast a message to all workers""" pass diff --git a/swarms/swarms/battle_royal.py b/swarms/swarms/battle_royal.py index 2a02186e..7b5c2a99 100644 --- a/swarms/swarms/battle_royal.py +++ b/swarms/swarms/battle_royal.py @@ -77,19 +77,15 @@ class BattleRoyalSwarm: # Check for clashes and handle them for i, worker1 in enumerate(self.workers): for j, worker2 in enumerate(self.workers): - if ( - i != j - and worker1.is_within_proximity(worker2) - and set(worker1.teams) != set(worker2.teams) - ): + if (i != j and worker1.is_within_proximity(worker2) and + set(worker1.teams) != set(worker2.teams)): winner, loser = self.clash(worker1, worker2, question) print(f"Worker {winner.id} won over Worker {loser.id}") def communicate(self, sender: Worker, reciever: Worker, message: str): """Communicate a message from one worker to another.""" if sender.is_within_proximity(reciever) or any( - team in sender.teams for team in reciever.teams - ): + team in sender.teams for team in reciever.teams): pass def clash(self, worker1: Worker, worker2: Worker, question: str): diff --git a/swarms/swarms/god_mode.py b/swarms/swarms/god_mode.py index fe842f0a..7f302318 100644 --- a/swarms/swarms/god_mode.py +++ b/swarms/swarms/god_mode.py @@ -49,9 +49,8 @@ class GodMode: table.append([f"LLM {i+1}", response]) print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" - ) - ) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), + "cyan")) def run_all(self, task): """Run the task on all LLMs""" @@ -74,18 +73,15 @@ class GodMode: table.append([f"LLM {i+1}", response]) print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" - ) - ) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), + "cyan")) # New Features def save_responses_to_file(self, filename): """Save responses to file""" with open(filename, "w") as file: - table = [ - [f"LLM {i+1}", response] - for i, response in enumerate(self.last_responses) - ] + table = [[f"LLM {i+1}", response] + for i, response in enumerate(self.last_responses)] file.write(tabulate(table, headers=["LLM", "Response"])) @classmethod @@ -105,11 +101,9 @@ class GodMode: for i, task in enumerate(self.task_history): print(f"{i + 1}. {task}") print("\nLast Responses:") - table = [ - [f"LLM {i+1}", response] for i, response in enumerate(self.last_responses) - ] + table = [[f"LLM {i+1}", response] + for i, response in enumerate(self.last_responses)] print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" - ) - ) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), + "cyan")) diff --git a/swarms/swarms/groupchat.py b/swarms/swarms/groupchat.py index dd3e36a2..842ebac9 100644 --- a/swarms/swarms/groupchat.py +++ b/swarms/swarms/groupchat.py @@ -3,7 +3,6 @@ from dataclasses import dataclass from typing import Dict, List from swarms.structs.flow import Flow - logger = logging.getLogger(__name__) @@ -34,7 +33,8 @@ class GroupChat: def next_agent(self, agent: Flow) -> Flow: """Return the next agent in the list.""" - return self.agents[(self.agent_names.index(agent.name) + 1) % len(self.agents)] + return self.agents[(self.agent_names.index(agent.name) + 1) % + len(self.agents)] def select_speaker_msg(self): """Return the message for selecting the next speaker.""" @@ -55,24 +55,17 @@ class GroupChat: if n_agents < 3: logger.warning( f"GroupChat is underpopulated with {n_agents} agents. Direct" - " communication would be more efficient." - ) + " communication would be more efficient.") name = selector.generate_reply( - self.format_history( - self.messages - + [ - { - "role": "system", - "content": ( - "Read the above conversation. Then select the next most" - f" suitable role from {self.agent_names} to play. Only" - " return the role." - ), - } - ] - ) - ) + self.format_history(self.messages + [{ + "role": + "system", + "content": + ("Read the above conversation. Then select the next most" + f" suitable role from {self.agent_names} to play. Only" + " return the role."), + }])) try: return self.agent_by_name(name["content"]) except ValueError: @@ -80,8 +73,7 @@ class GroupChat: def _participant_roles(self): return "\n".join( - [f"{agent.name}: {agent.system_message}" for agent in self.agents] - ) + [f"{agent.name}: {agent.system_message}" for agent in self.agents]) def format_history(self, messages: List[Dict]) -> str: formatted_messages = [] @@ -92,19 +84,21 @@ class GroupChat: class GroupChatManager: + def __init__(self, groupchat: GroupChat, selector: Flow): self.groupchat = groupchat self.selector = selector def __call__(self, task: str): - self.groupchat.messages.append({"role": self.selector.name, "content": task}) + self.groupchat.messages.append({ + "role": self.selector.name, + "content": task + }) for i in range(self.groupchat.max_round): - speaker = self.groupchat.select_speaker( - last_speaker=self.selector, selector=self.selector - ) + speaker = self.groupchat.select_speaker(last_speaker=self.selector, + selector=self.selector) reply = speaker.generate_reply( - self.groupchat.format_history(self.groupchat.messages) - ) + self.groupchat.format_history(self.groupchat.messages)) self.groupchat.messages.append(reply) print(reply) if i == self.groupchat.max_round - 1: diff --git a/swarms/swarms/multi_agent_collab.py b/swarms/swarms/multi_agent_collab.py index 9a5f27bc..a3b79d7f 100644 --- a/swarms/swarms/multi_agent_collab.py +++ b/swarms/swarms/multi_agent_collab.py @@ -5,16 +5,16 @@ from langchain.output_parsers import RegexParser # utils class BidOutputParser(RegexParser): + def get_format_instructions(self) -> str: return ( "Your response should be an integrater delimited by angled brackets like" - " this: " - ) + " this: ") -bid_parser = BidOutputParser( - regex=r"<(\d+)>", output_keys=["bid"], default_output_key="bid" -) +bid_parser = BidOutputParser(regex=r"<(\d+)>", + output_keys=["bid"], + default_output_key="bid") def select_next_speaker(step: int, agents, director) -> int: @@ -29,6 +29,7 @@ def select_next_speaker(step: int, agents, director) -> int: # main class MultiAgentCollaboration: + def __init__( self, agents, diff --git a/swarms/swarms/multi_agent_debate.py b/swarms/swarms/multi_agent_debate.py index 4bba3619..1c7ebdf9 100644 --- a/swarms/swarms/multi_agent_debate.py +++ b/swarms/swarms/multi_agent_debate.py @@ -46,7 +46,6 @@ class MultiAgentDebate: def format_results(self, results): formatted_results = "\n".join( - [f"Agent responded: {result['response']}" for result in results] - ) + [f"Agent responded: {result['response']}" for result in results]) return formatted_results diff --git a/swarms/swarms/orchestrate.py b/swarms/swarms/orchestrate.py index f522911b..d47771ab 100644 --- a/swarms/swarms/orchestrate.py +++ b/swarms/swarms/orchestrate.py @@ -111,7 +111,8 @@ class Orchestrator: self.chroma_client = chromadb.Client() - self.collection = self.chroma_client.create_collection(name=collection_name) + self.collection = self.chroma_client.create_collection( + name=collection_name) self.current_tasks = {} @@ -137,9 +138,8 @@ class Orchestrator: result = self.worker.run(task["content"]) # using the embed method to get the vector representation of the result - vector_representation = self.embed( - result, self.api_key, self.model_name - ) + vector_representation = self.embed(result, self.api_key, + self.model_name) self.collection.add( embeddings=[vector_representation], @@ -154,8 +154,7 @@ class Orchestrator: except Exception as error: logging.error( f"Failed to process task {id(task)} by agent {id(agent)}. Error:" - f" {error}" - ) + f" {error}") finally: with self.condition: self.agents.put(agent) @@ -163,8 +162,7 @@ class Orchestrator: def embed(self, input, api_key, model_name): openai = embedding_functions.OpenAIEmbeddingFunction( - api_key=api_key, model_name=model_name - ) + api_key=api_key, model_name=model_name) embedding = openai(input) return embedding @@ -175,13 +173,13 @@ class Orchestrator: try: # Query the vector database for documents created by the agents - results = self.collection.query(query_texts=[str(agent_id)], n_results=10) + results = self.collection.query(query_texts=[str(agent_id)], + n_results=10) return results except Exception as e: logging.error( - f"Failed to retrieve results from agent {agent_id}. Error {e}" - ) + f"Failed to retrieve results from agent {agent_id}. Error {e}") raise # @abstractmethod @@ -212,7 +210,8 @@ class Orchestrator: self.collection.add(documents=[result], ids=[str(id(result))]) except Exception as e: - logging.error(f"Failed to append the agent output to database. Error: {e}") + logging.error( + f"Failed to append the agent output to database. Error: {e}") raise def run(self, objective: str): @@ -225,8 +224,8 @@ class Orchestrator: self.task_queue.append(objective) results = [ - self.assign_task(agent_id, task) - for agent_id, task in zip(range(len(self.agents)), self.task_queue) + self.assign_task(agent_id, task) for agent_id, task in zip( + range(len(self.agents)), self.task_queue) ] for result in results: diff --git a/swarms/swarms/simple_swarm.py b/swarms/swarms/simple_swarm.py index 7e806215..a382c0d7 100644 --- a/swarms/swarms/simple_swarm.py +++ b/swarms/swarms/simple_swarm.py @@ -2,6 +2,7 @@ from queue import Queue, PriorityQueue class SimpleSwarm: + def __init__( self, llm, diff --git a/swarms/tools/autogpt.py b/swarms/tools/autogpt.py index cf5450e6..270504aa 100644 --- a/swarms/tools/autogpt.py +++ b/swarms/tools/autogpt.py @@ -8,8 +8,7 @@ import torch from langchain.agents import tool from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent from langchain.chains.qa_with_sources.loading import ( - BaseCombineDocumentsChain, -) + BaseCombineDocumentsChain,) from langchain.docstore.document import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.tools import BaseTool @@ -37,9 +36,10 @@ def pushd(new_dir): @tool -def process_csv( - llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None -) -> str: +def process_csv(llm, + csv_file_path: str, + instructions: str, + output_path: Optional[str] = None) -> str: """Process a CSV by with pandas in a limited REPL.\ Only use this after writing data to disk as a csv file.\ Any figures must be saved to disk to be viewed by the human.\ @@ -49,7 +49,10 @@ def process_csv( df = pd.read_csv(csv_file_path) except Exception as e: return f"Error: {e}" - agent = create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=False) + agent = create_pandas_dataframe_agent(llm, + df, + max_iterations=30, + verbose=False) if output_path is not None: instructions += f" Save output to disk at {output_path}" try: @@ -79,7 +82,8 @@ async def async_load_playwright(url: str) -> str: text = soup.get_text() lines = (line.strip() for line in text.splitlines()) - chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) + chunks = ( + phrase.strip() for line in lines for phrase in line.split(" ")) results = "\n".join(chunk for chunk in chunks if chunk) except Exception as e: results = f"Error: {e}" @@ -113,8 +117,7 @@ class WebpageQATool(BaseTool): "Browse a webpage and retrieve the information relevant to the question." ) text_splitter: RecursiveCharacterTextSplitter = Field( - default_factory=_get_text_splitter - ) + default_factory=_get_text_splitter) qa_chain: BaseCombineDocumentsChain def _run(self, url: str, question: str) -> str: @@ -125,9 +128,12 @@ class WebpageQATool(BaseTool): results = [] # TODO: Handle this with a MapReduceChain for i in range(0, len(web_docs), 4): - input_docs = web_docs[i : i + 4] + input_docs = web_docs[i:i + 4] window_result = self.qa_chain( - {"input_documents": input_docs, "question": question}, + { + "input_documents": input_docs, + "question": question + }, return_only_outputs=True, ) results.append(f"Response from window {i} - {window_result}") @@ -135,7 +141,10 @@ class WebpageQATool(BaseTool): Document(page_content="\n".join(results), metadata={"source": url}) ] return self.qa_chain( - {"input_documents": results_docs, "question": question}, + { + "input_documents": results_docs, + "question": question + }, return_only_outputs=True, ) @@ -171,18 +180,17 @@ def VQAinference(self, inputs): torch_dtype = torch.float16 if "cuda" in device else torch.float32 processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", torch_dtype=torch_dtype - ).to(device) + "Salesforce/blip-vqa-base", torch_dtype=torch_dtype).to(device) image_path, question = inputs.split(",") raw_image = Image.open(image_path).convert("RGB") - inputs = processor(raw_image, question, return_tensors="pt").to(device, torch_dtype) + inputs = processor(raw_image, question, + return_tensors="pt").to(device, torch_dtype) out = model.generate(**inputs) answer = processor.decode(out[0], skip_special_tokens=True) logger.debug( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}" - ) + f" Question: {question}, Output Answer: {answer}") return answer diff --git a/swarms/tools/mm_models.py b/swarms/tools/mm_models.py index 58fe11e5..fd115bd6 100644 --- a/swarms/tools/mm_models.py +++ b/swarms/tools/mm_models.py @@ -25,13 +25,14 @@ from swarms.utils.main import BaseHandler, get_new_image_name class MaskFormer: + def __init__(self, device): print("Initializing MaskFormer to %s" % device) self.device = device - self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") + self.processor = CLIPSegProcessor.from_pretrained( + "CIDAS/clipseg-rd64-refined") self.model = CLIPSegForImageSegmentation.from_pretrained( - "CIDAS/clipseg-rd64-refined" - ).to(device) + "CIDAS/clipseg-rd64-refined").to(device) def inference(self, image_path, text): threshold = 0.5 @@ -39,9 +40,10 @@ class MaskFormer: padding = 20 original_image = Image.open(image_path) image = original_image.resize((512, 512)) - inputs = self.processor( - text=text, images=image, padding="max_length", return_tensors="pt" - ).to(self.device) + inputs = self.processor(text=text, + images=image, + padding="max_length", + return_tensors="pt").to(self.device) with torch.no_grad(): outputs = self.model(**inputs) mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold @@ -52,8 +54,7 @@ class MaskFormer: mask_array = np.zeros_like(mask, dtype=bool) for idx in true_indices: padded_slice = tuple( - slice(max(0, i - padding), i + padding + 1) for i in idx - ) + slice(max(0, i - padding), i + padding + 1) for i in idx) mask_array[padded_slice] = True visual_mask = (mask_array * 255).astype(np.uint8) image_mask = Image.fromarray(visual_mask) @@ -61,6 +62,7 @@ class MaskFormer: class ImageEditing: + def __init__(self, device): print("Initializing ImageEditing to %s" % device) self.device = device @@ -75,25 +77,24 @@ class ImageEditing: @tool( name="Remove Something From The Photo", - description=( - "useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. " - ), + description= + ("useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. "), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",") - return self.inference_replace(f"{image_path},{to_be_removed_txt},background") + return self.inference_replace( + f"{image_path},{to_be_removed_txt},background") @tool( name="Replace Something From The Photo", - description=( - "useful when you want to replace an object from the object description or" - " location with another object from its description. The input to this tool" - " should be a comma separated string of three, representing the image_path," - " the object to be replaced, the object to be replaced with " - ), + description= + ("useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with "), ) def inference_replace(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") @@ -105,22 +106,21 @@ class ImageEditing: image=original_image.resize((512, 512)), mask_image=mask_image.resize((512, 512)), ).images[0] - updated_image_path = get_new_image_name( - image_path, func_name="replace-something" - ) + updated_image_path = get_new_image_name(image_path, + func_name="replace-something") updated_image = updated_image.resize(original_size) updated_image.save(updated_image_path) logger.debug( f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" - f" {updated_image_path}" - ) + f" {updated_image_path}") return updated_image_path class InstructPix2Pix: + def __init__(self, device): print("Initializing InstructPix2Pix to %s" % device) self.device = device @@ -131,60 +131,56 @@ class InstructPix2Pix: torch_dtype=self.torch_dtype, ).to(device) self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( - self.pipe.scheduler.config - ) + self.pipe.scheduler.config) @tool( name="Instruct Image Using Text", - description=( - "useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. " - ), + description= + ("useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. "), ) def inference(self, inputs): """Change style of image.""" logger.debug("===> Starting InstructPix2Pix Inference") image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) original_image = Image.open(image_path) - image = self.pipe( - text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2 - ).images[0] + image = self.pipe(text, + image=original_image, + num_inference_steps=40, + image_guidance_scale=1.2).images[0] updated_image_path = get_new_image_name(image_path, func_name="pix2pix") image.save(updated_image_path) logger.debug( f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" - f" {text}, Output Image: {updated_image_path}" - ) + f" {text}, Output Image: {updated_image_path}") return updated_image_path class Text2Image: + def __init__(self, device): print("Initializing Text2Image to %s" % device) self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.pipe = StableDiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype - ) + "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype) self.pipe.to(device) self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality" - ) + "fewer digits, cropped, worst quality, low quality") @tool( name="Generate Image From User Input Text", - description=( - "useful when you want to generate an image from a user input text and save" - " it to a file. like: generate an image of an object or something, or" - " generate an image that includes some objects. The input to this tool" - " should be a string, representing the text used to generate image. " - ), + description= + ("useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. "), ) def inference(self, text): image_filename = os.path.join("image", str(uuid.uuid4())[0:8] + ".png") @@ -194,59 +190,59 @@ class Text2Image: logger.debug( f"\nProcessed Text2Image, Input Text: {text}, Output Image:" - f" {image_filename}" - ) + f" {image_filename}") return image_filename class VisualQuestionAnswering: + def __init__(self, device): print("Initializing VisualQuestionAnswering to %s" % device) self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.device = device - self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") + self.processor = BlipProcessor.from_pretrained( + "Salesforce/blip-vqa-base") self.model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype - ).to(self.device) + "Salesforce/blip-vqa-base", + torch_dtype=self.torch_dtype).to(self.device) @tool( name="Answer Question About The Image", - description=( - "useful when you need an answer for a question based on an image. like:" - " what is the background color of the last image, how many cats in this" - " figure, what is in this figure. The input to this tool should be a comma" - " separated string of two, representing the image_path and the question" + description= + ("useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" ), ) def inference(self, inputs): image_path, question = inputs.split(",") raw_image = Image.open(image_path).convert("RGB") - inputs = self.processor(raw_image, question, return_tensors="pt").to( - self.device, self.torch_dtype - ) + inputs = self.processor(raw_image, question, + return_tensors="pt").to(self.device, + self.torch_dtype) out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) logger.debug( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}" - ) + f" Question: {question}, Output Answer: {answer}") return answer class ImageCaptioning(BaseHandler): + def __init__(self, device): print("Initializing ImageCaptioning to %s" % device) self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-image-captioning-base" - ) + "Salesforce/blip-image-captioning-base") self.model = BlipForConditionalGeneration.from_pretrained( - "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype - ).to(self.device) + "Salesforce/blip-image-captioning-base", + torch_dtype=self.torch_dtype).to(self.device) def handle(self, filename: str): img = Image.open(filename) @@ -258,14 +254,13 @@ class ImageCaptioning(BaseHandler): img.save(filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") - inputs = self.processor(Image.open(filename), return_tensors="pt").to( - self.device, self.torch_dtype - ) + inputs = self.processor(Image.open(filename), + return_tensors="pt").to(self.device, + self.torch_dtype) out = self.model.generate(**inputs) description = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text:" - f" {description}" - ) + f" {description}") return IMAGE_PROMPT.format(filename=filename, description=description) diff --git a/swarms/tools/stt.py b/swarms/tools/stt.py index cfe3e656..da9d7f27 100644 --- a/swarms/tools/stt.py +++ b/swarms/tools/stt.py @@ -9,6 +9,7 @@ from pytube import YouTube class SpeechToText: + def __init__( self, video_url, @@ -61,14 +62,15 @@ class SpeechToText: compute_type = "float16" # 1. Transcribe with original Whisper (batched) ๐Ÿ—ฃ๏ธ - model = whisperx.load_model("large-v2", device, compute_type=compute_type) + model = whisperx.load_model("large-v2", + device, + compute_type=compute_type) audio = whisperx.load_audio(audio_file) result = model.transcribe(audio, batch_size=batch_size) # 2. Align Whisper output ๐Ÿ” model_a, metadata = whisperx.load_align_model( - language_code=result["language"], device=device - ) + language_code=result["language"], device=device) result = whisperx.align( result["segments"], model_a, @@ -80,8 +82,7 @@ class SpeechToText: # 3. Assign speaker labels ๐Ÿท๏ธ diarize_model = whisperx.DiarizationPipeline( - use_auth_token=self.hf_api_key, device=device - ) + use_auth_token=self.hf_api_key, device=device) diarize_model(audio_file) try: @@ -98,8 +99,7 @@ class SpeechToText: # 2. Align Whisper output ๐Ÿ” model_a, metadata = whisperx.load_align_model( - language_code=result["language"], device=self.device - ) + language_code=result["language"], device=self.device) result = whisperx.align( result["segments"], @@ -112,8 +112,7 @@ class SpeechToText: # 3. Assign speaker labels ๐Ÿท๏ธ diarize_model = whisperx.DiarizationPipeline( - use_auth_token=self.hf_api_key, device=self.device - ) + use_auth_token=self.hf_api_key, device=self.device) diarize_model(audio_file) diff --git a/swarms/tools/tool.py b/swarms/tools/tool.py index 1b1072a5..29b0f5de 100644 --- a/swarms/tools/tool.py +++ b/swarms/tools/tool.py @@ -34,9 +34,8 @@ class SchemaAnnotationError(TypeError): """Raised when 'args_schema' is missing or has an incorrect type annotation.""" -def _create_subset_model( - name: str, model: BaseModel, field_names: list -) -> Type[BaseModel]: +def _create_subset_model(name: str, model: BaseModel, + field_names: list) -> Type[BaseModel]: """Create a pydantic model with only a subset of model's fields.""" fields = {} for field_name in field_names: @@ -52,7 +51,11 @@ def _get_filtered_args( """Get the arguments from a function's signature.""" schema = inferred_model.schema()["properties"] valid_keys = signature(func).parameters - return {k: schema[k] for k in valid_keys if k not in ("run_manager", "callbacks")} + return { + k: schema[k] + for k in valid_keys + if k not in ("run_manager", "callbacks") + } class _SchemaConfig: @@ -82,9 +85,8 @@ def create_schema_from_function( del inferred_model.__fields__["callbacks"] # Pydantic adds placeholder virtual fields we need to strip valid_properties = _get_filtered_args(inferred_model, func) - return _create_subset_model( - f"{model_name}Schema", inferred_model, list(valid_properties) - ) + return _create_subset_model(f"{model_name}Schema", inferred_model, + list(valid_properties)) class ToolException(Exception): @@ -125,8 +127,7 @@ class ChildTool(BaseTool): "Expected annotation of 'Type[BaseModel]'" f" but got '{args_schema_type}'.\n" "Expected class looks like:\n" - f"{typehint_mandate}" - ) + f"{typehint_mandate}") name: str """The unique name of the tool that clearly communicates its purpose.""" @@ -147,7 +148,8 @@ class ChildTool(BaseTool): callbacks: Callbacks = Field(default=None, exclude=True) """Callbacks to be called during tool execution.""" - callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) + callback_manager: Optional[BaseCallbackManager] = Field(default=None, + exclude=True) """Deprecated. Please use callbacks instead.""" tags: Optional[List[str]] = None """Optional list of tags associated with the tool. Defaults to None @@ -162,9 +164,8 @@ class ChildTool(BaseTool): You can use these to eg identify a specific instance of a tool with its use case. """ - handle_tool_error: Optional[ - Union[bool, str, Callable[[ToolException], str]] - ] = False + handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], + str]]] = False """Handle the content of the ToolException thrown.""" class Config(Serializable.Config): @@ -244,7 +245,9 @@ class ChildTool(BaseTool): else: if input_args is not None: result = input_args.parse_obj(tool_input) - return {k: v for k, v in result.dict().items() if k in tool_input} + return { + k: v for k, v in result.dict().items() if k in tool_input + } return tool_input @root_validator() @@ -286,7 +289,8 @@ class ChildTool(BaseTool): *args, ) - def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: + def _to_args_and_kwargs(self, + tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: # For backwards compatibility, if run_input is a string, # pass as a positional argument. if isinstance(tool_input, str): @@ -325,7 +329,10 @@ class ChildTool(BaseTool): # TODO: maybe also pass through run_manager is _run supports kwargs new_arg_supported = signature(self._run).parameters.get("run_manager") run_manager = callback_manager.on_tool_start( - {"name": self.name, "description": self.description}, + { + "name": self.name, + "description": self.description + }, tool_input if isinstance(tool_input, str) else str(tool_input), color=start_color, name=run_name, @@ -335,9 +342,7 @@ class ChildTool(BaseTool): tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) observation = ( self._run(*tool_args, run_manager=run_manager, **tool_kwargs) - if new_arg_supported - else self._run(*tool_args, **tool_kwargs) - ) + if new_arg_supported else self._run(*tool_args, **tool_kwargs)) except ToolException as e: if not self.handle_tool_error: run_manager.on_tool_error(e) @@ -354,19 +359,20 @@ class ChildTool(BaseTool): else: raise ValueError( "Got unexpected type of `handle_tool_error`. Expected bool, str " - f"or callable. Received: {self.handle_tool_error}" - ) - run_manager.on_tool_end( - str(observation), color="red", name=self.name, **kwargs - ) + f"or callable. Received: {self.handle_tool_error}") + run_manager.on_tool_end(str(observation), + color="red", + name=self.name, + **kwargs) return observation except (Exception, KeyboardInterrupt) as e: run_manager.on_tool_error(e) raise e else: - run_manager.on_tool_end( - str(observation), color=color, name=self.name, **kwargs - ) + run_manager.on_tool_end(str(observation), + color=color, + name=self.name, + **kwargs) return observation async def arun( @@ -399,7 +405,10 @@ class ChildTool(BaseTool): ) new_arg_supported = signature(self._arun).parameters.get("run_manager") run_manager = await callback_manager.on_tool_start( - {"name": self.name, "description": self.description}, + { + "name": self.name, + "description": self.description + }, tool_input if isinstance(tool_input, str) else str(tool_input), color=start_color, name=run_name, @@ -408,11 +417,10 @@ class ChildTool(BaseTool): try: # We then call the tool on the tool input to get an observation tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) - observation = ( - await self._arun(*tool_args, run_manager=run_manager, **tool_kwargs) - if new_arg_supported - else await self._arun(*tool_args, **tool_kwargs) - ) + observation = (await self._arun(*tool_args, + run_manager=run_manager, + **tool_kwargs) if new_arg_supported + else await self._arun(*tool_args, **tool_kwargs)) except ToolException as e: if not self.handle_tool_error: await run_manager.on_tool_error(e) @@ -429,19 +437,20 @@ class ChildTool(BaseTool): else: raise ValueError( "Got unexpected type of `handle_tool_error`. Expected bool, str " - f"or callable. Received: {self.handle_tool_error}" - ) - await run_manager.on_tool_end( - str(observation), color="red", name=self.name, **kwargs - ) + f"or callable. Received: {self.handle_tool_error}") + await run_manager.on_tool_end(str(observation), + color="red", + name=self.name, + **kwargs) return observation except (Exception, KeyboardInterrupt) as e: await run_manager.on_tool_error(e) raise e else: - await run_manager.on_tool_end( - str(observation), color=color, name=self.name, **kwargs - ) + await run_manager.on_tool_end(str(observation), + color=color, + name=self.name, + **kwargs) return observation def __call__(self, tool_input: str, callbacks: Callbacks = None) -> str: @@ -459,7 +468,6 @@ class Tool(BaseTool): """The asynchronous version of the function.""" # --- Runnable --- - async def ainvoke( self, input: Union[str, Dict], @@ -469,8 +477,7 @@ class Tool(BaseTool): if not self.coroutine: # If the tool does not implement async, fall back to default implementation return await asyncio.get_running_loop().run_in_executor( - None, partial(self.invoke, input, config, **kwargs) - ) + None, partial(self.invoke, input, config, **kwargs)) return await super().ainvoke(input, config, **kwargs) @@ -485,7 +492,8 @@ class Tool(BaseTool): # assume it takes a single string input. return {"tool_input": {"type": "string"}} - def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: + def _to_args_and_kwargs(self, + tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: """Convert tool input to pydantic model.""" args, kwargs = super()._to_args_and_kwargs(tool_input) # For backwards compatibility. The tool must be run with a single input @@ -504,16 +512,13 @@ class Tool(BaseTool): ) -> Any: """Use the tool.""" if self.func: - new_argument_supported = signature(self.func).parameters.get("callbacks") - return ( - self.func( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) - if new_argument_supported - else self.func(*args, **kwargs) - ) + new_argument_supported = signature( + self.func).parameters.get("callbacks") + return (self.func( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) if new_argument_supported else self.func(*args, **kwargs)) raise NotImplementedError("Tool does not support sync") async def _arun( @@ -524,31 +529,27 @@ class Tool(BaseTool): ) -> Any: """Use the tool asynchronously.""" if self.coroutine: - new_argument_supported = signature(self.coroutine).parameters.get( - "callbacks" - ) - return ( - await self.coroutine( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) - if new_argument_supported - else await self.coroutine(*args, **kwargs) - ) + new_argument_supported = signature( + self.coroutine).parameters.get("callbacks") + return (await self.coroutine( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) if new_argument_supported else await self.coroutine( + *args, **kwargs)) else: return await asyncio.get_running_loop().run_in_executor( - None, partial(self._run, run_manager=run_manager, **kwargs), *args - ) + None, partial(self._run, run_manager=run_manager, **kwargs), + *args) # TODO: this is for backwards compatibility, remove in future - def __init__( - self, name: str, func: Optional[Callable], description: str, **kwargs: Any - ) -> None: + def __init__(self, name: str, func: Optional[Callable], description: str, + **kwargs: Any) -> None: """Initialize tool.""" - super(Tool, self).__init__( - name=name, func=func, description=description, **kwargs - ) + super(Tool, self).__init__(name=name, + func=func, + description=description, + **kwargs) @classmethod def from_function( @@ -558,9 +559,8 @@ class Tool(BaseTool): description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, - coroutine: Optional[ - Callable[..., Awaitable[Any]] - ] = None, # This is last for compatibility, but should be after func + coroutine: Optional[Callable[..., Awaitable[ + Any]]] = None, # This is last for compatibility, but should be after func **kwargs: Any, ) -> Tool: """Initialize tool from a function.""" @@ -589,7 +589,6 @@ class StructuredTool(BaseTool): """The asynchronous version of the function.""" # --- Runnable --- - async def ainvoke( self, input: Union[str, Dict], @@ -599,8 +598,7 @@ class StructuredTool(BaseTool): if not self.coroutine: # If the tool does not implement async, fall back to default implementation return await asyncio.get_running_loop().run_in_executor( - None, partial(self.invoke, input, config, **kwargs) - ) + None, partial(self.invoke, input, config, **kwargs)) return await super().ainvoke(input, config, **kwargs) @@ -619,16 +617,13 @@ class StructuredTool(BaseTool): ) -> Any: """Use the tool.""" if self.func: - new_argument_supported = signature(self.func).parameters.get("callbacks") - return ( - self.func( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) - if new_argument_supported - else self.func(*args, **kwargs) - ) + new_argument_supported = signature( + self.func).parameters.get("callbacks") + return (self.func( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) if new_argument_supported else self.func(*args, **kwargs)) raise NotImplementedError("Tool does not support sync") async def _arun( @@ -639,18 +634,14 @@ class StructuredTool(BaseTool): ) -> str: """Use the tool asynchronously.""" if self.coroutine: - new_argument_supported = signature(self.coroutine).parameters.get( - "callbacks" - ) - return ( - await self.coroutine( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) - if new_argument_supported - else await self.coroutine(*args, **kwargs) - ) + new_argument_supported = signature( + self.coroutine).parameters.get("callbacks") + return (await self.coroutine( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) if new_argument_supported else await self.coroutine( + *args, **kwargs)) return await asyncio.get_running_loop().run_in_executor( None, partial(self._run, run_manager=run_manager, **kwargs), @@ -707,8 +698,7 @@ class StructuredTool(BaseTool): description = description or source_function.__doc__ if description is None: raise ValueError( - "Function must have a docstring if description not provided." - ) + "Function must have a docstring if description not provided.") # Description example: # search_api(query: str) - Searches the API for the query. @@ -716,7 +706,8 @@ class StructuredTool(BaseTool): description = f"{name}{sig} - {description.strip()}" _args_schema = args_schema if _args_schema is None and infer_schema: - _args_schema = create_schema_from_function(f"{name}Schema", source_function) + _args_schema = create_schema_from_function(f"{name}Schema", + source_function) return cls( name=name, func=func, @@ -764,6 +755,7 @@ def tool( """ def _make_with_name(tool_name: str) -> Callable: + def _make_tool(dec_func: Union[Callable, Runnable]) -> BaseTool: if isinstance(dec_func, Runnable): runnable = dec_func @@ -771,14 +763,13 @@ def tool( if runnable.input_schema.schema().get("type") != "object": raise ValueError("Runnable must have an object schema.") - async def ainvoke_wrapper( - callbacks: Optional[Callbacks] = None, **kwargs: Any - ) -> Any: - return await runnable.ainvoke(kwargs, {"callbacks": callbacks}) + async def ainvoke_wrapper(callbacks: Optional[Callbacks] = None, + **kwargs: Any) -> Any: + return await runnable.ainvoke(kwargs, + {"callbacks": callbacks}) - def invoke_wrapper( - callbacks: Optional[Callbacks] = None, **kwargs: Any - ) -> Any: + def invoke_wrapper(callbacks: Optional[Callbacks] = None, + **kwargs: Any) -> Any: return runnable.invoke(kwargs, {"callbacks": callbacks}) coroutine = ainvoke_wrapper @@ -811,8 +802,7 @@ def tool( if func.__doc__ is None: raise ValueError( "Function must have a docstring if " - "description not provided and infer_schema is False." - ) + "description not provided and infer_schema is False.") return Tool( name=tool_name, func=func, @@ -823,7 +813,8 @@ def tool( return _make_tool - if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable): + if len(args) == 2 and isinstance(args[0], str) and isinstance( + args[1], Runnable): return _make_with_name(args[0])(args[1]) elif len(args) == 1 and isinstance(args[0], str): # if the argument is a string, then we use the string as the tool name diff --git a/swarms/tools/tool_registry.py b/swarms/tools/tool_registry.py index 5aa544e9..3354646a 100644 --- a/swarms/tools/tool_registry.py +++ b/swarms/tools/tool_registry.py @@ -6,6 +6,7 @@ FuncToolBuilder = Callable[[], ToolBuilder] class ToolsRegistry: + def __init__(self) -> None: self.tools: Dict[str, FuncToolBuilder] = {} @@ -18,8 +19,7 @@ class ToolsRegistry: if isinstance(ret, tool): return ret raise ValueError( - "Tool builder {} did not return a Tool instance".format(tool_name) - ) + "Tool builder {} did not return a Tool instance".format(tool_name)) def list_tools(self) -> List[str]: return list(self.tools.keys()) @@ -29,6 +29,7 @@ tools_registry = ToolsRegistry() def register(tool_name): + def decorator(tool: FuncToolBuilder): tools_registry.register(tool_name, tool) return tool diff --git a/swarms/utils/code_interpreter.py b/swarms/utils/code_interpreter.py index 80eb6700..c89ac7a7 100644 --- a/swarms/utils/code_interpreter.py +++ b/swarms/utils/code_interpreter.py @@ -118,14 +118,19 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): # Most of the time it doesn't matter, but we should figure out why it happens frequently with: # applescript yield {"output": traceback.format_exc()} - yield {"output": f"Retrying... ({retry_count}/{max_retries})"} + yield { + "output": f"Retrying... ({retry_count}/{max_retries})" + } yield {"output": "Restarting process."} self.start_process() retry_count += 1 if retry_count > max_retries: - yield {"output": "Maximum retries reached. Could not execute code."} + yield { + "output": + "Maximum retries reached. Could not execute code." + } return while True: @@ -134,7 +139,8 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): else: time.sleep(0.1) try: - output = self.output_queue.get(timeout=0.3) # Waits for 0.3 seconds + output = self.output_queue.get( + timeout=0.3) # Waits for 0.3 seconds yield output except queue.Empty: if self.done.is_set(): diff --git a/swarms/utils/decorators.py b/swarms/utils/decorators.py index 8a5a5d56..2f22528b 100644 --- a/swarms/utils/decorators.py +++ b/swarms/utils/decorators.py @@ -6,6 +6,7 @@ import warnings def log_decorator(func): + def wrapper(*args, **kwargs): logging.info(f"Entering {func.__name__}") result = func(*args, **kwargs) @@ -16,6 +17,7 @@ def log_decorator(func): def error_decorator(func): + def wrapper(*args, **kwargs): try: return func(*args, **kwargs) @@ -27,18 +29,22 @@ def error_decorator(func): def timing_decorator(func): + def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() - logging.info(f"{func.__name__} executed in {end_time - start_time} seconds") + logging.info( + f"{func.__name__} executed in {end_time - start_time} seconds") return result return wrapper def retry_decorator(max_retries=5): + def decorator(func): + @functools.wraps(func) def wrapper(*args, **kwargs): for _ in range(max_retries): @@ -77,16 +83,20 @@ def synchronized_decorator(func): def deprecated_decorator(func): + @functools.wraps(func) def wrapper(*args, **kwargs): - warnings.warn(f"{func.__name__} is deprecated", category=DeprecationWarning) + warnings.warn(f"{func.__name__} is deprecated", + category=DeprecationWarning) return func(*args, **kwargs) return wrapper def validate_inputs_decorator(validator): + def decorator(func): + @functools.wraps(func) def wrapper(*args, **kwargs): if not validator(*args, **kwargs): diff --git a/swarms/utils/futures.py b/swarms/utils/futures.py index 55a4e5d5..5c2dfdcd 100644 --- a/swarms/utils/futures.py +++ b/swarms/utils/futures.py @@ -5,6 +5,8 @@ T = TypeVar("T") def execute_futures_dict(fs_dict: dict[str, futures.Future[T]]) -> dict[str, T]: - futures.wait(fs_dict.values(), timeout=None, return_when=futures.ALL_COMPLETED) + futures.wait(fs_dict.values(), + timeout=None, + return_when=futures.ALL_COMPLETED) return {key: future.result() for key, future in fs_dict.items()} diff --git a/swarms/utils/hash.py b/swarms/utils/hash.py index 725cc6ba..458fc147 100644 --- a/swarms/utils/hash.py +++ b/swarms/utils/hash.py @@ -4,8 +4,7 @@ import hashlib def dataframe_to_hash(dataframe: pd.DataFrame) -> str: return hashlib.sha256( - pd.util.hash_pandas_object(dataframe, index=True).values - ).hexdigest() + pd.util.hash_pandas_object(dataframe, index=True).values).hexdigest() def str_to_hash(text: str, hash_algorithm: str = "sha256") -> str: diff --git a/swarms/utils/main.py b/swarms/utils/main.py index 9c1342aa..9d5eefdf 100644 --- a/swarms/utils/main.py +++ b/swarms/utils/main.py @@ -51,16 +51,16 @@ def get_new_image_name(org_img_name, func_name="update"): if len(name_split) == 1: most_org_file_name = name_split[0] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.png".format( - this_new_uuid, func_name, recent_prev_file_name, most_org_file_name - ) + new_file_name = "{}_{}_{}_{}.png".format(this_new_uuid, func_name, + recent_prev_file_name, + most_org_file_name) else: assert len(name_split) == 4 most_org_file_name = name_split[3] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.png".format( - this_new_uuid, func_name, recent_prev_file_name, most_org_file_name - ) + new_file_name = "{}_{}_{}_{}.png".format(this_new_uuid, func_name, + recent_prev_file_name, + most_org_file_name) return os.path.join(head, new_file_name) @@ -73,26 +73,26 @@ def get_new_dataframe_name(org_img_name, func_name="update"): if len(name_split) == 1: most_org_file_name = name_split[0] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.csv".format( - this_new_uuid, func_name, recent_prev_file_name, most_org_file_name - ) + new_file_name = "{}_{}_{}_{}.csv".format(this_new_uuid, func_name, + recent_prev_file_name, + most_org_file_name) else: assert len(name_split) == 4 most_org_file_name = name_split[3] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.csv".format( - this_new_uuid, func_name, recent_prev_file_name, most_org_file_name - ) + new_file_name = "{}_{}_{}_{}.csv".format(this_new_uuid, func_name, + recent_prev_file_name, + most_org_file_name) return os.path.join(head, new_file_name) # =======================> utils end - # =======================> ANSI BEGINNING class Code: + def __init__(self, value: int): self.value = value @@ -101,6 +101,7 @@ class Code: class Color(Code): + def bg(self) -> "Color": self.value += 10 return self @@ -147,6 +148,7 @@ class Color(Code): class Style(Code): + @staticmethod def reset() -> "Style": return Style(0) @@ -203,19 +205,19 @@ def dim_multiline(message: str) -> str: lines = message.split("\n") if len(lines) <= 1: return lines[0] - return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to(Color.black().bright()) + return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to( + Color.black().bright()) # +=============================> ANSI Ending - # ================================> upload base - STATIC_DIR = "static" class AbstractUploader(ABC): + @abstractmethod def upload(self, filepath: str) -> str: pass @@ -227,12 +229,13 @@ class AbstractUploader(ABC): # ================================> upload end - # ========================= upload s3 class S3Uploader(AbstractUploader): - def __init__(self, accessKey: str, secretKey: str, region: str, bucket: str): + + def __init__(self, accessKey: str, secretKey: str, region: str, + bucket: str): self.accessKey = accessKey self.secretKey = secretKey self.region = region @@ -263,11 +266,11 @@ class S3Uploader(AbstractUploader): # ========================= upload s3 - # ========================> upload/static class StaticUploader(AbstractUploader): + def __init__(self, server: str, path: Path, endpoint: str): self.server = server self.path = path @@ -292,7 +295,6 @@ class StaticUploader(AbstractUploader): # ========================> handlers/base - # from env import settings @@ -336,16 +338,19 @@ class FileType(Enum): class BaseHandler: + def handle(self, filename: str) -> str: raise NotImplementedError class FileHandler: + def __init__(self, handlers: Dict[FileType, BaseHandler], path: Path): self.handlers = handlers self.path = path - def register(self, filetype: FileType, handler: BaseHandler) -> "FileHandler": + def register(self, filetype: FileType, + handler: BaseHandler) -> "FileHandler": self.handlers[filetype] = handler return self @@ -353,8 +358,8 @@ class FileHandler: filetype = FileType.from_url(url) data = requests.get(url).content local_filename = os.path.join( - "file", str(uuid.uuid4())[0:8] + filetype.to_extension() - ) + "file", + str(uuid.uuid4())[0:8] + filetype.to_extension()) os.makedirs(os.path.dirname(local_filename), exist_ok=True) with open(local_filename, "wb") as f: size = f.write(data) @@ -363,17 +368,15 @@ class FileHandler: def handle(self, url: str) -> str: try: - if url.startswith(os.environ.get("SERVER", "http://localhost:8000")): + if url.startswith(os.environ.get("SERVER", + "http://localhost:8000")): local_filepath = url[ - len(os.environ.get("SERVER", "http://localhost:8000")) + 1 : - ] + len(os.environ.get("SERVER", "http://localhost:8000")) + 1:] local_filename = Path("file") / local_filepath.split("/")[-1] src = self.path / local_filepath - dst = ( - self.path - / os.environ.get("PLAYGROUND_DIR", "./playground") - / local_filename - ) + dst = (self.path / + os.environ.get("PLAYGROUND_DIR", "./playground") / + local_filename) os.makedirs(os.path.dirname(dst), exist_ok=True) shutil.copy(src, dst) else: @@ -383,8 +386,7 @@ class FileHandler: if FileType.from_url(url) == FileType.IMAGE: raise Exception( f"No handler for {FileType.from_url(url)}. " - "Please set USE_GPU to True in env/settings.py" - ) + "Please set USE_GPU to True in env/settings.py") else: raise Exception(f"No handler for {FileType.from_url(url)}") return handler.handle(local_filename) @@ -394,22 +396,21 @@ class FileHandler: # => base end - # ===========================> class CsvToDataframe(BaseHandler): + def handle(self, filename: str): df = pd.read_csv(filename) description = ( f"Dataframe with {len(df)} rows and {len(df.columns)} columns. " "Columns are: " - f"{', '.join(df.columns)}" - ) + f"{', '.join(df.columns)}") print( f"\nProcessed CsvToDataframe, Input CSV: {filename}, Output Description:" - f" {description}" - ) + f" {description}") - return DATAFRAME_PROMPT.format(filename=filename, description=description) + return DATAFRAME_PROMPT.format(filename=filename, + description=description) diff --git a/swarms/utils/parse_code.py b/swarms/utils/parse_code.py index a2f346ea..020c9bef 100644 --- a/swarms/utils/parse_code.py +++ b/swarms/utils/parse_code.py @@ -7,5 +7,6 @@ def extract_code_in_backticks_in_string(message: str) -> str: """ pattern = r"`` ``(.*?)`` " # Non-greedy match between six backticks - match = re.search(pattern, message, re.DOTALL) # re.DOTALL to match newline chars + match = re.search(pattern, message, + re.DOTALL) # re.DOTALL to match newline chars return match.group(1).strip() if match else None diff --git a/swarms/utils/revutils.py b/swarms/utils/revutils.py index 7868ae44..9db1e123 100644 --- a/swarms/utils/revutils.py +++ b/swarms/utils/revutils.py @@ -49,16 +49,12 @@ def get_input( """ Multiline input function. """ - return ( - session.prompt( - completer=completer, - multiline=True, - auto_suggest=AutoSuggestFromHistory(), - key_bindings=key_bindings, - ) - if session - else prompt(multiline=True) - ) + return (session.prompt( + completer=completer, + multiline=True, + auto_suggest=AutoSuggestFromHistory(), + key_bindings=key_bindings, + ) if session else prompt(multiline=True)) async def get_input_async( @@ -68,15 +64,11 @@ async def get_input_async( """ Multiline input function. """ - return ( - await session.prompt_async( - completer=completer, - multiline=True, - auto_suggest=AutoSuggestFromHistory(), - ) - if session - else prompt(multiline=True) - ) + return (await session.prompt_async( + completer=completer, + multiline=True, + auto_suggest=AutoSuggestFromHistory(), + ) if session else prompt(multiline=True)) def get_filtered_keys_from_object(obj: object, *keys: str) -> any: @@ -94,9 +86,7 @@ def get_filtered_keys_from_object(obj: object, *keys: str) -> any: return {key for key in class_keys if key not in keys[1:]} # Check if all passed keys are valid if invalid_keys := set(keys) - class_keys: - raise ValueError( - f"Invalid keys: {invalid_keys}", - ) + raise ValueError(f"Invalid keys: {invalid_keys}",) # Only return specified keys that are in class_keys return {key for key in keys if key in class_keys} @@ -124,8 +114,8 @@ def random_int(min: int, max: int) -> int: if __name__ == "__main__": logging.basicConfig( - format="%(asctime)s - %(name)s - %(levelname)s - %(funcName)s - %(message)s", - ) + format= + "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s - %(message)s",) log = logging.getLogger(__name__) diff --git a/swarms/utils/serializable.py b/swarms/utils/serializable.py index 8f0e5ccf..47cc815f 100644 --- a/swarms/utils/serializable.py +++ b/swarms/utils/serializable.py @@ -106,21 +106,22 @@ class Serializable(BaseModel, ABC): lc_kwargs.update({key: secret_value}) return { - "lc": 1, - "type": "constructor", + "lc": + 1, + "type": + "constructor", "id": [*self.lc_namespace, self.__class__.__name__], - "kwargs": lc_kwargs - if not secrets - else _replace_secrets(lc_kwargs, secrets), + "kwargs": + lc_kwargs if not secrets else _replace_secrets( + lc_kwargs, secrets), } def to_json_not_implemented(self) -> SerializedNotImplemented: return to_json_not_implemented(self) -def _replace_secrets( - root: Dict[Any, Any], secrets_map: Dict[str, str] -) -> Dict[Any, Any]: +def _replace_secrets(root: Dict[Any, Any], + secrets_map: Dict[str, str]) -> Dict[Any, Any]: result = root.copy() for path, secret_id in secrets_map.items(): [*parts, last] = path.split(".") diff --git a/swarms/utils/static.py b/swarms/utils/static.py index 3b8a276d..23f13996 100644 --- a/swarms/utils/static.py +++ b/swarms/utils/static.py @@ -8,6 +8,7 @@ from swarms.utils.main import AbstractUploader class StaticUploader(AbstractUploader): + def __init__(self, server: str, path: Path, endpoint: str): self.server = server self.path = path diff --git a/swarms/workers/worker.py b/swarms/workers/worker.py index 9986666a..bef9682a 100644 --- a/swarms/workers/worker.py +++ b/swarms/workers/worker.py @@ -4,8 +4,7 @@ from typing import Dict, Union import faiss from langchain.chains.qa_with_sources.loading import ( - load_qa_with_sources_chain, -) + load_qa_with_sources_chain,) from langchain.docstore import InMemoryDocstore from langchain.embeddings import OpenAIEmbeddings from langchain.tools import ReadFileTool, WriteFileTool @@ -132,8 +131,7 @@ class Worker: ``` """ query_website_tool = WebpageQATool( - qa_chain=load_qa_with_sources_chain(self.llm) - ) + qa_chain=load_qa_with_sources_chain(self.llm)) self.tools = [ WriteFileTool(root_dir=ROOT_DIR), @@ -157,15 +155,13 @@ class Worker: embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) - self.vectorstore = FAISS( - embeddings_model.embed_query, index, InMemoryDocstore({}), {} - ) + self.vectorstore = FAISS(embeddings_model.embed_query, index, + InMemoryDocstore({}), {}) except Exception as error: raise RuntimeError( "Error setting up memory perhaps try try tuning the embedding size:" - f" {error}" - ) + f" {error}") def setup_agent(self): """ @@ -294,8 +290,6 @@ class Worker: def is_within_proximity(self, other_worker): """Using Euclidean distance for proximity check""" - distance = ( - (self.coordinates[0] - other_worker.coordinates[0]) ** 2 - + (self.coordinates[1] - other_worker.coordinates[1]) ** 2 - ) ** 0.5 + distance = ((self.coordinates[0] - other_worker.coordinates[0])**2 + + (self.coordinates[1] - other_worker.coordinates[1])**2)**0.5 return distance < 10 # threshold for proximity From 4af1cb42fe3ba2c49bd1ed6d239e1b77528271fe Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 8 Nov 2023 17:52:26 -0500 Subject: [PATCH 60/63] clean up Former-commit-id: fe19f21bceef17429294fe2f14b5e19fca2c2cf9 --- CONTRIBUTING.md | 29 +++++++++++++++++++ quality.sh => code_quality.sh | 0 .../multi_modal_auto_agent.py | 0 example.py | 2 +- pyproject.toml | 2 +- swarms/models/__init__.py | 4 +-- 6 files changed, 33 insertions(+), 4 deletions(-) rename quality.sh => code_quality.sh (100%) rename multi_modal_auto_agent.py => demos/multi_modal_auto_agent.py (100%) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index bd9090de..be04abaa 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -100,6 +100,35 @@ You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/) - Run all the tests in the tests folder `find ./tests -name '*.py' -exec pytest {} \;` +## Code Quality +`quality.sh` runs 4 different code formatters for ultra reliable code cleanup using Autopep8, Black, Ruff, YAPF +1. Open your terminal. + +2. Change directory to where `quality.sh` is located using `cd` command: + ```sh + cd /path/to/directory + ``` + +3. Make sure the script has execute permissions: + ```sh + chmod +x quality.sh + ``` + +4. Run the script: + ```sh + ./quality.sh + ``` + +If the script requires administrative privileges, you might need to run it with `sudo`: +```sh +sudo ./quality.sh +``` + +Please replace `/path/to/directory` with the actual path where the `quality.sh` script is located on your system. + +If you're asking for a specific content or functionality inside `quality.sh` related to YAPF or other code quality tools, you would need to edit the `quality.sh` script to include the desired commands, such as running YAPF on a directory. The contents of `quality.sh` would dictate exactly what happens when you run it. + + ## ๐Ÿ“„ license By contributing, you agree that your contributions will be licensed under an [MIT license](https://github.com/kyegomez/swarms/blob/develop/LICENSE.md). \ No newline at end of file diff --git a/quality.sh b/code_quality.sh similarity index 100% rename from quality.sh rename to code_quality.sh diff --git a/multi_modal_auto_agent.py b/demos/multi_modal_auto_agent.py similarity index 100% rename from multi_modal_auto_agent.py rename to demos/multi_modal_auto_agent.py diff --git a/example.py b/example.py index 6c27bceb..c84448a8 100644 --- a/example.py +++ b/example.py @@ -1,7 +1,7 @@ from swarms.models import OpenAIChat from swarms.structs import Flow -api_key = "" +api_key = "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" # Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( diff --git a/pyproject.toml b/pyproject.toml index 62c0ec13..bad710f4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.1.3" +version = "2.1.4" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 6f6ea8ba..c0e55db5 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -19,7 +19,7 @@ from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA # from swarms.models.gpt4v import GPT4Vision # from swarms.models.dalle3 import Dalle3 # from swarms.models.distilled_whisperx import DistilWhisperModel -from swarms.models.fuyu import Fuyu # Not working, wait until they update +# from swarms.models.fuyu import Fuyu # Not working, wait until they update import sys @@ -45,5 +45,5 @@ __all__ = [ "WizardLLMStoryTeller", # "GPT4Vision", # "Dalle3", - "Fuyu", + # "Fuyu", ] From cc96496754abb42107470111db6c9bff246c4e55 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 9 Nov 2023 12:04:13 -0500 Subject: [PATCH 61/63] account swarm + layout document fix Former-commit-id: 1dc1e8f270b2317749c4833eabf3df68e167dd6f --- .gitignore | 1 + CONTRIBUTING.md | 2 +- demos/accountant_team/accountant_team.py | 55 +++++++++--------------- example.py | 2 +- pyproject.toml | 2 +- sequential_workflow_example.py | 2 +- swarms/__init__.py | 12 +++--- swarms/models/layoutlm_document_qa.py | 4 +- swarms/models/nougat.py | 17 +++++++- 9 files changed, 47 insertions(+), 50 deletions(-) diff --git a/.gitignore b/.gitignore index a336e116..767abb9d 100644 --- a/.gitignore +++ b/.gitignore @@ -28,6 +28,7 @@ error.txt # C extensions *.so +.ruff_cache errors.txt diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index be04abaa..04f0f593 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -111,7 +111,7 @@ You can learn more about mkdocs on the [mkdocs website](https://www.mkdocs.org/) 3. Make sure the script has execute permissions: ```sh - chmod +x quality.sh + chmod +x code_quality.sh ``` 4. Run the script: diff --git a/demos/accountant_team/accountant_team.py b/demos/accountant_team/accountant_team.py index 06f89684..7eadec96 100644 --- a/demos/accountant_team/accountant_team.py +++ b/demos/accountant_team/accountant_team.py @@ -1,50 +1,35 @@ -# !pip install --upgrade swarms==2.0.6 - -from swarms.models import BioGPT +import re from swarms.models.nougat import Nougat from swarms.structs import Flow +from swarms.models import OpenAIChat +from swarms.models import LayoutLMDocumentQA # # URL of the image of the financial document IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg" # Example usage -api_key = "" # Your actual API key here - -# Initialize the OCR model - +api_key = "" # Initialize the language flow -llm = BioGPT() - - -# Create a prompt for the language model -def summary_agent_prompt(analyzed_doc: str): - model = Nougat( - max_new_tokens=5000, - ) - - out = model(analyzed_doc) - - return f""" - Generate an actionable summary of this financial document, provide bulletpoints: +llm = OpenAIChat( + openai_api_key=api_key, +) - Here is the Analyzed Document: - --- - {out} - """ +# LayoutLM Document QA +pdf_analyzer = LayoutLMDocumentQA() +question = "What is the total amount of expenses?" +answer = pdf_analyzer( + question, + IMAGE_OF_FINANCIAL_DOC_URL, +) # Initialize the Flow with the language flow -flow1 = Flow(llm=llm, max_loops=1, dashboard=False) - -# Create another Flow for a different task -flow2 = Flow(llm=llm, max_loops=1, dashboard=False) - +agent = Flow(llm=llm) +SUMMARY_AGENT_PROMPT = f""" +Generate an actionable summary of this financial document be very specific and precise, provide bulletpoints be very specific provide methods of lowering expenses: {answer}" +""" # Add tasks to the workflow -summary_agent = flow1.run(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL)) - -# Suppose the next task takes the output of the first task as input -out = flow2.run( - f"Provide an actionable step by step plan on how to cut costs from the analyzed financial document. {summary_agent}" -) +summary_agent = agent.run(SUMMARY_AGENT_PROMPT) +print(summary_agent) diff --git a/example.py b/example.py index c84448a8..6c27bceb 100644 --- a/example.py +++ b/example.py @@ -1,7 +1,7 @@ from swarms.models import OpenAIChat from swarms.structs import Flow -api_key = "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" +api_key = "" # Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC llm = OpenAIChat( diff --git a/pyproject.toml b/pyproject.toml index bad710f4..4ea6bffb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ classifiers = [ [tool.poetry.dependencies] python = "^3.8.1" transformers = "*" -openai = "*" +openai = "0.28.1" langchain = "*" asyncio = "*" nest_asyncio = "*" diff --git a/sequential_workflow_example.py b/sequential_workflow_example.py index feb6c748..51a48df2 100644 --- a/sequential_workflow_example.py +++ b/sequential_workflow_example.py @@ -3,7 +3,7 @@ from swarms.structs import Flow from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = "" # Your actual API key here +api_key = "" # Initialize the language flow llm = OpenAIChat( diff --git a/swarms/__init__.py b/swarms/__init__.py index 8f0dfc26..5de7829b 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -1,9 +1,3 @@ -from swarms.agents import * -from swarms.swarms import * -from swarms.structs import * -from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py -from swarms.chunkers import * -from swarms.workers import * import os import warnings @@ -12,3 +6,9 @@ warnings.filterwarnings("ignore", category=UserWarning) # disable tensorflow warnings os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" +from swarms.agents import * +from swarms.swarms import * +from swarms.structs import * +from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py +from swarms.chunkers import * +from swarms.workers import * diff --git a/swarms/models/layoutlm_document_qa.py b/swarms/models/layoutlm_document_qa.py index 6fe83210..26734a25 100644 --- a/swarms/models/layoutlm_document_qa.py +++ b/swarms/models/layoutlm_document_qa.py @@ -3,10 +3,8 @@ LayoutLMDocumentQA is a multimodal good for visual question answering on real world docs lik invoice, pdfs, etc """ from transformers import pipeline -from swarms.models.base import AbstractModel - -class LayoutLMDocumentQA(AbstractModel): +class LayoutLMDocumentQA: """ LayoutLMDocumentQA for document question answering: diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index a362f94f..9dee7d1b 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -8,7 +8,7 @@ format - Extracting metadata from pdfs """ - +import re import torch from PIL import Image from transformers import NougatProcessor, VisionEncoderDecoderModel @@ -70,5 +70,18 @@ class Nougat: sequence = self.processor.post_process_generation(sequence, fix_markdown=False) - out = print(repr(sequence)) + out = print(sequence) return out + + def clean_nougat_output(raw_output): + # Define the pattern to extract the relevant data + daily_balance_pattern = r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*" + + # Find all matches of the pattern + matches = re.findall(daily_balance_pattern, raw_output) + + # Convert the matches to a readable format + cleaned_data = ["Date: {}, Amount: {}".format(date, amount.replace(',', '')) for date, amount in matches] + + # Join the cleaned data with new lines for readability + return '\n'.join(cleaned_data) From 3e6a3f7139e8f985aceed91fa3892e37832013f6 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 9 Nov 2023 15:52:29 -0500 Subject: [PATCH 62/63] dynamic max loops, + gpt4clean up Former-commit-id: 371da7944e020bb711c8065848f347efbbbd3744 --- README.md | 15 +- code_quality.sh | 0 demos/ui_software_demo.py | 5 + playground/models/gpt4_v.py | 15 ++ pyproject.toml | 6 +- requirements.txt | 1 + sequential_workflow_example.py | 2 +- swarms/__init__.py | 2 +- swarms/memory/weaviate.py | 4 + swarms/models/fuyu.py | 1 - swarms/models/gpt4v.py | 325 ++++++++++++-------------- swarms/models/layoutlm_document_qa.py | 5 +- swarms/models/nougat.py | 16 +- swarms/models/openai_models.py | 4 +- swarms/structs/flow.py | 142 ++++++----- tests/models/mpt7b.py | 1 + 16 files changed, 287 insertions(+), 257 deletions(-) mode change 100644 => 100755 code_quality.sh create mode 100644 demos/ui_software_demo.py create mode 100644 playground/models/gpt4_v.py create mode 100644 swarms/memory/weaviate.py diff --git a/README.md b/README.md index 289a4c22..abc6ab69 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,9 @@ Book a [1-on-1 Session with Kye](https://calendly.com/swarm-corp/30min), the Cre We have a small gallery of examples to run here, [for more check out the docs to build your own agent and or swarms!](https://docs.apac.ai) ### `Flow` Example -- The `Flow` is a superior iteratioin of the `LLMChain` from Langchain, our intent with `Flow` is to create the most reliable loop structure that gives the agents their "autonomy" through 3 main methods of interaction, one through user specified loops, then dynamic where the agent parses a token, and or an interactive human input verison, or a mix of all 3. +- Reliable Structure that provides LLMS autonomy +- Extremely Customizeable with stopping conditions, interactivity, dynamical temperature, loop intervals, and so much more +- Enterprise Grade + Production Grade: `Flow` is designed and optimized for automating real-world tasks at scale! ```python @@ -86,9 +88,10 @@ out = flow.run("Generate a 10,000 word blog on health and wellness.") ------ ### `SequentialWorkflow` -- Execute tasks step by step by passing in an LLM and the task description! -- Pass in flows with various LLMs -- Save and restore Workflow states! +- A Sequential swarm of autonomous agents where each agent's outputs are fed into the next agent +- Save and Restore Workflow states! +- Integrate Flow's with various LLMs and Multi-Modality Models + ```python from swarms.models import OpenAIChat from swarms.structs import Flow @@ -130,7 +133,6 @@ for task in workflow.tasks: ``` - --- ## Documentation @@ -140,6 +142,9 @@ for task in workflow.tasks: ## Contribute - We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) +## Community +- [Join the Swarms community here on Discord!](https://discord.gg/AJazBmhKnr) + # License MIT diff --git a/code_quality.sh b/code_quality.sh old mode 100644 new mode 100755 diff --git a/demos/ui_software_demo.py b/demos/ui_software_demo.py new file mode 100644 index 00000000..6271d96e --- /dev/null +++ b/demos/ui_software_demo.py @@ -0,0 +1,5 @@ +""" +Autonomous swarm that optimizes UI autonomously + +GPT4Vision ->> GPT4 ->> UI +""" \ No newline at end of file diff --git a/playground/models/gpt4_v.py b/playground/models/gpt4_v.py new file mode 100644 index 00000000..5e5d7c95 --- /dev/null +++ b/playground/models/gpt4_v.py @@ -0,0 +1,15 @@ +from swarms.models.gpt4v import GPT4Vision + +api_key = "" + +gpt4vision = GPT4Vision( + openai_api_key=api_key, +) + +img = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/VFPt_Solenoid_correct2.svg/640px-VFPt_Solenoid_correct2.svg.png" + +task = "What is this image" + +answer = gpt4vision.run(task, img) + +print(answer) diff --git a/pyproject.toml b/pyproject.toml index 4ea6bffb..c44cf9dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "2.1.4" +version = "2.1.6" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] @@ -24,7 +24,7 @@ classifiers = [ [tool.poetry.dependencies] python = "^3.8.1" transformers = "*" -openai = "0.28.1" +openai = "*" langchain = "*" asyncio = "*" nest_asyncio = "*" @@ -45,6 +45,8 @@ httpx = "*" tiktoken = "*" attrs = "*" ggl = "*" +ratelimit = "*" + beautifulsoup4 = "*" huggingface-hub = "*" pydantic = "*" diff --git a/requirements.txt b/requirements.txt index f1a5c689..5cb854b9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -36,6 +36,7 @@ tabulate colored griptape addict +ratelimit albumentations basicsr termcolor diff --git a/sequential_workflow_example.py b/sequential_workflow_example.py index 51a48df2..9dc9c828 100644 --- a/sequential_workflow_example.py +++ b/sequential_workflow_example.py @@ -3,7 +3,7 @@ from swarms.structs import Flow from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = "" +api_key = "" # Initialize the language flow llm = OpenAIChat( diff --git a/swarms/__init__.py b/swarms/__init__.py index 5de7829b..71481e16 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -9,6 +9,6 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" from swarms.agents import * from swarms.swarms import * from swarms.structs import * -from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py +from swarms.models import * from swarms.chunkers import * from swarms.workers import * diff --git a/swarms/memory/weaviate.py b/swarms/memory/weaviate.py new file mode 100644 index 00000000..a482f71b --- /dev/null +++ b/swarms/memory/weaviate.py @@ -0,0 +1,4 @@ +""" +Weaviate API Client + +""" diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index d7148d0e..63108835 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -29,7 +29,6 @@ class Fuyu: >>> fuyu = Fuyu() >>> fuyu("Hello, my name is", "path/to/image.png") - """ def __init__( diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py index 87393fab..251744e8 100644 --- a/swarms/models/gpt4v.py +++ b/swarms/models/gpt4v.py @@ -1,30 +1,22 @@ +import asyncio import base64 -import logging -import os -import time +import concurrent.futures +import re from dataclasses import dataclass -from typing import List, Optional, Union +from typing import List, Optional, Tuple +import openai import requests +from cachetools import TTLCache from dotenv import load_dotenv from openai import OpenAI +from ratelimit import limits, sleep_and_retry from termcolor import colored # ENV load_dotenv() -def logging_config(): - """Configures logging""" - logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - ) - logger = logging.getLogger(__name__) - - return logger - - @dataclass class GPT4VisionResponse: """A response structure for GPT-4""" @@ -56,7 +48,7 @@ class GPT4Vision: -------- process_img(self, img_path: str) -> str: Processes the image to be used for the API request - __call__(self, img: Union[str, List[str]], tasks: List[str]) -> GPT4VisionResponse: + run(self, img: Union[str, List[str]], tasks: List[str]) -> GPT4VisionResponse: Makes a call to the GPT-4 Vision API and returns the image url Example: @@ -66,23 +58,24 @@ class GPT4Vision: >>> answer = gpt4vision(img, tasks) >>> print(answer) - """ max_retries: int = 3 model: str = "gpt-4-vision-preview" backoff_factor: float = 2.0 timeout_seconds: int = 10 - api_key: Optional[str] = None + openai_api_key: Optional[str] = None # 'Low' or 'High' for respesctively fast or high quality, but high more token usage quality: str = "low" # Max tokens to use for the API request, the maximum might be 3,000 but we don't know max_tokens: int = 200 - client = OpenAI( - api_key=api_key, - max_retries=max_retries, - ) - logger = logging_config() + client = OpenAI(api_key=openai_api_key,) + dashboard: bool = True + call_limit: int = 1 + period_seconds: int = 60 + + # Cache for storing API Responses + cache = TTLCache(maxsize=100, ttl=600) # Cache for 10 minutes class Config: """Config class for the GPT4Vision model""" @@ -94,204 +87,172 @@ class GPT4Vision: with open(img, "rb") as image_file: return base64.b64encode(image_file.read()).decode("utf-8") - def __call__( - self, - img: Union[str, List[str]], - tasks: List[str], - ) -> GPT4VisionResponse: - """ - Calls the GPT-4 Vision API and returns the image url - - Parameters: - ----------- - img: Union[str, List[str]] - The image to be used for the API request - tasks: List[str] - The tasks to be used for the API request - - Returns: - -------- - answer: GPT4VisionResponse - The response from the API request - - Example: - -------- - >>> gpt4vision = GPT4Vision() - >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - >>> tasks = ["A painting of a dog"] - >>> answer = gpt4vision(img, tasks) - >>> print(answer) - - - """ - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {self.api_key}", - } - - # Image content - image_content = [{ - "type": "imavge_url", - "image_url": img - } if img.startswith("http") else { - "type": "image", - "data": img - } for img in img] - - messages = [{ - "role": - "user", - "content": - image_content + [{ - "type": "text", - "text": q - } for q in tasks], - }] - - payload = { - "model": "gpt-4-vision-preview", - "messages": messages, - "max_tokens": self.max_tokens, - "detail": self.quality, - } - - for attempt in range(self.max_retries): - try: - response = requests.post( - "https://api.openai.com/v1/chat/completions", - headers=headers, - json=payload, - timeout=self.timeout_seconds, - ) - response.raise_for_status() - answer = response.json( - )["choices"][0]["message"]["content"]["text"] - return GPT4VisionResponse(answer=answer) - except requests.exceptions.HTTPError as error: - self.logger.error( - f"HTTP error: {error.response.status_code}, {error.response.text}" - ) - if error.response.status_code in [429, 500, 503]: - # Exponential backoff = 429(too many requesys) - # And 503 = (Service unavailable) errors - time.sleep(self.backoff_factor**attempt) - else: - break - - except requests.exceptions.RequestException as error: - self.logger.error(f"Request error: {error}") - time.sleep(self.backoff_factor**attempt) - except Exception as error: - self.logger.error( - f"Unexpected Error: {error} try optimizing your api key and try" - " again") - raise error from None - - raise TimeoutError("API Request timed out after multiple retries") - - def run(self, task: str, img: str) -> str: + @sleep_and_retry + @limits(calls=call_limit, + period=period_seconds) # Rate limit of 10 calls per minute + def run(self, task: str, img: str): """ - Runs the GPT-4 Vision API + Run the GPT-4 Vision model - Parameters: - ----------- - task: str - The task to be used for the API request - img: str - The image to be used for the API request + Task: str + The task to run + Img: str + The image to run the task on - Returns: - -------- - out: str - The response from the API request - - Example: - -------- - >>> gpt4vision = GPT4Vision() - >>> task = "A painting of a dog" - >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - >>> answer = gpt4vision.run(task, img) - >>> print(answer) """ + if self.dashboard: + self.print_dashboard() try: response = self.client.chat.completions.create( - model=self.model, + model="gpt-4-vision-preview", messages=[{ "role": "user", "content": [ { "type": "text", - "text": f"{task}" + "text": task }, { "type": "image_url", - "image_url": f"{img}", + "image_url": { + "url": str(img), + }, }, ], }], max_tokens=self.max_tokens, ) - out = response.choices[0].text + out = print(response.choices[0]) + # out = self.clean_output(out) return out - except Exception as error: - print( - colored( - (f"Error when calling GPT4Vision, Error: {error} Try optimizing" - " your key, and try again"), - "red", - )) - - async def arun(self, task: str, img: str) -> str: + except openai.OpenAIError as e: + # logger.error(f"OpenAI API error: {e}") + return f"OpenAI API error: Could not process the image. {e}" + except Exception as e: + return f"Unexpected error occurred while processing the image. {e}" + + def clean_output(self, output: str): + # Regex pattern to find the Choice object representation in the output + pattern = r"Choice\(.*?\(content=\"(.*?)\".*?\)\)" + match = re.search(pattern, output, re.DOTALL) + + if match: + # Extract the content from the matched pattern + content = match.group(1) + # Replace escaped quotes to get the clean content + content = content.replace(r"\"", '"') + print(content) + else: + print("No content found in the output.") + + async def arun(self, task: str, img: str): """ - Asynchronous run method for GPT-4 Vision + Arun is an async version of run - Parameters: - ----------- - task: str - The task to be used for the API request - img: str - The image to be used for the API request + Task: str + The task to run + Img: str + The image to run the task on - Returns: - -------- - out: str - The response from the API request - - Example: - -------- - >>> gpt4vision = GPT4Vision() - >>> task = "A painting of a dog" - >>> img = "https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png" - >>> answer = await gpt4vision.arun(task, img) - >>> print(answer) """ try: response = await self.client.chat.completions.create( - model=self.model, + model="gpt-4-vision-preview", messages=[{ "role": "user", "content": [ { "type": "text", - "text": f"{task}" + "text": task }, { "type": "image_url", - "image_url": f"{img}", + "image_url": { + "url": img, + }, }, ], }], max_tokens=self.max_tokens, ) - out = response.choices[0].text - return out - except Exception as error: - print( - colored( - (f"Error when calling GPT4Vision, Error: {error} Try optimizing" - " your key, and try again"), - "red", - )) + + return print(response.choices[0]) + except openai.OpenAIError as e: + # logger.error(f"OpenAI API error: {e}") + return f"OpenAI API error: Could not process the image. {e}" + except Exception as e: + return f"Unexpected error occurred while processing the image. {e}" + + def run_batch(self, tasks_images: List[Tuple[str, str]]) -> List[str]: + """Process a batch of tasks and images""" + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [ + executor.submit(self.run, task, img) + for task, img in tasks_images + ] + results = [future.result() for future in futures] + return results + + async def run_batch_async(self, + tasks_images: List[Tuple[str, str]]) -> List[str]: + """Process a batch of tasks and images asynchronously""" + loop = asyncio.get_event_loop() + futures = [ + loop.run_in_executor(None, self.run, task, img) + for task, img in tasks_images + ] + return await asyncio.gather(*futures) + + async def run_batch_async_with_retries( + self, tasks_images: List[Tuple[str, str]]) -> List[str]: + """Process a batch of tasks and images asynchronously with retries""" + loop = asyncio.get_event_loop() + futures = [ + loop.run_in_executor(None, self.run_with_retries, task, img) + for task, img in tasks_images + ] + return await asyncio.gather(*futures) + + def print_dashboard(self): + dashboard = print( + colored( + f""" + GPT4Vision Dashboard + ------------------- + Max Retries: {self.max_retries} + Model: {self.model} + Backoff Factor: {self.backoff_factor} + Timeout Seconds: {self.timeout_seconds} + Image Quality: {self.quality} + Max Tokens: {self.max_tokens} + + """, + "green", + )) + return dashboard + + def health_check(self): + """Health check for the GPT4Vision model""" + try: + response = requests.get("https://api.openai.com/v1/engines") + return response.status_code == 200 + except requests.RequestException as error: + print(f"Health check failed: {error}") + return False + + def sanitize_input(self, text: str) -> str: + """ + Sanitize input to prevent injection attacks. + + Parameters: + text: str - The input text to be sanitized. + + Returns: + The sanitized text. + """ + # Example of simple sanitization, this should be expanded based on the context and usage + sanitized_text = re.sub(r"[^\w\s]", "", text) + return sanitized_text diff --git a/swarms/models/layoutlm_document_qa.py b/swarms/models/layoutlm_document_qa.py index 26734a25..e2b8d1e4 100644 --- a/swarms/models/layoutlm_document_qa.py +++ b/swarms/models/layoutlm_document_qa.py @@ -4,6 +4,7 @@ visual question answering on real world docs lik invoice, pdfs, etc """ from transformers import pipeline + class LayoutLMDocumentQA: """ LayoutLMDocumentQA for document question answering: @@ -23,9 +24,9 @@ class LayoutLMDocumentQA: def __init__( self, model_name: str = "impira/layoutlm-document-qa", - task: str = "document-question-answering", + task_type: str = "document-question-answering", ): - self.pipeline = pipeline(self.task, model=self.model_name) + self.pipeline = pipeline(self.task_type, model=self.model_name) def __call__(self, task: str, img_path: str): """Call for model""" diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index 9dee7d1b..4de1d952 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -75,13 +75,17 @@ class Nougat: def clean_nougat_output(raw_output): # Define the pattern to extract the relevant data - daily_balance_pattern = r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*" - + daily_balance_pattern = ( + r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*") + # Find all matches of the pattern matches = re.findall(daily_balance_pattern, raw_output) - + # Convert the matches to a readable format - cleaned_data = ["Date: {}, Amount: {}".format(date, amount.replace(',', '')) for date, amount in matches] - + cleaned_data = [ + "Date: {}, Amount: {}".format(date, amount.replace(",", "")) + for date, amount in matches + ] + # Join the cleaned data with new lines for readability - return '\n'.join(cleaned_data) + return "\n".join(cleaned_data) diff --git a/swarms/models/openai_models.py b/swarms/models/openai_models.py index e1a327b5..128169a3 100644 --- a/swarms/models/openai_models.py +++ b/swarms/models/openai_models.py @@ -493,7 +493,7 @@ class BaseOpenAI(BaseLLM): openai.proxy = { "http": self.openai_proxy, - "https": self.openai_proxy + "https": self.openai_proxy, } # type: ignore[assignment] # noqa: E501 return {**openai_creds, **self._default_params} @@ -783,7 +783,7 @@ class OpenAIChat(BaseLLM): if openai_proxy: openai.proxy = { "http": openai_proxy, - "https": openai_proxy + "https": openai_proxy, } # type: ignore[assignment] # noqa: E501 except ImportError: raise ImportError("Could not import openai python package. " diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index a7a19258..a3633a2c 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -8,9 +8,9 @@ TODO: - add async processing for run and batch run - add plan module - concurrent -- +- Add batched inputs """ - +import asyncio import json import logging import time @@ -100,24 +100,26 @@ class Flow: self, llm: Any, # template: str, - max_loops: int = 5, + max_loops = 5, stopping_condition: Optional[Callable[[str], bool]] = None, loop_interval: int = 1, retry_attempts: int = 3, retry_interval: int = 1, + return_history: bool = False, + dynamic_loops: Optional[bool] = False, interactive: bool = False, dashboard: bool = False, - name: str = "Flow agent", + agent_name: str = "Flow agent", system_prompt: str = FLOW_SYSTEM_PROMPT, # tools: List[BaseTool] = None, dynamic_temperature: bool = False, saved_state_path: Optional[str] = "flow_state.json", autosave: bool = False, context_length: int = 8192, + user_name: str = "Human", **kwargs: Any, ): self.llm = llm - # self.template = template self.max_loops = max_loops self.stopping_condition = stopping_condition self.loop_interval = loop_interval @@ -130,9 +132,14 @@ class Flow: self.interactive = interactive self.dashboard = dashboard self.dynamic_temperature = dynamic_temperature + self.dynamic_loops = dynamic_loops + self.user_name = user_name + # The max_loops will be set dynamically if the dynamic_loop + if self.dynamic_loops: + self.max_loops = "auto" # self.tools = tools self.system_prompt = system_prompt - self.name = name + self.agent_name = agent_name self.saved_state_path = saved_state_path self.autosave = autosave self.response_filters = [] @@ -194,7 +201,7 @@ class Flow: def add_task_to_memory(self, task: str): """Add the task to the memory""" - self.memory.append([f"Human: {task}"]) + self.memory.append([f"{self.user_name}: {task}"]) def add_message_to_memory(self, message: str): """Add the message to the memory""" @@ -222,7 +229,7 @@ class Flow: ---------------------------------------- Flow Configuration: - Name: {self.name} + Name: {self.agent_name} System Prompt: {self.system_prompt} Task: {task} Max Loops: {self.max_loops} @@ -277,47 +284,40 @@ class Flow: 5. Repeat until stopping condition is met or max_loops is reached """ - # Restore from saved state if provided, ortherwise start with a new history - # if self.saved_state: - # self.load_state(self.saved_state) - # history = self.memory[-1] - # print(f"Loaded state from {self.saved_state}") - # else: - # history = [f"Human: {task}"] - # self.memory.append(history) - - # print(colored(">>> Autonomous Agent Activated", "cyan", attrs=["bold"])) + # Activate Autonomous agent message self.activate_autonomous_agent() - # if self.autosave: - response = task - history = [f"Human: {task}"] + history = [f"{self.user_name}: {task}"] # If dashboard = True then print the dashboard if self.dashboard: self.print_dashboard(task) - for i in range(self.max_loops): - print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) + loop_count = 0 + # for i in range(self.max_loops): + while self.max_loops == 'auto' or loop_count < self.max_loops: + loop_count += 1 + print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue")) print("\n") - if self._check_stopping_condition(response) or parse_done_token( - response): + + if self._check_stopping_condition(response) or parse_done_token(response): break # Adjust temperature, comment if no work if self.dynamic_temperature: self.dynamic_temperature() + # Preparing the prompt + task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) + attempt = 0 while attempt < self.retry_attempts: try: response = self.llm( - self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response), + task **kwargs, ) - # print(f"Next query: {response}") - # break if self.interactive: print(f"AI: {response}") history.append(f"AI: {response}") @@ -341,13 +341,14 @@ class Flow: print(colored(f"Autosaving flow state to {save_path}", "green")) self.save_state(save_path) - return response # , history + if self.return_history: + return response, history + + return response async def arun(self, task: str, **kwargs): - """Async run""" - pass """ - Run the autonomous agent loop + Run the autonomous agent loop aschnronously Args: task (str): The initial task to run @@ -360,44 +361,40 @@ class Flow: 5. Repeat until stopping condition is met or max_loops is reached """ - # Restore from saved state if provided, ortherwise start with a new history - # if self.saved_state: - # self.load_state(self.saved_state) - # history = self.memory[-1] - # print(f"Loaded state from {self.saved_state}") - # else: - # history = [f"Human: {task}"] - # self.memory.append(history) - - print(colored(">>> Autonomous Agent Activated", "cyan", attrs=["bold"])) + # Activate Autonomous agent message + self.activate_autonomous_agent() response = task - history = [f"Human: {task}"] + history = [f"{self.user_name}: {task}"] # If dashboard = True then print the dashboard if self.dashboard: self.print_dashboard(task) - for i in range(self.max_loops): - print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue")) + loop_count = 0 + # for i in range(self.max_loops): + while self.max_loops == 'auto' or loop_count < self.max_loops: + loop_count += 1 + print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue")) print("\n") - if self._check_stopping_condition(response) or parse_done_token( - response): + + if self._check_stopping_condition(response) or parse_done_token(response): break # Adjust temperature, comment if no work if self.dynamic_temperature: self.dynamic_temperature() + # Preparing the prompt + task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) + attempt = 0 while attempt < self.retry_attempts: try: response = self.llm( - self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response), + task **kwargs, ) - # print(f"Next query: {response}") - # break if self.interactive: print(f"AI: {response}") history.append(f"AI: {response}") @@ -416,10 +413,15 @@ class Flow: time.sleep(self.loop_interval) self.memory.append(history) - # if self.autosave: - # self.save_state("flow_state.json") + if self.autosave: + save_path = self.saved_state_path or "flow_state.json" + print(colored(f"Autosaving flow state to {save_path}", "green")) + self.save_state(save_path) - return response # , history + if self.return_history: + return response, history + + return response def _run(self, **kwargs: Any) -> str: """Generate a result using the provided keyword args.""" @@ -451,6 +453,19 @@ class Flow: """ return agent_history_prompt + async def run_concurrent(self, tasks: List[str], **kwargs): + """ + Run a batch of tasks concurrently and handle an infinite level of task inputs. + + Args: + tasks (List[str]): A list of tasks to run. + """ + task_coroutines = [ + self.run_async(task, **kwargs) for task in tasks + ] + completed_tasks = await asyncio.gather(*task_coroutines) + return completed_tasks + def bulk_run(self, inputs: List[Dict[str, Any]]) -> List[str]: """Generate responses for multiple input sets.""" return [self.run(**input_data) for input_data in inputs] @@ -666,7 +681,8 @@ class Flow: def get_llm_params(self): """ Extracts and returns the parameters of the llm object for serialization. - It assumes that the llm object has an __init__ method with parameters that can be used to recreate it. + It assumes that the llm object has an __init__ method + with parameters that can be used to recreate it. """ if not hasattr(self.llm, "__init__"): return None @@ -770,8 +786,24 @@ class Flow: Your response: """ response = self.llm(prompt, **kwargs) - return {"role": self.name, "content": response} + return {"role": self.agent_name, "content": response} def update_system_prompt(self, system_prompt: str): """Upddate the system message""" self.system_prompt = system_prompt + + def update_max_loops(self, max_loops: int): + """Update the max loops""" + self.max_loops = max_loops + + def update_loop_interval(self, loop_interval: int): + """Update the loop interval""" + self.loop_interval = loop_interval + + def update_retry_attempts(self, retry_attempts: int): + """Update the retry attempts""" + self.retry_attempts = retry_attempts + + def update_retry_interval(self, retry_interval: int): + """Update the retry interval""" + self.retry_interval = retry_interval diff --git a/tests/models/mpt7b.py b/tests/models/mpt7b.py index cdbd57f6..dfde578d 100644 --- a/tests/models/mpt7b.py +++ b/tests/models/mpt7b.py @@ -1,5 +1,6 @@ import pytest from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms.models.mpt import MPT7B From 7da009a5ec46d9469fd2690c7b37f11f49aca6e8 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 9 Nov 2023 16:49:12 -0500 Subject: [PATCH 63/63] dockerfile running Former-commit-id: 991979dfc6feb88f3152f49d6de0ac4cfbdcbc38 --- Dockerfile | 31 + demos/ui_software_demo.py | 2 +- swarms/__init__.py | 2 +- swarms/agents/__init__.py | 1 + swarms/agents/agent.py | 120 +-- swarms/agents/aot.py | 54 +- swarms/agents/browser_agent.py | 89 +- swarms/agents/hf_agents.py | 112 +-- swarms/agents/meta_prompter.py | 14 +- swarms/agents/multi_modal_visual_agent.py | 950 +++++++++--------- .../neural_architecture_search_worker.py | 1 - swarms/agents/omni_modal_agent.py | 32 +- swarms/agents/profitpilot.py | 105 +- swarms/agents/refiner_agent.py | 4 +- swarms/agents/registry.py | 4 +- swarms/agents/simple_agent.py | 3 +- swarms/artifacts/base.py | 8 +- swarms/artifacts/main.py | 15 +- swarms/chunkers/base.py | 39 +- swarms/chunkers/omni_chunker.py | 8 +- swarms/loaders/asana.py | 80 +- swarms/loaders/base.py | 126 ++- swarms/memory/base.py | 113 ++- swarms/memory/chroma.py | 90 +- swarms/memory/cosine_similarity.py | 6 +- swarms/memory/db.py | 23 +- swarms/memory/ocean.py | 9 +- swarms/memory/pg.py | 59 +- swarms/memory/pinecone.py | 53 +- swarms/memory/schemas.py | 34 +- swarms/memory/utils.py | 9 +- swarms/models/anthropic.py | 97 +- swarms/models/bioclip.py | 31 +- swarms/models/biogpt.py | 18 +- swarms/models/dalle3.py | 24 +- swarms/models/distilled_whisperx.py | 32 +- swarms/models/fastvit.py | 25 +- swarms/models/fuyu.py | 18 +- swarms/models/gpt4v.py | 77 +- swarms/models/huggingface.py | 88 +- swarms/models/idefics.py | 54 +- swarms/models/jina_embeds.py | 25 +- swarms/models/kosmos2.py | 48 +- swarms/models/kosmos_two.py | 80 +- swarms/models/llava.py | 5 +- swarms/models/mistral.py | 9 +- swarms/models/mpt.py | 18 +- swarms/models/nougat.py | 15 +- swarms/models/openai_assistant.py | 11 +- swarms/models/openai_embeddings.py | 128 ++- swarms/models/openai_models.py | 291 +++--- swarms/models/openai_tokenizer.py | 36 +- swarms/models/palm.py | 35 +- swarms/models/pegasus.py | 10 +- swarms/models/simple_ada.py | 4 +- swarms/models/speecht5.py | 15 +- swarms/models/timm.py | 5 +- swarms/models/trocr.py | 5 +- swarms/models/vilt.py | 6 +- swarms/models/wizard_storytelling.py | 76 +- swarms/models/yarn_mistral.py | 70 +- swarms/models/zephyr.py | 3 +- swarms/prompts/agent_output_parser.py | 5 +- swarms/prompts/agent_prompt.py | 25 +- swarms/prompts/agent_prompts.py | 30 +- swarms/prompts/base.py | 26 +- swarms/prompts/chat_prompt.py | 13 +- swarms/prompts/debate.py | 5 +- swarms/prompts/multi_modal_prompts.py | 3 +- swarms/prompts/python.py | 24 +- swarms/prompts/sales.py | 31 +- swarms/prompts/sales_prompts.py | 31 +- swarms/schemas/typings.py | 10 +- swarms/structs/document.py | 13 +- swarms/structs/flow.py | 58 +- swarms/structs/nonlinear_workflow.py | 13 +- swarms/structs/sequential_workflow.py | 81 +- swarms/structs/task.py | 18 +- swarms/structs/workflow.py | 12 +- swarms/swarms/autoscaler.py | 3 +- swarms/swarms/base.py | 4 +- swarms/swarms/battle_royal.py | 10 +- swarms/swarms/god_mode.py | 26 +- swarms/swarms/groupchat.py | 45 +- swarms/swarms/multi_agent_collab.py | 11 +- swarms/swarms/multi_agent_debate.py | 3 +- swarms/swarms/orchestrate.py | 27 +- swarms/swarms/simple_swarm.py | 1 - swarms/tools/autogpt.py | 42 +- swarms/tools/mm_models.py | 141 +-- swarms/tools/stt.py | 17 +- swarms/tools/tool.py | 221 ++-- swarms/tools/tool_registry.py | 5 +- swarms/utils/code_interpreter.py | 12 +- swarms/utils/decorators.py | 14 +- swarms/utils/futures.py | 4 +- swarms/utils/hash.py | 3 +- swarms/utils/main.py | 72 +- swarms/utils/parse_code.py | 3 +- swarms/utils/revutils.py | 38 +- swarms/utils/serializable.py | 17 +- swarms/utils/static.py | 1 - swarms/workers/worker.py | 20 +- 103 files changed, 2452 insertions(+), 2241 deletions(-) create mode 100644 Dockerfile diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..1ce589ae --- /dev/null +++ b/Dockerfile @@ -0,0 +1,31 @@ +# Use an official Python runtime as a parent image +FROM python:3.9-slim + +# Set environment variables to make Python output unbuffered and disable the PIP cache +ENV PYTHONDONTWRITEBYTECODE 1 +ENV PYTHONUNBUFFERED 1 +ENV PIP_NO_CACHE_DIR off +ENV PIP_DISABLE_PIP_VERSION_CHECK on +ENV PIP_DEFAULT_TIMEOUT 100 + +# Set the working directory in the container +WORKDIR /usr/src/app + +# Copy the current directory contents into the container at /usr/src/app +COPY . . + +# Install Poetry +RUN pip install poetry + +# Disable virtualenv creation by poetry and install dependencies +RUN poetry config virtualenvs.create false +RUN poetry install --no-interaction --no-ansi + +# Install the 'swarms' package if it's not included in the poetry.lock +RUN pip install swarms + +# Assuming tests require pytest to run +RUN pip install pytest + +# Run pytest on all tests in the tests directory +CMD find ./tests -name '*.py' -exec pytest {} + diff --git a/demos/ui_software_demo.py b/demos/ui_software_demo.py index 6271d96e..d322f71b 100644 --- a/demos/ui_software_demo.py +++ b/demos/ui_software_demo.py @@ -2,4 +2,4 @@ Autonomous swarm that optimizes UI autonomously GPT4Vision ->> GPT4 ->> UI -""" \ No newline at end of file +""" diff --git a/swarms/__init__.py b/swarms/__init__.py index 71481e16..f45f876f 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -9,6 +9,6 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" from swarms.agents import * from swarms.swarms import * from swarms.structs import * -from swarms.models import * +from swarms.models import * from swarms.chunkers import * from swarms.workers import * diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py index cd3aa221..52afb476 100644 --- a/swarms/agents/__init__.py +++ b/swarms/agents/__init__.py @@ -8,6 +8,7 @@ from swarms.agents.registry import Registry # from swarms.agents.idea_to_image_agent import Idea2Image from swarms.agents.simple_agent import SimpleAgent + """Agent Infrastructure, models, memory, utils, tools""" __all__ = [ diff --git a/swarms/agents/agent.py b/swarms/agents/agent.py index c16dd780..bad9d3bb 100644 --- a/swarms/agents/agent.py +++ b/swarms/agents/agent.py @@ -8,7 +8,8 @@ from langchain.chains.llm import LLMChain from langchain.chat_models.base import BaseChatModel from langchain.memory import ChatMessageHistory from langchain.prompts.chat import ( - BaseChatPromptTemplate,) + BaseChatPromptTemplate, +) from langchain.schema import ( BaseChatMessageHistory, Document, @@ -70,12 +71,14 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] send_token_limit: int = 4196 def construct_full_prompt(self, goals: List[str]) -> str: - prompt_start = ("Your decisions must always be made independently " - "without seeking user assistance.\n" - "Play to your strengths as an LLM and pursue simple " - "strategies with no legal complications.\n" - "If you have completed all your tasks, make sure to " - 'use the "finish" command.') + prompt_start = ( + "Your decisions must always be made independently " + "without seeking user assistance.\n" + "Play to your strengths as an LLM and pursue simple " + "strategies with no legal complications.\n" + "If you have completed all your tasks, make sure to " + 'use the "finish" command.' + ) # Construct full prompt full_prompt = ( f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n" @@ -87,23 +90,25 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] return full_prompt def format_messages(self, **kwargs: Any) -> List[BaseMessage]: - base_prompt = SystemMessage( - content=self.construct_full_prompt(kwargs["goals"])) + base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"])) time_prompt = SystemMessage( - content=f"The current time and date is {time.strftime('%c')}") - used_tokens = self.token_counter( - base_prompt.content) + self.token_counter(time_prompt.content) + content=f"The current time and date is {time.strftime('%c')}" + ) + used_tokens = self.token_counter(base_prompt.content) + self.token_counter( + time_prompt.content + ) memory: VectorStoreRetriever = kwargs["memory"] previous_messages = kwargs["messages"] - relevant_docs = memory.get_relevant_documents( - str(previous_messages[-10:])) + relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:])) relevant_memory = [d.page_content for d in relevant_docs] relevant_memory_tokens = sum( - [self.token_counter(doc) for doc in relevant_memory]) + [self.token_counter(doc) for doc in relevant_memory] + ) while used_tokens + relevant_memory_tokens > 2500: relevant_memory = relevant_memory[:-1] relevant_memory_tokens = sum( - [self.token_counter(doc) for doc in relevant_memory]) + [self.token_counter(doc) for doc in relevant_memory] + ) content_format = ( f"This reminds you of these events from your past:\n{relevant_memory}\n\n" ) @@ -141,23 +146,13 @@ class PromptGenerator: self.performance_evaluation: List[str] = [] self.response_format = { "thoughts": { - "text": - "thought", - "reasoning": - "reasoning", - "plan": - "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": - "constructive self-criticism", - "speak": - "thoughts summary to say to user", - }, - "command": { - "name": "command name", - "args": { - "arg name": "value" - } + "text": "thought", + "reasoning": "reasoning", + "plan": "- short bulleted\n- list that conveys\n- long-term plan", + "criticism": "constructive self-criticism", + "speak": "thoughts summary to say to user", }, + "command": {"name": "command name", "args": {"arg name": "value"}}, } def add_constraint(self, constraint: str) -> None: @@ -195,9 +190,7 @@ class PromptGenerator: """ self.performance_evaluation.append(evaluation) - def _generate_numbered_list(self, - items: list, - item_type: str = "list") -> str: + def _generate_numbered_list(self, items: list, item_type: str = "list") -> str: """ Generate a numbered list from given items based on the item_type. @@ -215,11 +208,16 @@ class PromptGenerator: for i, item in enumerate(items) ] finish_description = ( - "use this to signal that you have finished all your objectives") - finish_args = ('"response": "final response to let ' - 'people know you have finished your objectives"') - finish_string = (f"{len(items) + 1}. {FINISH_NAME}: " - f"{finish_description}, args: {finish_args}") + "use this to signal that you have finished all your objectives" + ) + finish_args = ( + '"response": "final response to let ' + 'people know you have finished your objectives"' + ) + finish_string = ( + f"{len(items) + 1}. {FINISH_NAME}: " + f"{finish_description}, args: {finish_args}" + ) return "\n".join(command_strings + [finish_string]) else: return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items)) @@ -240,7 +238,8 @@ class PromptGenerator: f"{self._generate_numbered_list(self.performance_evaluation)}\n\n" "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - "\nEnsure the response can be parsed by Python json.loads") + "\nEnsure the response can be parsed by Python json.loads" + ) return prompt_string @@ -261,11 +260,13 @@ def get_prompt(tools: List[BaseTool]) -> str: prompt_generator.add_constraint( "~16000 word limit for short term memory. " "Your short term memory is short, " - "so immediately save important information to files.") + "so immediately save important information to files." + ) prompt_generator.add_constraint( "If you are unsure how you previously did something " "or want to recall past events, " - "thinking about similar events will help you remember.") + "thinking about similar events will help you remember." + ) prompt_generator.add_constraint("No user assistance") prompt_generator.add_constraint( 'Exclusively use the commands listed in double quotes e.g. "command name"' @@ -277,23 +278,29 @@ def get_prompt(tools: List[BaseTool]) -> str: # Add resources to the PromptGenerator object prompt_generator.add_resource( - "Internet access for searches and information gathering.") + "Internet access for searches and information gathering." + ) prompt_generator.add_resource("Long Term memory management.") prompt_generator.add_resource( - "GPT-3.5 powered Agents for delegation of simple tasks.") + "GPT-3.5 powered Agents for delegation of simple tasks." + ) prompt_generator.add_resource("File output.") # Add performance evaluations to the PromptGenerator object prompt_generator.add_performance_evaluation( "Continuously review and analyze your actions " - "to ensure you are performing to the best of your abilities.") + "to ensure you are performing to the best of your abilities." + ) prompt_generator.add_performance_evaluation( - "Constructively self-criticize your big-picture behavior constantly.") + "Constructively self-criticize your big-picture behavior constantly." + ) prompt_generator.add_performance_evaluation( - "Reflect on past decisions and strategies to refine your approach.") + "Reflect on past decisions and strategies to refine your approach." + ) prompt_generator.add_performance_evaluation( "Every command has a cost, so be smart and efficient. " - "Aim to complete tasks in the least number of steps.") + "Aim to complete tasks in the least number of steps." + ) # Generate the prompt string prompt_string = prompt_generator.generate_prompt_string() @@ -364,8 +371,10 @@ class AutoGPT: ) def run(self, goals: List[str]) -> str: - user_input = ("Determine which next command to use, " - "and respond using the format specified above:") + user_input = ( + "Determine which next command to use, " + "and respond using the format specified above:" + ) # Interaction Loop loop_count = 0 while True: @@ -382,10 +391,8 @@ class AutoGPT: # Print Assistant thoughts print(assistant_reply) - self.chat_history_memory.add_message( - HumanMessage(content=user_input)) - self.chat_history_memory.add_message( - AIMessage(content=assistant_reply)) + self.chat_history_memory.add_message(HumanMessage(content=user_input)) + self.chat_history_memory.add_message(AIMessage(content=assistant_reply)) # Get command name and arguments action = self.output_parser.parse(assistant_reply) @@ -411,7 +418,8 @@ class AutoGPT: result = ( f"Unknown command '{action.name}'. " "Please refer to the 'COMMANDS' list for available " - "commands and only respond in the specified JSON format.") + "commands and only respond in the specified JSON format." + ) memory_to_add = f"Assistant Reply: {assistant_reply} \nResult: {result} " if self.feedback_tool is not None: diff --git a/swarms/agents/aot.py b/swarms/agents/aot.py index 123f5591..b36fb43c 100644 --- a/swarms/agents/aot.py +++ b/swarms/agents/aot.py @@ -4,13 +4,13 @@ import time import openai_model -logging.basicConfig(level=logging.INFO, - format="%(asctime)s - %(levelname)s - %(message)s") +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) logger = logging.getLogger(__name__) class OpenAI: - def __init__( self, api_key, @@ -68,13 +68,16 @@ class OpenAI: temperature=temperature, ) with open("openai.logs", "a") as log_file: - log_file.write("\n" + "-----------" + "\n" + "Prompt : " + - prompt + "\n") + log_file.write( + "\n" + "-----------" + "\n" + "Prompt : " + prompt + "\n" + ) return response except openai_model.error.RateLimitError as e: sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30) - print(f"{str(e)}, sleep for {sleep_duratoin}s, set it by env" - " OPENAI_RATE_TIMEOUT") + print( + f"{str(e)}, sleep for {sleep_duratoin}s, set it by env" + " OPENAI_RATE_TIMEOUT" + ) time.sleep(sleep_duratoin) def openai_choice2text_handler(self, choice): @@ -97,16 +100,11 @@ class OpenAI: else: response = self.run(prompt, 300, 0.5, k) thoughts = [ - self.openai_choice2text_handler(choice) - for choice in response.choices + self.openai_choice2text_handler(choice) for choice in response.choices ] return thoughts - def generate_thoughts(self, - state, - k, - initial_prompt, - rejected_solutions=None): + def generate_thoughts(self, state, k, initial_prompt, rejected_solutions=None): if isinstance(state, str): pass else: @@ -179,8 +177,7 @@ class OpenAI: """ response = self.run(prompt, 10, 1) try: - value_text = self.openai_choice2text_handler( - response.choices[0]) + value_text = self.openai_choice2text_handler(response.choices[0]) # print(f'state: {value_text}') value = float(value_text) print(f"Evaluated Thought Value: {value}") @@ -190,12 +187,10 @@ class OpenAI: return state_values else: - raise ValueError( - "Invalid evaluation strategy. Choose 'value' or 'vote'.") + raise ValueError("Invalid evaluation strategy. Choose 'value' or 'vote'.") class AoTAgent: - def __init__( self, num_thoughts: int = None, @@ -227,8 +222,7 @@ class AoTAgent: return None best_state, _ = max(self.output, key=lambda x: x[1]) - solution = self.model.generate_solution(self.initial_prompt, - best_state) + solution = self.model.generate_solution(self.initial_prompt, best_state) print(f"Solution is {solution}") return solution if solution else best_state except Exception as error: @@ -245,8 +239,11 @@ class AoTAgent: for next_state in thoughts: state_value = self.evaluated_thoughts[next_state] if state_value > self.value_threshold: - child = ((state, next_state) if isinstance(state, str) else - (*state, next_state)) + child = ( + (state, next_state) + if isinstance(state, str) + else (*state, next_state) + ) self.dfs(child, step + 1) # backtracking @@ -256,14 +253,17 @@ class AoTAgent: continue def generate_and_filter_thoughts(self, state): - thoughts = self.model.generate_thoughts(state, self.num_thoughts, - self.initial_prompt) + thoughts = self.model.generate_thoughts( + state, self.num_thoughts, self.initial_prompt + ) self.evaluated_thoughts = self.model.evaluate_states( - thoughts, self.initial_prompt) + thoughts, self.initial_prompt + ) filtered_thoughts = [ - thought for thought in thoughts + thought + for thought in thoughts if self.evaluated_thoughts[thought] >= self.pruning_threshold ] print(f"filtered_thoughts: {filtered_thoughts}") diff --git a/swarms/agents/browser_agent.py b/swarms/agents/browser_agent.py index 3a274468..02c4ef0d 100644 --- a/swarms/agents/browser_agent.py +++ b/swarms/agents/browser_agent.py @@ -38,8 +38,7 @@ def record(agent_name: str, autotab_ext_path: Optional[str] = None): if not os.path.exists("agents"): os.makedirs("agents") - if os.path.exists( - f"agents/{agent_name}.py") and config.environment != "local": + if os.path.exists(f"agents/{agent_name}.py") and config.environment != "local": if not _is_blank_agent(agent_name=agent_name): raise Exception(f"Agent with name {agent_name} already exists") driver = get_driver( # noqa: F841 @@ -55,10 +54,12 @@ def record(agent_name: str, autotab_ext_path: Optional[str] = None): print( "\033[34mYou have the Python debugger open, you can run commands in it like you" - " would in a normal Python shell.\033[0m") + " would in a normal Python shell.\033[0m" + ) print( "\033[34mTo exit, type 'q' and press enter. For a list of commands type '?' and" - " press enter.\033[0m") + " press enter.\033[0m" + ) breakpoint() @@ -78,13 +79,12 @@ def extract_domain_from_url(url: str): class AutotabChromeDriver(uc.Chrome): - def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - def find_element_with_retry(self, - by=By.ID, - value: Optional[str] = None) -> WebElement: + def find_element_with_retry( + self, by=By.ID, value: Optional[str] = None + ) -> WebElement: try: return super().find_element(by, value) except Exception as e: @@ -102,8 +102,11 @@ def open_plugin(driver: AutotabChromeDriver): def open_plugin_and_login(driver: AutotabChromeDriver): if config.autotab_api_key is not None: - backend_url = ("http://localhost:8000" if config.environment == "local" - else "https://api.autotab.com") + backend_url = ( + "http://localhost:8000" + if config.environment == "local" + else "https://api.autotab.com" + ) driver.get(f"{backend_url}/auth/signin-api-key-page") response = requests.post( f"{backend_url}/auth/signin-api-key", @@ -116,7 +119,8 @@ def open_plugin_and_login(driver: AutotabChromeDriver): else: raise Exception( f"Error {response.status_code} from backend while logging you in" - f" with your API key: {response.text}") + f" with your API key: {response.text}" + ) cookie["name"] = cookie["key"] del cookie["key"] driver.add_cookie(cookie) @@ -126,21 +130,26 @@ def open_plugin_and_login(driver: AutotabChromeDriver): else: print("No autotab API key found, heading to autotab.com to sign up") - url = ("http://localhost:3000/dashboard" if config.environment - == "local" else "https://autotab.com/dashboard") + url = ( + "http://localhost:3000/dashboard" + if config.environment == "local" + else "https://autotab.com/dashboard" + ) driver.get(url) time.sleep(0.5) open_plugin(driver) -def get_driver(autotab_ext_path: Optional[str] = None, - record_mode: bool = False) -> AutotabChromeDriver: +def get_driver( + autotab_ext_path: Optional[str] = None, record_mode: bool = False +) -> AutotabChromeDriver: options = webdriver.ChromeOptions() options.add_argument("--no-sandbox") # Necessary for running options.add_argument( "--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" - " (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36") + " (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36" + ) options.add_argument("--enable-webgl") options.add_argument("--enable-3d-apis") options.add_argument("--enable-clipboard-read-write") @@ -229,8 +238,7 @@ class Config(BaseModel): return cls( autotab_api_key=autotab_api_key, credentials=_credentials, - google_credentials=GoogleCredentials( - credentials=google_credentials), + google_credentials=GoogleCredentials(credentials=google_credentials), chrome_binary_location=config.get("chrome_binary_location"), environment=config.get("environment", "prod"), ) @@ -248,9 +256,9 @@ def is_signed_in_to_google(driver): return len([c for c in cookies if c["name"] == "SAPISID"]) != 0 -def google_login(driver, - credentials: Optional[SiteCredentials] = None, - navigate: bool = True): +def google_login( + driver, credentials: Optional[SiteCredentials] = None, navigate: bool = True +): print("Logging in to Google") if navigate: driver.get("https://accounts.google.com/") @@ -282,7 +290,8 @@ def google_login(driver, email_input.send_keys(credentials.email) email_input.send_keys(Keys.ENTER) WebDriverWait(driver, 10).until( - EC.element_to_be_clickable((By.CSS_SELECTOR, "[type='password']"))) + EC.element_to_be_clickable((By.CSS_SELECTOR, "[type='password']")) + ) password_input = driver.find_element(By.CSS_SELECTOR, "[type='password']") password_input.send_keys(credentials.password) @@ -305,20 +314,21 @@ def google_login(driver, cookies = driver.get_cookies() cookie_names = ["__Host-GAPS", "SMSV", "NID", "ACCOUNT_CHOOSER"] google_cookies = [ - cookie for cookie in cookies - if cookie["domain"] in [".google.com", "accounts.google.com"] and - cookie["name"] in cookie_names + cookie + for cookie in cookies + if cookie["domain"] in [".google.com", "accounts.google.com"] + and cookie["name"] in cookie_names ] with open("google_cookies.json", "w") as f: json.dump(google_cookies, f) # Log back in login_button = driver.find_element( - By.CSS_SELECTOR, f"[data-identifier='{credentials.email}']") + By.CSS_SELECTOR, f"[data-identifier='{credentials.email}']" + ) login_button.click() time.sleep(1) - password_input = driver.find_element(By.CSS_SELECTOR, - "[type='password']") + password_input = driver.find_element(By.CSS_SELECTOR, "[type='password']") password_input.send_keys(credentials.password) password_input.send_keys(Keys.ENTER) @@ -333,7 +343,8 @@ def login(driver, url: str): login_url = credentials.login_url if credentials.login_with_google_account: google_credentials = config.google_credentials.credentials[ - credentials.login_with_google_account] + credentials.login_with_google_account + ] _login_with_google(driver, login_url, google_credentials) else: _login(driver, login_url, credentials=credentials) @@ -360,15 +371,16 @@ def _login_with_google(driver, url: str, google_credentials: SiteCredentials): driver.get(url) WebDriverWait(driver, 10).until( - EC.presence_of_element_located((By.TAG_NAME, "body"))) + EC.presence_of_element_located((By.TAG_NAME, "body")) + ) main_window = driver.current_window_handle xpath = ( "//*[contains(text(), 'Continue with Google') or contains(text(), 'Sign in with" - " Google') or contains(@title, 'Sign in with Google')]") + " Google') or contains(@title, 'Sign in with Google')]" + ) - WebDriverWait(driver, - 10).until(EC.presence_of_element_located((By.XPATH, xpath))) + WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.XPATH, xpath))) driver.find_element( By.XPATH, xpath, @@ -376,8 +388,8 @@ def _login_with_google(driver, url: str, google_credentials: SiteCredentials): driver.switch_to.window(driver.window_handles[-1]) driver.find_element( - By.XPATH, - f"//*[contains(text(), '{google_credentials.email}')]").click() + By.XPATH, f"//*[contains(text(), '{google_credentials.email}')]" + ).click() driver.switch_to.window(main_window) @@ -430,11 +442,8 @@ def should_update(): # Parse the XML file root = ET.fromstring(xml_content) - namespaces = { - "ns": "http://www.google.com/update2/response" - } # add namespaces - xml_version = root.find(".//ns:app/ns:updatecheck", - namespaces).get("version") + namespaces = {"ns": "http://www.google.com/update2/response"} # add namespaces + xml_version = root.find(".//ns:app/ns:updatecheck", namespaces).get("version") # Load the local JSON file with open("src/extension/autotab/manifest.json", "r") as f: diff --git a/swarms/agents/hf_agents.py b/swarms/agents/hf_agents.py index e13d3462..4e186e3a 100644 --- a/swarms/agents/hf_agents.py +++ b/swarms/agents/hf_agents.py @@ -56,24 +56,23 @@ HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB = [ def get_remote_tools(organization="huggingface-tools"): if is_offline_mode(): - logger.info( - "You are in offline mode, so remote tools are not available.") + logger.info("You are in offline mode, so remote tools are not available.") return {} spaces = list_spaces(author=organization) tools = {} for space_info in spaces: repo_id = space_info.id - resolved_config_file = hf_hub_download(repo_id, - TOOL_CONFIG_FILE, - repo_type="space") + resolved_config_file = hf_hub_download( + repo_id, TOOL_CONFIG_FILE, repo_type="space" + ) with open(resolved_config_file, encoding="utf-8") as reader: config = json.load(reader) task = repo_id.split("/")[-1] - tools[config["name"]] = PreTool(task=task, - description=config["description"], - repo_id=repo_id) + tools[config["name"]] = PreTool( + task=task, description=config["description"], repo_id=repo_id + ) return tools @@ -93,7 +92,8 @@ def _setup_default_tools(): tool_class = getattr(tools_module, tool_class_name) description = tool_class.description HUGGINGFACE_DEFAULT_TOOLS[tool_class.name] = PreTool( - task=task_name, description=description, repo_id=None) + task=task_name, description=description, repo_id=None + ) if not is_offline_mode(): for task_name in HUGGINGFACE_DEFAULT_TOOLS_FROM_HUB: @@ -197,19 +197,18 @@ class Agent: one of the default tools, that default tool will be overridden. """ - def __init__(self, - chat_prompt_template=None, - run_prompt_template=None, - additional_tools=None): + def __init__( + self, chat_prompt_template=None, run_prompt_template=None, additional_tools=None + ): _setup_default_tools() agent_name = self.__class__.__name__ - self.chat_prompt_template = download_prompt(chat_prompt_template, - agent_name, - mode="chat") - self.run_prompt_template = download_prompt(run_prompt_template, - agent_name, - mode="run") + self.chat_prompt_template = download_prompt( + chat_prompt_template, agent_name, mode="chat" + ) + self.run_prompt_template = download_prompt( + run_prompt_template, agent_name, mode="run" + ) self._toolbox = HUGGINGFACE_DEFAULT_TOOLS.copy() self.log = print if additional_tools is not None: @@ -225,16 +224,17 @@ class Agent: } self._toolbox.update(additional_tools) if len(replacements) > 1: - names = "\n".join( - [f"- {n}: {t}" for n, t in replacements.items()]) + names = "\n".join([f"- {n}: {t}" for n, t in replacements.items()]) logger.warning( "The following tools have been replaced by the ones provided in" - f" `additional_tools`:\n{names}.") + f" `additional_tools`:\n{names}." + ) elif len(replacements) == 1: name = list(replacements.keys())[0] logger.warning( f"{name} has been replaced by {replacements[name]} as provided in" - " `additional_tools`.") + " `additional_tools`." + ) self.prepare_for_new_chat() @@ -244,20 +244,17 @@ class Agent: return self._toolbox def format_prompt(self, task, chat_mode=False): - description = "\n".join([ - f"- {name}: {tool.description}" - for name, tool in self.toolbox.items() - ]) + description = "\n".join( + [f"- {name}: {tool.description}" for name, tool in self.toolbox.items()] + ) if chat_mode: if self.chat_history is None: - prompt = self.chat_prompt_template.replace( - "<>", description) + prompt = self.chat_prompt_template.replace("<>", description) else: prompt = self.chat_history prompt += CHAT_MESSAGE_PROMPT.replace("<>", task) else: - prompt = self.run_prompt_template.replace("<>", - description) + prompt = self.run_prompt_template.replace("<>", description) prompt = prompt.replace("<>", task) return prompt @@ -306,19 +303,14 @@ class Agent: if not return_code: self.log("\n\n==Result==") self.cached_tools = resolve_tools( - code, - self.toolbox, - remote=remote, - cached_tools=self.cached_tools) + code, self.toolbox, remote=remote, cached_tools=self.cached_tools + ) self.chat_state.update(kwargs) - return evaluate(code, - self.cached_tools, - self.chat_state, - chat_mode=True) + return evaluate( + code, self.cached_tools, self.chat_state, chat_mode=True + ) else: - tool_code = get_tool_creation_code(code, - self.toolbox, - remote=remote) + tool_code = get_tool_creation_code(code, self.toolbox, remote=remote) return f"{tool_code}\n{code}" def prepare_for_new_chat(self): @@ -360,15 +352,12 @@ class Agent: self.log(f"\n\n==Code generated by the agent==\n{code}") if not return_code: self.log("\n\n==Result==") - self.cached_tools = resolve_tools(code, - self.toolbox, - remote=remote, - cached_tools=self.cached_tools) + self.cached_tools = resolve_tools( + code, self.toolbox, remote=remote, cached_tools=self.cached_tools + ) return evaluate(code, self.cached_tools, state=kwargs.copy()) else: - tool_code = get_tool_creation_code(code, - self.toolbox, - remote=remote) + tool_code = get_tool_creation_code(code, self.toolbox, remote=remote) return f"{tool_code}\n{code}" def generate_one(self, prompt, stop): @@ -428,7 +417,8 @@ class HFAgent(Agent): ): if not is_openai_available(): raise ImportError( - "Using `OpenAiAgent` requires `openai`: `pip install openai`.") + "Using `OpenAiAgent` requires `openai`: `pip install openai`." + ) if api_key is None: api_key = os.environ.get("OPENAI_API_KEY", None) @@ -436,7 +426,8 @@ class HFAgent(Agent): raise ValueError( "You need an openai key to use `OpenAIAgent`. You can get one here: Get" " one here https://openai.com/api/`. If you have one, set it in your" - " env with `os.environ['OPENAI_API_KEY'] = xxx.") + " env with `os.environ['OPENAI_API_KEY'] = xxx." + ) else: openai.api_key = api_key self.model = model @@ -461,10 +452,7 @@ class HFAgent(Agent): def _chat_generate(self, prompt, stop): result = openai.ChatCompletion.create( model=self.model, - messages=[{ - "role": "user", - "content": prompt - }], + messages=[{"role": "user", "content": prompt}], temperature=0, stop=stop, ) @@ -542,7 +530,8 @@ class AzureOpenAI(Agent): ): if not is_openai_available(): raise ImportError( - "Using `OpenAiAgent` requires `openai`: `pip install openai`.") + "Using `OpenAiAgent` requires `openai`: `pip install openai`." + ) self.deployment_id = deployment_id openai.api_type = "azure" @@ -552,7 +541,8 @@ class AzureOpenAI(Agent): raise ValueError( "You need an Azure openAI key to use `AzureOpenAIAgent`. If you have" " one, set it in your env with `os.environ['AZURE_OPENAI_API_KEY'] =" - " xxx.") + " xxx." + ) else: openai.api_key = api_key if resource_name is None: @@ -561,7 +551,8 @@ class AzureOpenAI(Agent): raise ValueError( "You need a resource_name to use `AzureOpenAIAgent`. If you have one," " set it in your env with `os.environ['AZURE_OPENAI_RESOURCE_NAME'] =" - " xxx.") + " xxx." + ) else: openai.api_base = f"https://{resource_name}.openai.azure.com" openai.api_version = api_version @@ -591,10 +582,7 @@ class AzureOpenAI(Agent): def _chat_generate(self, prompt, stop): result = openai.ChatCompletion.create( engine=self.deployment_id, - messages=[{ - "role": "user", - "content": prompt - }], + messages=[{"role": "user", "content": prompt}], temperature=0, stop=stop, ) diff --git a/swarms/agents/meta_prompter.py b/swarms/agents/meta_prompter.py index f744e38e..aeee9878 100644 --- a/swarms/agents/meta_prompter.py +++ b/swarms/agents/meta_prompter.py @@ -88,8 +88,9 @@ class MetaPrompterAgent: Assistant: """ - prompt = PromptTemplate(input_variables=["history", "human_input"], - template=template) + prompt = PromptTemplate( + input_variables=["history", "human_input"], template=template + ) self.chain = LLMChain( llm=self.llm(), @@ -101,15 +102,13 @@ class MetaPrompterAgent: def get_chat_history(self, chain_memory): """Get Chat History from the memory""" memory_key = chain_memory.memory_key - chat_history = chain_memory.load_memory_variables( - memory_key)[memory_key] + chat_history = chain_memory.load_memory_variables(memory_key)[memory_key] return chat_history def get_new_instructions(self, meta_output): """Get New Instructions from the meta_output""" delimiter = "Instructions: " - new_instructions = meta_output[meta_output.find(delimiter) + - len(delimiter):] + new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter) :] return new_instructions def run(self, task: str): @@ -150,7 +149,8 @@ class MetaPrompterAgent: meta_chain = self.initialize_meta_chain() meta_output = meta_chain.predict( - chat_history=self.get_chat_history(chain.memory)) + chat_history=self.get_chat_history(chain.memory) + ) print(f"Feedback: {meta_output}") self.instructions = self.get_new_instructions(meta_output) diff --git a/swarms/agents/multi_modal_visual_agent.py b/swarms/agents/multi_modal_visual_agent.py index 72b6c50e..34780594 100644 --- a/swarms/agents/multi_modal_visual_agent.py +++ b/swarms/agents/multi_modal_visual_agent.py @@ -150,7 +150,6 @@ def seed_everything(seed): def prompts(name, description): - def decorator(func): func.name = name func.description = description @@ -172,12 +171,9 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): kernel = np.multiply(kernel_h, np.transpose(kernel_w)) kernel[steps:-steps, steps:-steps] = 1 - kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, - steps - 1] - kernel[:steps, - -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] - kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, - steps - 1] + kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1] + kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)] + kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1] kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps] kernel = np.expand_dims(kernel, 2) kernel = np.repeat(kernel, 3, 2) @@ -211,12 +207,12 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100): kernel[steps:-steps, :steps] = left kernel[steps:-steps, -steps:] = right - pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] - gaussian_gt_img = (kernel * gt_img_array + (1 - kernel) * pt_gt_img - ) # gt img with blur img + pt_gt_img = easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] + gaussian_gt_img = ( + kernel * gt_img_array + (1 - kernel) * pt_gt_img + ) # gt img with blur img gaussian_gt_img = gaussian_gt_img.astype(np.int64) - easy_img[pos_h:pos_h + old_size[1], - pos_w:pos_w + old_size[0]] = gaussian_gt_img + easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] = gaussian_gt_img gaussian_img = Image.fromarray(easy_img) return gaussian_img @@ -256,7 +252,6 @@ def get_new_image_name(org_img_name, func_name="update"): class InstructPix2Pix: - def __init__(self, device): print(f"Initializing InstructPix2Pix to {device}") self.device = device @@ -265,102 +260,110 @@ class InstructPix2Pix: self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ).to(device) self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) @prompts( name="Instruct Image Using Text", - description= - ("useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. "), + description=( + "useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. " + ), ) def inference(self, inputs): """Change style of image.""" print("===>Starting InstructPix2Pix Inference") image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) original_image = Image.open(image_path) - image = self.pipe(text, - image=original_image, - num_inference_steps=40, - image_guidance_scale=1.2).images[0] + image = self.pipe( + text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2 + ).images[0] updated_image_path = get_new_image_name(image_path, func_name="pix2pix") image.save(updated_image_path) print( f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" - f" {text}, Output Image: {updated_image_path}") + f" {text}, Output Image: {updated_image_path}" + ) return updated_image_path class Text2Image: - def __init__(self, device): print(f"Initializing Text2Image to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.pipe = StableDiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype) + "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype + ) self.pipe.to(device) self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image From User Input Text", - description= - ("useful when you want to generate an image from a user input text and save" - " it to a file. like: generate an image of an object or something, or" - " generate an image that includes some objects. The input to this tool" - " should be a string, representing the text used to generate image. "), + description=( + "useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. " + ), ) def inference(self, text): image_filename = os.path.join("image", f"{str(uuid.uuid4())[:8]}.png") prompt = text + ", " + self.a_prompt image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0] image.save(image_filename) - print(f"\nProcessed Text2Image, Input Text: {text}, Output Image:" - f" {image_filename}") + print( + f"\nProcessed Text2Image, Input Text: {text}, Output Image:" + f" {image_filename}" + ) return image_filename class ImageCaptioning: - def __init__(self, device): print(f"Initializing ImageCaptioning to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-image-captioning-base") + "Salesforce/blip-image-captioning-base" + ) self.model = BlipForConditionalGeneration.from_pretrained( - "Salesforce/blip-image-captioning-base", - torch_dtype=self.torch_dtype).to(self.device) + "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype + ).to(self.device) @prompts( name="Get Photo Description", - description= - ("useful when you want to know what is inside the photo. receives image_path" - " as input. The input to this tool should be a string, representing the" - " image_path. "), + description=( + "useful when you want to know what is inside the photo. receives image_path" + " as input. The input to this tool should be a string, representing the" + " image_path. " + ), ) def inference(self, image_path): - inputs = self.processor(Image.open(image_path), - return_tensors="pt").to(self.device, - self.torch_dtype) + inputs = self.processor(Image.open(image_path), return_tensors="pt").to( + self.device, self.torch_dtype + ) out = self.model.generate(**inputs) captions = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text:" - f" {captions}") + f" {captions}" + ) return captions class Image2Canny: - def __init__(self, device): print("Initializing Image2Canny") self.low_threshold = 100 @@ -368,11 +371,12 @@ class Image2Canny: @prompts( name="Edge Detection On Image", - description= - ("useful when you want to detect the edge of the image. like: detect the" - " edges of this image, or canny detection on image, or perform edge" - " detection on this image, or detect the canny image of this image. The" - " input to this tool should be a string, representing the image_path"), + description=( + "useful when you want to detect the edge of the image. like: detect the" + " edges of this image, or canny detection on image, or perform edge" + " detection on this image, or detect the canny image of this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -383,13 +387,14 @@ class Image2Canny: canny = Image.fromarray(canny) updated_image_path = get_new_image_name(inputs, func_name="edge") canny.save(updated_image_path) - print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text:" + f" {updated_image_path}" + ) return updated_image_path class CannyText2Image: - def __init__(self, device): print(f"Initializing CannyText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -401,31 +406,36 @@ class CannyText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Canny Image", - description= - ("useful when you want to generate a new real image from both the user" - " description and a canny image. like: generate a real image of a object or" - " something from this canny image, or generate a new real image of a object" - " or something from this edge image. The input to this tool should be a" - " comma separated string of two, representing the image_path and the user" - " description. "), + description=( + "useful when you want to generate a new real image from both the user" + " description and a canny image. like: generate a real image of a object or" + " something from this canny image, or generate a new real image of a object" + " or something from this edge image. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description. " + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -438,77 +448,83 @@ class CannyText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="canny2image") + updated_image_path = get_new_image_name(image_path, func_name="canny2image") image.save(updated_image_path) print( f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text:" - f" {instruct_text}, Output Text: {updated_image_path}") + f" {instruct_text}, Output Text: {updated_image_path}" + ) return updated_image_path class Image2Line: - def __init__(self, device): print("Initializing Image2Line") self.detector = MLSDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Line Detection On Image", - description= - ("useful when you want to detect the straight line of the image. like:" - " detect the straight lines of this image, or straight line detection on" - " image, or perform straight line detection on this image, or detect the" - " straight line image of this image. The input to this tool should be a" - " string, representing the image_path"), + description=( + "useful when you want to detect the straight line of the image. like:" + " detect the straight lines of this image, or straight line detection on" + " image, or perform straight line detection on this image, or detect the" + " straight line image of this image. The input to this tool should be a" + " string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) mlsd = self.detector(image) updated_image_path = get_new_image_name(inputs, func_name="line-of") mlsd.save(updated_image_path) - print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Line, Input Image: {inputs}, Output Line:" + f" {updated_image_path}" + ) return updated_image_path class LineText2Image: - def __init__(self, device): print(f"Initializing LineText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-mlsd", - torch_dtype=self.torch_dtype) + "fusing/stable-diffusion-v1-5-controlnet-mlsd", torch_dtype=self.torch_dtype + ) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Line Image", - description= - ("useful when you want to generate a new real image from both the user" - " description and a straight line image. like: generate a real image of a" - " object or something from this straight line image, or generate a new real" - " image of a object or something from this straight lines. The input to" - " this tool should be a comma separated string of two, representing the" - " image_path and the user description. "), + description=( + "useful when you want to generate a new real image from both the user" + " description and a straight line image. like: generate a real image of a" + " object or something from this straight line image, or generate a new real" + " image of a object or something from this straight lines. The input to" + " this tool should be a comma separated string of two, representing the" + " image_path and the user description. " + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -521,78 +537,83 @@ class LineText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="line2image") + updated_image_path = get_new_image_name(image_path, func_name="line2image") image.save(updated_image_path) print( f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text:" - f" {instruct_text}, Output Text: {updated_image_path}") + f" {instruct_text}, Output Text: {updated_image_path}" + ) return updated_image_path class Image2Hed: - def __init__(self, device): print("Initializing Image2Hed") self.detector = HEDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Hed Detection On Image", - description= - ("useful when you want to detect the soft hed boundary of the image. like:" - " detect the soft hed boundary of this image, or hed boundary detection on" - " image, or perform hed boundary detection on this image, or detect soft" - " hed boundary image of this image. The input to this tool should be a" - " string, representing the image_path"), + description=( + "useful when you want to detect the soft hed boundary of the image. like:" + " detect the soft hed boundary of this image, or hed boundary detection on" + " image, or perform hed boundary detection on this image, or detect soft" + " hed boundary image of this image. The input to this tool should be a" + " string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) hed = self.detector(image) - updated_image_path = get_new_image_name(inputs, - func_name="hed-boundary") + updated_image_path = get_new_image_name(inputs, func_name="hed-boundary") hed.save(updated_image_path) - print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed:" + f" {updated_image_path}" + ) return updated_image_path class HedText2Image: - def __init__(self, device): print(f"Initializing HedText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-hed", - torch_dtype=self.torch_dtype) + "fusing/stable-diffusion-v1-5-controlnet-hed", torch_dtype=self.torch_dtype + ) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Soft Hed Boundary Image", - description= - ("useful when you want to generate a new real image from both the user" - " description and a soft hed boundary image. like: generate a real image of" - " a object or something from this soft hed boundary image, or generate a" - " new real image of a object or something from this hed boundary. The input" - " to this tool should be a comma separated string of two, representing the" - " image_path and the user description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and a soft hed boundary image. like: generate a real image of" + " a object or something from this soft hed boundary image, or generate a" + " new real image of a object or something from this hed boundary. The input" + " to this tool should be a comma separated string of two, representing the" + " image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -605,27 +626,28 @@ class HedText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="hed2image") + updated_image_path = get_new_image_name(image_path, func_name="hed2image") image.save(updated_image_path) - print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + print( + f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class Image2Scribble: - def __init__(self, device): print("Initializing Image2Scribble") self.detector = HEDdetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Sketch Detection On Image", - description= - ("useful when you want to generate a scribble of the image. like: generate a" - " scribble of this image, or generate a sketch from this image, detect the" - " sketch from this image. The input to this tool should be a string," - " representing the image_path"), + description=( + "useful when you want to generate a scribble of the image. like: generate a" + " scribble of this image, or generate a sketch from this image, detect the" + " sketch from this image. The input to this tool should be a string," + " representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -634,12 +656,12 @@ class Image2Scribble: scribble.save(updated_image_path) print( f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble:" - f" {updated_image_path}") + f" {updated_image_path}" + ) return updated_image_path class ScribbleText2Image: - def __init__(self, device): print(f"Initializing ScribbleText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -651,29 +673,34 @@ class ScribbleText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Sketch Image", - description= - ("useful when you want to generate a new real image from both the user" - " description and a scribble image or a sketch image. The input to this" - " tool should be a comma separated string of two, representing the" - " image_path and the user description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and a scribble image or a sketch image. The input to this" + " tool should be a comma separated string of two, representing the" + " image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -686,41 +713,41 @@ class ScribbleText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="scribble2image") + updated_image_path = get_new_image_name(image_path, func_name="scribble2image") image.save(updated_image_path) print( f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class Image2Pose: - def __init__(self, device): print("Initializing Image2Pose") - self.detector = OpenposeDetector.from_pretrained( - "lllyasviel/ControlNet") + self.detector = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") @prompts( name="Pose Detection On Image", - description= - ("useful when you want to detect the human pose of the image. like: generate" - " human poses of this image, or generate a pose image from this image. The" - " input to this tool should be a string, representing the image_path"), + description=( + "useful when you want to detect the human pose of the image. like: generate" + " human poses of this image, or generate a pose image from this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) pose = self.detector(image) updated_image_path = get_new_image_name(inputs, func_name="human-pose") pose.save(updated_image_path) - print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose:" + f" {updated_image_path}" + ) return updated_image_path class PoseText2Image: - def __init__(self, device): print(f"Initializing PoseText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -732,11 +759,13 @@ class PoseText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.num_inference_steps = 20 self.seed = -1 @@ -744,20 +773,23 @@ class PoseText2Image: self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality") + " fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Pose Image", - description= - ("useful when you want to generate a new real image from both the user" - " description and a human pose image. like: generate a real image of a" - " human from this human pose image, or generate a new real image of a human" - " from this pose. The input to this tool should be a comma separated string" - " of two, representing the image_path and the user description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and a human pose image. like: generate a real image of a" + " human from this human pose image, or generate a new real image of a human" + " from this pose. The input to this tool should be a comma separated string" + " of two, representing the image_path and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -770,52 +802,56 @@ class PoseText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="pose2image") + updated_image_path = get_new_image_name(image_path, func_name="pose2image") image.save(updated_image_path) print( f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class SegText2Image: - def __init__(self, device): print(f"Initializing SegText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.controlnet = ControlNetModel.from_pretrained( - "fusing/stable-diffusion-v1-5-controlnet-seg", - torch_dtype=self.torch_dtype) + "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=self.torch_dtype + ) self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality") + " fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Segmentations", - description= - ("useful when you want to generate a new real image from both the user" - " description and segmentations. like: generate a real image of a object or" - " something from this segmentation image, or generate a new real image of a" - " object or something from these segmentations. The input to this tool" - " should be a comma separated string of two, representing the image_path" - " and the user description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and segmentations. like: generate a real image of a object or" + " something from this segmentation image, or generate a new real image of a" + " object or something from these segmentations. The input to this tool" + " should be a comma separated string of two, representing the image_path" + " and the user description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -828,27 +864,28 @@ class SegText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="segment2image") + updated_image_path = get_new_image_name(image_path, func_name="segment2image") image.save(updated_image_path) - print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + print( + f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text:" + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class Image2Depth: - def __init__(self, device): print("Initializing Image2Depth") self.depth_estimator = pipeline("depth-estimation") @prompts( name="Predict Depth On Image", - description= - ("useful when you want to detect depth of the image. like: generate the" - " depth from this image, or detect the depth map on this image, or predict" - " the depth for this image. The input to this tool should be a string," - " representing the image_path"), + description=( + "useful when you want to detect depth of the image. like: generate the" + " depth from this image, or detect the depth map on this image, or predict" + " the depth for this image. The input to this tool should be a string," + " representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -859,13 +896,14 @@ class Image2Depth: depth = Image.fromarray(depth) updated_image_path = get_new_image_name(inputs, func_name="depth") depth.save(updated_image_path) - print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}" + ) return updated_image_path class DepthText2Image: - def __init__(self, device): print(f"Initializing DepthText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -877,31 +915,36 @@ class DepthText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality") + " fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Depth", - description= - ("useful when you want to generate a new real image from both the user" - " description and depth image. like: generate a real image of a object or" - " something from this depth image, or generate a new real image of a object" - " or something from the depth map. The input to this tool should be a comma" - " separated string of two, representing the image_path and the user" - " description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and depth image. like: generate a real image of a object or" + " something from this depth image, or generate a new real image of a object" + " or something from the depth map. The input to this tool should be a comma" + " separated string of two, representing the image_path and the user" + " description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -914,29 +957,30 @@ class DepthText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="depth2image") + updated_image_path = get_new_image_name(image_path, func_name="depth2image") image.save(updated_image_path) print( f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class Image2Normal: - def __init__(self, device): print("Initializing Image2Normal") - self.depth_estimator = pipeline("depth-estimation", - model="Intel/dpt-hybrid-midas") + self.depth_estimator = pipeline( + "depth-estimation", model="Intel/dpt-hybrid-midas" + ) self.bg_threhold = 0.4 @prompts( name="Predict Normal Map On Image", - description= - ("useful when you want to detect norm map of the image. like: generate" - " normal map from this image, or predict normal map of this image. The" - " input to this tool should be a string, representing the image_path"), + description=( + "useful when you want to detect norm map of the image. like: generate" + " normal map from this image, or predict normal map of this image. The" + " input to this tool should be a string, representing the image_path" + ), ) def inference(self, inputs): image = Image.open(inputs) @@ -952,19 +996,20 @@ class Image2Normal: y[image_depth < self.bg_threhold] = 0 z = np.ones_like(x) * np.pi * 2.0 image = np.stack([x, y, z], axis=2) - image /= np.sum(image**2.0, axis=2, keepdims=True)**0.5 + image /= np.sum(image**2.0, axis=2, keepdims=True) ** 0.5 image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) image = Image.fromarray(image) image = image.resize(original_size) updated_image_path = get_new_image_name(inputs, func_name="normal-map") image.save(updated_image_path) - print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth:" - f" {updated_image_path}") + print( + f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth:" + f" {updated_image_path}" + ) return updated_image_path class NormalText2Image: - def __init__(self, device): print(f"Initializing NormalText2Image to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 @@ -976,31 +1021,36 @@ class NormalText2Image: "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), torch_dtype=self.torch_dtype, ) self.pipe.scheduler = UniPCMultistepScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) self.pipe.to(device) self.seed = -1 self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit," - " fewer digits, cropped, worst quality, low quality") + " fewer digits, cropped, worst quality, low quality" + ) @prompts( name="Generate Image Condition On Normal Map", - description= - ("useful when you want to generate a new real image from both the user" - " description and normal map. like: generate a real image of a object or" - " something from this normal map, or generate a new real image of a object" - " or something from the normal map. The input to this tool should be a" - " comma separated string of two, representing the image_path and the user" - " description"), + description=( + "useful when you want to generate a new real image from both the user" + " description and normal map. like: generate a real image of a object or" + " something from this normal map, or generate a new real image of a object" + " or something from the normal map. The input to this tool should be a" + " comma separated string of two, representing the image_path and the user" + " description" + ), ) def inference(self, inputs): image_path, instruct_text = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) image = Image.open(image_path) self.seed = random.randint(0, 65535) seed_everything(self.seed) @@ -1013,53 +1063,50 @@ class NormalText2Image: negative_prompt=self.n_prompt, guidance_scale=9.0, ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="normal2image") + updated_image_path = get_new_image_name(image_path, func_name="normal2image") image.save(updated_image_path) print( f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text:" - f" {instruct_text}, Output Image: {updated_image_path}") + f" {instruct_text}, Output Image: {updated_image_path}" + ) return updated_image_path class VisualQuestionAnswering: - def __init__(self, device): print(f"Initializing VisualQuestionAnswering to {device}") self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.device = device - self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-vqa-base") + self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") self.model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", - torch_dtype=self.torch_dtype).to(self.device) + "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype + ).to(self.device) @prompts( name="Answer Question About The Image", - description= - ("useful when you need an answer for a question based on an image. like:" - " what is the background color of the last image, how many cats in this" - " figure, what is in this figure. The input to this tool should be a comma" - " separated string of two, representing the image_path and the question" + description=( + "useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" ), ) def inference(self, inputs): - image_path, question = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + image_path, question = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) raw_image = Image.open(image_path).convert("RGB") - inputs = self.processor(raw_image, question, - return_tensors="pt").to(self.device, - self.torch_dtype) + inputs = self.processor(raw_image, question, return_tensors="pt").to( + self.device, self.torch_dtype + ) out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}") + f" Question: {question}, Output Answer: {answer}" + ) return answer class Segmenting: - def __init__(self, device): print(f"Inintializing Segmentation to {device}") self.device = device @@ -1104,8 +1151,7 @@ class Segmenting: h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) * 255 - image = cv2.addWeighted(image, 0.7, mask_image.astype("uint8"), - transparency, 0) + image = cv2.addWeighted(image, 0.7, mask_image.astype("uint8"), transparency, 0) return image @@ -1113,12 +1159,10 @@ class Segmenting: x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch( - plt.Rectangle((x0, y0), - w, - h, - edgecolor="green", - facecolor=(0, 0, 0, 0), - lw=2)) + plt.Rectangle( + (x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=2 + ) + ) ax.text(x0, y0, label) def get_mask_with_boxes(self, image_pil, image, boxes_filt): @@ -1131,7 +1175,8 @@ class Segmenting: boxes_filt = boxes_filt.cpu() transformed_boxes = self.sam_predictor.transform.apply_boxes_torch( - boxes_filt, image.shape[:2]).to(self.device) + boxes_filt, image.shape[:2] + ).to(self.device) masks, _, _ = self.sam_predictor.predict_torch( point_coords=None, @@ -1141,8 +1186,7 @@ class Segmenting: ) return masks - def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, - pred_phrases): + def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, pred_phrases): image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam_predictor.set_image(image) @@ -1152,13 +1196,11 @@ class Segmenting: # draw output image for mask in masks: - image = self.show_mask(mask[0].cpu().numpy(), - image, - random_color=True, - transparency=0.3) + image = self.show_mask( + mask[0].cpu().numpy(), image, random_color=True, transparency=0.3 + ) - updated_image_path = get_new_image_name(image_path, - func_name="segmentation") + updated_image_path = get_new_image_name(image_path, func_name="segmentation") new_image = Image.fromarray(image) new_image.save(updated_image_path) @@ -1170,8 +1212,9 @@ class Segmenting: with torch.cuda.amp.autocast(): self.sam_predictor.set_image(img) - def show_points(self, coords: np.ndarray, labels: np.ndarray, - image: np.ndarray) -> np.ndarray: + def show_points( + self, coords: np.ndarray, labels: np.ndarray, image: np.ndarray + ) -> np.ndarray: """Visualize points on top of an image. Args: @@ -1185,17 +1228,13 @@ class Segmenting: pos_points = coords[labels == 1] neg_points = coords[labels == 0] for p in pos_points: - image = cv2.circle(image, - p.astype(int), - radius=3, - color=(0, 255, 0), - thickness=-1) + image = cv2.circle( + image, p.astype(int), radius=3, color=(0, 255, 0), thickness=-1 + ) for p in neg_points: - image = cv2.circle(image, - p.astype(int), - radius=3, - color=(255, 0, 0), - thickness=-1) + image = cv2.circle( + image, p.astype(int), radius=3, color=(255, 0, 0), thickness=-1 + ) return image def segment_image_with_click(self, img, is_positive: bool): @@ -1213,17 +1252,13 @@ class Segmenting: multimask_output=False, ) - img = self.show_mask(masks[0], - img, - random_color=False, - transparency=0.3) + img = self.show_mask(masks[0], img, random_color=False, transparency=0.3) img = self.show_points(input_point, input_label, img) return img - def segment_image_with_coordinate(self, img, is_positive: bool, - coordinate: tuple): + def segment_image_with_coordinate(self, img, is_positive: bool, coordinate: tuple): """ Args: img (numpy.ndarray): the given image, shape: H x W x 3. @@ -1254,10 +1289,7 @@ class Segmenting: multimask_output=False, ) - img = self.show_mask(masks[0], - img, - random_color=False, - transparency=0.3) + img = self.show_mask(masks[0], img, random_color=False, transparency=0.3) img = self.show_points(input_point, input_label, img) @@ -1269,12 +1301,13 @@ class Segmenting: @prompts( name="Segment the Image", - description= - ("useful when you want to segment all the part of the image, but not segment" - " a certain object.like: segment all the object in this image, or generate" - " segmentations on this image, or segment the image,or perform segmentation" - " on this image, or segment all the object in this image.The input to this" - " tool should be a string, representing the image_path"), + description=( + "useful when you want to segment all the part of the image, but not segment" + " a certain object.like: segment all the object in this image, or generate" + " segmentations on this image, or segment the image,or perform segmentation" + " on this image, or segment all the object in this image.The input to this" + " tool should be a string, representing the image_path" + ), ) def inference_all(self, image_path): image = cv2.imread(image_path) @@ -1295,26 +1328,19 @@ class Segmenting: img[:, :, i] = color_mask[i] ax.imshow(np.dstack((img, m))) - updated_image_path = get_new_image_name(image_path, - func_name="segment-image") + updated_image_path = get_new_image_name(image_path, func_name="segment-image") plt.axis("off") - plt.savefig(updated_image_path, - bbox_inches="tight", - dpi=300, - pad_inches=0.0) + plt.savefig(updated_image_path, bbox_inches="tight", dpi=300, pad_inches=0.0) return updated_image_path class Text2Box: - def __init__(self, device): print(f"Initializing ObjectDetection to {device}") self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 - self.model_checkpoint_path = os.path.join("checkpoints", - "groundingdino") - self.model_config_path = os.path.join("checkpoints", - "grounding_config.py") + self.model_checkpoint_path = os.path.join("checkpoints", "groundingdino") + self.model_config_path = os.path.join("checkpoints", "grounding_config.py") self.download_parameters() self.box_threshold = 0.3 self.text_threshold = 0.25 @@ -1332,11 +1358,13 @@ class Text2Box: # load image image_pil = Image.open(image_path).convert("RGB") # load image - transform = T.Compose([ - T.RandomResize([512], max_size=1333), - T.ToTensor(), - T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ]) + transform = T.Compose( + [ + T.RandomResize([512], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) image, _ = transform(image_pil, None) # 3, h, w return image_pil, image @@ -1345,8 +1373,9 @@ class Text2Box: args.device = self.device model = build_model(args) checkpoint = torch.load(self.model_checkpoint_path, map_location="cpu") - load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), - strict=False) + load_res = model.load_state_dict( + clean_state_dict(checkpoint["model"]), strict=False + ) print(load_res) _ = model.eval() return model @@ -1377,11 +1406,11 @@ class Text2Box: # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): - pred_phrase = get_phrases_from_posmap(logit > self.text_threshold, - tokenized, tokenlizer) + pred_phrase = get_phrases_from_posmap( + logit > self.text_threshold, tokenized, tokenlizer + ) if with_logits: - pred_phrases.append(pred_phrase + - f"({str(logit.max().item())[:4]})") + pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) @@ -1391,8 +1420,7 @@ class Text2Box: H, W = tgt["size"] boxes = tgt["boxes"] labels = tgt["labels"] - assert len(boxes) == len( - labels), "boxes and labels must have same length" + assert len(boxes) == len(labels), "boxes and labels must have same length" draw = ImageDraw.Draw(image_pil) mask = Image.new("L", image_pil.size, 0) @@ -1430,11 +1458,12 @@ class Text2Box: @prompts( name="Detect the Give Object", - description= - ("useful when you only want to detect or find out given objects in the" - " pictureThe input to this tool should be a comma separated string of two," - " representing the image_path, the text description of the object to be" - " found"), + description=( + "useful when you only want to detect or find out given objects in the" + " pictureThe input to this tool should be a comma separated string of two," + " representing the image_path, the text description of the object to be" + " found" + ), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") @@ -1452,18 +1481,19 @@ class Text2Box: image_with_box = self.plot_boxes_to_image(image_pil, pred_dict)[0] - updated_image_path = get_new_image_name(image_path, - func_name="detect-something") + updated_image_path = get_new_image_name( + image_path, func_name="detect-something" + ) updated_image = image_with_box.resize(size) updated_image.save(updated_image_path) print( f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be" - f" Detect {det_prompt}, Output Image: {updated_image_path}") + f" Detect {det_prompt}, Output Image: {updated_image_path}" + ) return updated_image_path class Inpainting: - def __init__(self, device): self.device = device self.revision = "fp16" if "cuda" in self.device else None @@ -1474,16 +1504,13 @@ class Inpainting: revision=self.revision, torch_dtype=self.torch_dtype, safety_checker=StableDiffusionSafetyChecker.from_pretrained( - "CompVis/stable-diffusion-safety-checker"), + "CompVis/stable-diffusion-safety-checker" + ), ).to(device) - def __call__(self, - prompt, - image, - mask_image, - height=512, - width=512, - num_inference_steps=50): + def __call__( + self, prompt, image, mask_image, height=512, width=512, num_inference_steps=50 + ): update_image = self.inpaint( prompt=prompt, image=image.resize((width, height)), @@ -1506,27 +1533,29 @@ class InfinityOutPainting: self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) def get_BLIP_vqa(self, image, question): - inputs = self.ImageVQA.processor(image, question, - return_tensors="pt").to( - self.ImageVQA.device, - self.ImageVQA.torch_dtype) + inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to( + self.ImageVQA.device, self.ImageVQA.torch_dtype + ) out = self.ImageVQA.model.generate(**inputs) - answer = self.ImageVQA.processor.decode(out[0], - skip_special_tokens=True) + answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output" - f" Answer: {answer}") + f" Answer: {answer}" + ) return answer def get_BLIP_caption(self, image): inputs = self.ImageCaption.processor(image, return_tensors="pt").to( - self.ImageCaption.device, self.ImageCaption.torch_dtype) + self.ImageCaption.device, self.ImageCaption.torch_dtype + ) out = self.ImageCaption.model.generate(**inputs) BLIP_caption = self.ImageCaption.processor.decode( - out[0], skip_special_tokens=True) + out[0], skip_special_tokens=True + ) return BLIP_caption def check_prompt(self, prompt): @@ -1540,7 +1569,8 @@ class InfinityOutPainting: def get_imagine_caption(self, image, imagine): BLIP_caption = self.get_BLIP_caption(image) background_color = self.get_BLIP_vqa( - image, "what is the background color of this image") + image, "what is the background color of this image" + ) style = self.get_BLIP_vqa(image, "what is the style of this image") imagine_prompt = ( "let's pretend you are an excellent painter and now there is an incomplete" @@ -1548,47 +1578,54 @@ class InfinityOutPainting: " painting and describe ityou should consider the background color is" f" {background_color}, the style is {style}You should make the painting as" " vivid and realistic as possibleYou can not use words like painting or" - " pictureand you should use no more than 50 words to describe it") + " pictureand you should use no more than 50 words to describe it" + ) caption = self.llm(imagine_prompt) if imagine else BLIP_caption caption = self.check_prompt(caption) - print(f"BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}" - ) if imagine else print(f"Prompt: {caption}") + print( + f"BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}" + ) if imagine else print(f"Prompt: {caption}") return caption def resize_image(self, image, max_size=1000000, multiple=8): aspect_ratio = image.size[0] / image.size[1] new_width = int(math.sqrt(max_size * aspect_ratio)) new_height = int(new_width / aspect_ratio) - new_width, new_height = new_width - ( - new_width % multiple), new_height - (new_height % multiple) + new_width, new_height = new_width - (new_width % multiple), new_height - ( + new_height % multiple + ) return image.resize((new_width, new_height)) def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt): old_img = original_img while old_img.size != tosize: - prompt = (self.check_prompt(usr_prompt) if usr_prompt else - self.get_imagine_caption(old_img, imagine)) + prompt = ( + self.check_prompt(usr_prompt) + if usr_prompt + else self.get_imagine_caption(old_img, imagine) + ) crop_w = 15 if old_img.size[0] != tosize[0] else 0 crop_h = 15 if old_img.size[1] != tosize[1] else 0 old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h)) temp_canvas_size = ( expand_ratio * old_img.width - if expand_ratio * old_img.width < tosize[0] else tosize[0], + if expand_ratio * old_img.width < tosize[0] + else tosize[0], expand_ratio * old_img.height - if expand_ratio * old_img.height < tosize[1] else tosize[1], + if expand_ratio * old_img.height < tosize[1] + else tosize[1], ) - temp_canvas, temp_mask = Image.new("RGB", - temp_canvas_size, - color="white"), Image.new( - "L", - temp_canvas_size, - color="white") + temp_canvas, temp_mask = Image.new( + "RGB", temp_canvas_size, color="white" + ), Image.new("L", temp_canvas_size, color="white") x, y = (temp_canvas.width - old_img.width) // 2, ( - temp_canvas.height - old_img.height) // 2 + temp_canvas.height - old_img.height + ) // 2 temp_canvas.paste(old_img, (x, y)) temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height)) resized_temp_canvas, resized_temp_mask = self.resize_image( - temp_canvas), self.resize_image(temp_mask) + temp_canvas + ), self.resize_image(temp_mask) image = self.inpaint( prompt=prompt, image=resized_temp_canvas, @@ -1603,11 +1640,11 @@ class InfinityOutPainting: @prompts( name="Extend An Image", - description= - ("useful when you need to extend an image into a larger image.like: extend" - " the image into a resolution of 2048x1024, extend the image into" - " 2048x1024. The input to this tool should be a comma separated string of" - " two, representing the image_path and the resolution of widthxheight" + description=( + "useful when you need to extend an image into a larger image.like: extend" + " the image into a resolution of 2048x1024, extend the image into" + " 2048x1024. The input to this tool should be a comma separated string of" + " two, representing the image_path and the resolution of widthxheight" ), ) def inference(self, inputs): @@ -1617,12 +1654,12 @@ class InfinityOutPainting: image = Image.open(image_path) image = ImageOps.crop(image, (10, 10, 10, 10)) out_painted_image = self.dowhile(image, tosize, 4, True, False) - updated_image_path = get_new_image_name(image_path, - func_name="outpainting") + updated_image_path = get_new_image_name(image_path, func_name="outpainting") out_painted_image.save(updated_image_path) print( f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input" - f" Resolution: {resolution}, Output Image: {updated_image_path}") + f" Resolution: {resolution}, Output Image: {updated_image_path}" + ) return updated_image_path @@ -1641,20 +1678,22 @@ class ObjectSegmenting: " pictureaccording to the given textlike: segment the cat,or can you" " segment an obeject for meThe input to this tool should be a comma" " separated string of two, representing the image_path, the text" - " description of the object to be found"), + " description of the object to be found" + ), ) def inference(self, inputs): image_path, det_prompt = inputs.split(",") print(f"image_path={image_path}, text_prompt={det_prompt}") image_pil, image = self.grounding.load_image(image_path) - boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( - image, det_prompt) + boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, det_prompt) updated_image_path = self.sam.segment_image_with_boxes( - image_pil, image_path, boxes_filt, pred_phrases) + image_pil, image_path, boxes_filt, pred_phrases + ) print( f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be" - f" Segment {det_prompt}, Output Image: {updated_image_path}") + f" Segment {det_prompt}, Output Image: {updated_image_path}" + ) return updated_image_path def merge_masks(self, masks): @@ -1685,7 +1724,8 @@ class ObjectSegmenting: image_pil, image = self.grounding.load_image(image_path) boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( - image, text_prompt) + image, text_prompt + ) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam.sam_predictor.set_image(image) @@ -1698,10 +1738,9 @@ class ObjectSegmenting: # draw output image for mask in masks: - image = self.sam.show_mask(mask[0].cpu().numpy(), - image, - random_color=True, - transparency=0.3) + image = self.sam.show_mask( + mask[0].cpu().numpy(), image, random_color=True, transparency=0.3 + ) Image.fromarray(merged_mask) @@ -1711,8 +1750,9 @@ class ObjectSegmenting: class ImageEditing: template_model = True - def __init__(self, Text2Box: Text2Box, Segmenting: Segmenting, - Inpainting: Inpainting): + def __init__( + self, Text2Box: Text2Box, Segmenting: Segmenting, Inpainting: Inpainting + ): print("Initializing ImageEditing") self.sam = Segmenting self.grounding = Text2Box @@ -1725,7 +1765,8 @@ class ImageEditing: mask_array = np.zeros_like(mask, dtype=bool) for idx in true_indices: padded_slice = tuple( - slice(max(0, i - padding), i + padding + 1) for i in idx) + slice(max(0, i - padding), i + padding + 1) for i in idx + ) mask_array[padded_slice] = True new_mask = (mask_array * 255).astype(np.uint8) # new_mask @@ -1733,34 +1774,38 @@ class ImageEditing: @prompts( name="Remove Something From The Photo", - description= - ("useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. "), + description=( + "useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. " + ), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",")[0], ",".join( - inputs.split(",")[1:]) + inputs.split(",")[1:] + ) return self.inference_replace_sam( - f"{image_path},{to_be_removed_txt},background") + f"{image_path},{to_be_removed_txt},background" + ) @prompts( name="Replace Something From The Photo", - description= - ("useful when you want to replace an object from the object description or" - " location with another object from its description. The input to this tool" - " should be a comma separated string of three, representing the image_path," - " the object to be replaced, the object to be replaced with "), + description=( + "useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with " + ), ) def inference_replace_sam(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") - print( - f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}") + print(f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}") image_pil, image = self.grounding.load_image(image_path) boxes_filt, pred_phrases = self.grounding.get_grounding_boxes( - image, to_be_replaced_txt) + image, to_be_replaced_txt + ) image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) self.sam.sam_predictor.set_image(image) @@ -1772,16 +1817,19 @@ class ImageEditing: mask = self.pad_edge(mask, padding=20) # numpy mask_image = Image.fromarray(mask) - updated_image = self.inpaint(prompt=replace_with_txt, - image=image_pil, - mask_image=mask_image) - updated_image_path = get_new_image_name(image_path, - func_name="replace-something") + updated_image = self.inpaint( + prompt=replace_with_txt, image=image_pil, mask_image=mask_image + ) + updated_image_path = get_new_image_name( + image_path, func_name="replace-something" + ) updated_image = updated_image.resize(image_pil.size) updated_image.save(updated_image_path) - print(f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" - f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" - f" {updated_image_path}") + print( + f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" + f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" + f" {updated_image_path}" + ) return updated_image_path @@ -1803,9 +1851,10 @@ class BackgroundRemoving: @prompts( name="Remove the background", - description= - ("useful when you want to extract the object or remove the background," - "the input should be a string image_path"), + description=( + "useful when you want to extract the object or remove the background," + "the input should be a string image_path" + ), ) def inference(self, image_path): """ @@ -1819,8 +1868,9 @@ class BackgroundRemoving: mask = Image.fromarray(mask) image.putalpha(mask) - updated_image_path = get_new_image_name(image_path, - func_name="detect-something") + updated_image_path = get_new_image_name( + image_path, func_name="detect-something" + ) image.save(updated_image_path) return updated_image_path @@ -1843,7 +1893,6 @@ class BackgroundRemoving: class MultiModalVisualAgent: - def __init__( self, load_dict, @@ -1856,7 +1905,8 @@ class MultiModalVisualAgent: if "ImageCaptioning" not in load_dict: raise ValueError( "You have to load ImageCaptioning as a basic function for" - " MultiModalVisualAgent") + " MultiModalVisualAgent" + ) self.models = {} @@ -1866,18 +1916,17 @@ class MultiModalVisualAgent: for class_name, module in globals().items(): if getattr(module, "template_model", False): template_required_names = { - k for k in inspect.signature( - module.__init__).parameters.keys() if k != "self" + k + for k in inspect.signature(module.__init__).parameters.keys() + if k != "self" } - loaded_names = set( - [type(e).__name__ for e in self.models.values()]) + loaded_names = set([type(e).__name__ for e in self.models.values()]) if template_required_names.issubset(loaded_names): - self.models[class_name] = globals()[class_name](**{ - name: self.models[name] - for name in template_required_names - }) + self.models[class_name] = globals()[class_name]( + **{name: self.models[name] for name in template_required_names} + ) print(f"All the Available Functions: {self.models}") @@ -1887,13 +1936,13 @@ class MultiModalVisualAgent: if e.startswith("inference"): func = getattr(instance, e) self.tools.append( - Tool(name=func.name, - description=func.description, - func=func)) + Tool(name=func.name, description=func.description, func=func) + ) self.llm = OpenAI(temperature=0) - self.memory = ConversationBufferMemory(memory_key="chat_history", - output_key="output") + self.memory = ConversationBufferMemory( + memory_key="chat_history", output_key="output" + ) def init_agent(self, lang): self.memory.clear() @@ -1931,7 +1980,8 @@ class MultiModalVisualAgent: def run_text(self, text): self.agent.memory.buffer = cut_dialogue_history( - self.agent.memory.buffer, keep_last_n_words=500) + self.agent.memory.buffer, keep_last_n_words=500 + ) res = self.agent({"input": text.strip()}) res["output"] = res["output"].replace("\\", "/") @@ -1941,8 +1991,10 @@ class MultiModalVisualAgent: res["output"], ) - print(f"\nProcessed run_text, Input text: {text}\n" - f"Current Memory: {self.agent.memory.buffer}") + print( + f"\nProcessed run_text, Input text: {text}\n" + f"Current Memory: {self.agent.memory.buffer}" + ) return response @@ -1964,10 +2016,12 @@ class MultiModalVisualAgent: description = self.models["ImageCaptioning"].inference(image_filename) if lang == "Chinese": - Human_prompt = (f"\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ:" - f" {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒ" - "ไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚" - ' ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n') + Human_prompt = ( + f"\nHuman: ๆไพ›ไธ€ๅผ ๅไธบ {image_filename}็š„ๅ›พ็‰‡ใ€‚ๅฎƒ็š„ๆ่ฟฐๆ˜ฏ:" + f" {description}ใ€‚ ่ฟ™ไบ›ไฟกๆฏๅธฎๅŠฉไฝ ็†่งฃ่ฟ™ไธชๅ›พๅƒ๏ผŒ" + "ไฝ†ๆ˜ฏไฝ ๅบ”่ฏฅไฝฟ็”จๅทฅๅ…ทๆฅๅฎŒๆˆไธ‹้ข็š„ไปปๅŠก๏ผŒ่€Œไธๆ˜ฏ็›ดๆŽฅไปŽๆˆ‘็š„ๆ่ฟฐไธญๆƒณ่ฑกใ€‚" + ' ๅฆ‚ๆžœไฝ ๆ˜Ž็™ฝไบ†, ่ฏด "ๆ”ถๅˆฐ". \n' + ) AI_prompt = "ๆ”ถๅˆฐใ€‚ " else: Human_prompt = ( @@ -1975,14 +2029,18 @@ class MultiModalVisualAgent: f" {description}. This information helps you to understand this image," " but you should use tools to finish following tasks, rather than" " directly imagine from my description. If you understand, say" - ' "Received". \n') + ' "Received". \n' + ) AI_prompt = "Received. " - self.agent.memory.buffer = (self.agent.memory.buffer + Human_prompt + - "AI: " + AI_prompt) + self.agent.memory.buffer = ( + self.agent.memory.buffer + Human_prompt + "AI: " + AI_prompt + ) - print(f"\nProcessed run_image, Input image: {image_filename}\n" - f"Current Memory: {self.agent.memory.buffer}") + print( + f"\nProcessed run_image, Input image: {image_filename}\n" + f"Current Memory: {self.agent.memory.buffer}" + ) return AI_prompt @@ -2029,10 +2087,7 @@ class MultiModalAgent: """ - def __init__(self, - load_dict, - temperature: int = 0.1, - language: str = "english"): + def __init__(self, load_dict, temperature: int = 0.1, language: str = "english"): self.load_dict = load_dict self.temperature = temperature self.langigage = language @@ -2068,10 +2123,7 @@ class MultiModalAgent: except Exception as error: return f"Error processing image: {str(error)}" - def chat(self, - msg: str = None, - language: str = "english", - streaming: bool = False): + def chat(self, msg: str = None, language: str = "english", streaming: bool = False): """ Run chat with the multi-modal agent diff --git a/swarms/agents/neural_architecture_search_worker.py b/swarms/agents/neural_architecture_search_worker.py index 3bfd8323..fd253b95 100644 --- a/swarms/agents/neural_architecture_search_worker.py +++ b/swarms/agents/neural_architecture_search_worker.py @@ -2,7 +2,6 @@ class Replicator: - def __init__( self, model_name, diff --git a/swarms/agents/omni_modal_agent.py b/swarms/agents/omni_modal_agent.py index b6fdfbdc..007a2219 100644 --- a/swarms/agents/omni_modal_agent.py +++ b/swarms/agents/omni_modal_agent.py @@ -3,20 +3,23 @@ from typing import Dict, List from langchain.base_language import BaseLanguageModel from langchain.tools.base import BaseTool from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import ( - load_response_generator,) + load_response_generator, +) from langchain_experimental.autonomous_agents.hugginggpt.task_executor import ( - TaskExecutor,) + TaskExecutor, +) from langchain_experimental.autonomous_agents.hugginggpt.task_planner import ( - load_chat_planner,) + load_chat_planner, +) from transformers import load_tool from swarms.agents.message import Message class Step: - - def __init__(self, task: str, id: int, dep: List[int], args: Dict[str, str], - tool: BaseTool): + def __init__( + self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool + ): self.task = task self.id = id self.dep = dep @@ -25,7 +28,6 @@ class Step: class Plan: - def __init__(self, steps: List[Step]): self.steps = steps @@ -71,7 +73,8 @@ class OmniModalAgent: print("Loading tools...") self.tools = [ - load_tool(tool_name) for tool_name in [ + load_tool(tool_name) + for tool_name in [ "document-question-answering", "image-captioning", "image-question-answering", @@ -96,15 +99,18 @@ class OmniModalAgent: def run(self, input: str) -> str: """Run the OmniAgent""" - plan = self.chat_planner.plan(inputs={ - "input": input, - "hf_tools": self.tools, - }) + plan = self.chat_planner.plan( + inputs={ + "input": input, + "hf_tools": self.tools, + } + ) self.task_executor = TaskExecutor(plan) self.task_executor.run() response = self.response_generator.generate( - {"task_execution": self.task_executor}) + {"task_execution": self.task_executor} + ) return response diff --git a/swarms/agents/profitpilot.py b/swarms/agents/profitpilot.py index a4ff13a5..6858dc72 100644 --- a/swarms/agents/profitpilot.py +++ b/swarms/agents/profitpilot.py @@ -145,12 +145,13 @@ def setup_knowledge_base(product_catalog: str = None): llm = OpenAI(temperature=0) embeddings = OpenAIEmbeddings() - docsearch = Chroma.from_texts(texts, - embeddings, - collection_name="product-knowledge-base") + docsearch = Chroma.from_texts( + texts, embeddings, collection_name="product-knowledge-base" + ) knowledge_base = RetrievalQA.from_chain_type( - llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()) + llm=llm, chain_type="stuff", retriever=docsearch.as_retriever() + ) return knowledge_base @@ -162,8 +163,8 @@ def get_tools(product_catalog): Tool( name="ProductSearch", func=knowledge_base.run, - description= - ("useful for when you need to answer questions about product information" + description=( + "useful for when you need to answer questions about product information" ), ), # omnimodal agent @@ -193,7 +194,8 @@ class CustomPromptTemplateForTools(StringPromptTemplate): tools = self.tools_getter(kwargs["input"]) # Create a tools variable from the list of tools provided kwargs["tools"] = "\n".join( - [f"{tool.name}: {tool.description}" for tool in tools]) + [f"{tool.name}: {tool.description}" for tool in tools] + ) # Create a list of tool names for the tools provided kwargs["tool_names"] = ", ".join([tool.name for tool in tools]) return self.template.format(**kwargs) @@ -216,7 +218,8 @@ class SalesConvoOutputParser(AgentOutputParser): print("-------") if f"{self.ai_prefix}:" in text: return AgentFinish( - {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text) + {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text + ) regex = r"Action: (.*?)[\n]*Action Input: (.*)" match = re.search(regex, text) if not match: @@ -225,15 +228,15 @@ class SalesConvoOutputParser(AgentOutputParser): { "output": ( "I apologize, I was unable to find the answer to your question." - " Is there anything else I can help with?") + " Is there anything else I can help with?" + ) }, text, ) # raise OutputParserException(f"Could not parse LLM output: `{text}`") action = match.group(1) action_input = match.group(2) - return AgentAction(action.strip(), - action_input.strip(" ").strip('"'), text) + return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text) @property def _type(self) -> str: @@ -261,11 +264,13 @@ class ProfitPilot(Chain, BaseModel): "2": ( "Qualification: Qualify the prospect by confirming if they are the right" " person to talk to regarding your product/service. Ensure that they have" - " the authority to make purchasing decisions."), + " the authority to make purchasing decisions." + ), "3": ( "Value proposition: Briefly explain how your product/service can benefit" " the prospect. Focus on the unique selling points and value proposition of" - " your product/service that sets it apart from competitors."), + " your product/service that sets it apart from competitors." + ), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs" " and pain points. Listen carefully to their responses and take notes." @@ -277,11 +282,13 @@ class ProfitPilot(Chain, BaseModel): "6": ( "Objection handling: Address any objections that the prospect may have" " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims."), + " testimonials to support your claims." + ), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has" - " been discussed and reiterate the benefits."), + " been discussed and reiterate the benefits." + ), } salesperson_name: str = "Ted Lasso" @@ -291,16 +298,19 @@ class ProfitPilot(Chain, BaseModel): "Sleep Haven is a premium mattress company that provides customers with the" " most comfortable and supportive sleeping experience possible. We offer a" " range of high-quality mattresses, pillows, and bedding accessories that are" - " designed to meet the unique needs of our customers.") + " designed to meet the unique needs of our customers." + ) company_values: str = ( "Our mission at Sleep Haven is to help people achieve a better night's sleep by" " providing them with the best possible sleep solutions. We believe that" " quality sleep is essential to overall health and well-being, and we are" " committed to helping our customers achieve optimal sleep by offering" - " exceptional products and customer service.") + " exceptional products and customer service." + ) conversation_purpose: str = ( "find out whether they are looking to achieve better sleep via buying a premier" - " mattress.") + " mattress." + ) conversation_type: str = "call" def retrieve_conversation_stage(self, key): @@ -326,7 +336,8 @@ class ProfitPilot(Chain, BaseModel): ) self.current_conversation_stage = self.retrieve_conversation_stage( - conversation_stage_id) + conversation_stage_id + ) print(f"Conversation Stage: {self.current_conversation_stage}") @@ -380,15 +391,13 @@ class ProfitPilot(Chain, BaseModel): return {} @classmethod - def from_llm(cls, - llm: BaseLLM, - verbose: bool = False, - **kwargs): # noqa: F821 + def from_llm(cls, llm: BaseLLM, verbose: bool = False, **kwargs): # noqa: F821 """Initialize the SalesGPT Controller.""" stage_analyzer_chain = StageAnalyzerChain.from_llm(llm, verbose=verbose) sales_conversation_utterance_chain = SalesConversationChain.from_llm( - llm, verbose=verbose) + llm, verbose=verbose + ) if "use_tools" in kwargs.keys() and kwargs["use_tools"] is False: sales_agent_executor = None @@ -421,8 +430,7 @@ class ProfitPilot(Chain, BaseModel): # WARNING: this output parser is NOT reliable yet # It makes assumptions about output from LLM which can break and throw an error - output_parser = SalesConvoOutputParser( - ai_prefix=kwargs["salesperson_name"]) + output_parser = SalesConvoOutputParser(ai_prefix=kwargs["salesperson_name"]) sales_agent_with_tools = LLMSingleActionAgent( llm_chain=llm_chain, @@ -433,12 +441,12 @@ class ProfitPilot(Chain, BaseModel): ) sales_agent_executor = AgentExecutor.from_agent_and_tools( - agent=sales_agent_with_tools, tools=tools, verbose=verbose) + agent=sales_agent_with_tools, tools=tools, verbose=verbose + ) return cls( stage_analyzer_chain=stage_analyzer_chain, - sales_conversation_utterance_chain= - sales_conversation_utterance_chain, + sales_conversation_utterance_chain=sales_conversation_utterance_chain, sales_agent_executor=sales_agent_executor, verbose=verbose, **kwargs, @@ -450,27 +458,32 @@ config = dict( salesperson_name="Ted Lasso", salesperson_role="Business Development Representative", company_name="Sleep Haven", - company_business= - ("Sleep Haven is a premium mattress company that provides customers with the" - " most comfortable and supportive sleeping experience possible. We offer a" - " range of high-quality mattresses, pillows, and bedding accessories that are" - " designed to meet the unique needs of our customers."), - company_values= - ("Our mission at Sleep Haven is to help people achieve a better night's sleep by" - " providing them with the best possible sleep solutions. We believe that" - " quality sleep is essential to overall health and well-being, and we are" - " committed to helping our customers achieve optimal sleep by offering" - " exceptional products and customer service."), - conversation_purpose= - ("find out whether they are looking to achieve better sleep via buying a premier" - " mattress."), + company_business=( + "Sleep Haven is a premium mattress company that provides customers with the" + " most comfortable and supportive sleeping experience possible. We offer a" + " range of high-quality mattresses, pillows, and bedding accessories that are" + " designed to meet the unique needs of our customers." + ), + company_values=( + "Our mission at Sleep Haven is to help people achieve a better night's sleep by" + " providing them with the best possible sleep solutions. We believe that" + " quality sleep is essential to overall health and well-being, and we are" + " committed to helping our customers achieve optimal sleep by offering" + " exceptional products and customer service." + ), + conversation_purpose=( + "find out whether they are looking to achieve better sleep via buying a premier" + " mattress." + ), conversation_history=[], conversation_type="call", conversation_stage=conversation_stages.get( "1", - ("Introduction: Start the conversation by introducing yourself and your" - " company. Be polite and respectful while keeping the tone of the" - " conversation professional."), + ( + "Introduction: Start the conversation by introducing yourself and your" + " company. Be polite and respectful while keeping the tone of the" + " conversation professional." + ), ), use_tools=True, product_catalog="sample_product_catalog.txt", diff --git a/swarms/agents/refiner_agent.py b/swarms/agents/refiner_agent.py index 509484e3..2a1383e9 100644 --- a/swarms/agents/refiner_agent.py +++ b/swarms/agents/refiner_agent.py @@ -1,11 +1,9 @@ class PromptRefiner: - def __init__(self, system_prompt: str, llm): super().__init__() self.system_prompt = system_prompt self.llm = llm def run(self, task: str): - refine = self.llm( - f"System Prompt: {self.system_prompt} Current task: {task}") + refine = self.llm(f"System Prompt: {self.system_prompt} Current task: {task}") return refine diff --git a/swarms/agents/registry.py b/swarms/agents/registry.py index 5cf2c0d5..aa1f1375 100644 --- a/swarms/agents/registry.py +++ b/swarms/agents/registry.py @@ -10,7 +10,6 @@ class Registry(BaseModel): entries: Dict = {} def register(self, key: str): - def decorator(class_builder): self.entries[key] = class_builder return class_builder @@ -21,7 +20,8 @@ class Registry(BaseModel): if type not in self.entries: raise ValueError( f"{type} is not registered. Please register with the" - f' .register("{type}") method provided in {self.name} registry') + f' .register("{type}") method provided in {self.name} registry' + ) return self.entries[type](**kwargs) def get_all_entries(self): diff --git a/swarms/agents/simple_agent.py b/swarms/agents/simple_agent.py index 847cbc67..88327095 100644 --- a/swarms/agents/simple_agent.py +++ b/swarms/agents/simple_agent.py @@ -29,8 +29,7 @@ class SimpleAgent: def run(self, task: str) -> str: """Run method""" - metrics = print( - colored(f"Agent {self.name} is running task: {task}", "red")) + metrics = print(colored(f"Agent {self.name} is running task: {task}", "red")) print(metrics) response = self.flow.run(task) diff --git a/swarms/artifacts/base.py b/swarms/artifacts/base.py index 1357a86b..dac7a523 100644 --- a/swarms/artifacts/base.py +++ b/swarms/artifacts/base.py @@ -10,8 +10,9 @@ from marshmallow.exceptions import RegistryError @define class BaseArtifact(ABC): id: str = field(default=Factory(lambda: uuid.uuid4().hex), kw_only=True) - name: str = field(default=Factory(lambda self: self.id, takes_self=True), - kw_only=True) + name: str = field( + default=Factory(lambda self: self.id, takes_self=True), kw_only=True + ) value: any = field() type: str = field( default=Factory(lambda self: self.__class__.__name__, takes_self=True), @@ -53,8 +54,7 @@ class BaseArtifact(ABC): class_registry.register("ListArtifact", ListArtifactSchema) try: - return class_registry.get_class( - artifact_dict["type"])().load(artifact_dict) + return class_registry.get_class(artifact_dict["type"])().load(artifact_dict) except RegistryError: raise ValueError("Unsupported artifact type") diff --git a/swarms/artifacts/main.py b/swarms/artifacts/main.py index 8845ada3..4b240b22 100644 --- a/swarms/artifacts/main.py +++ b/swarms/artifacts/main.py @@ -15,7 +15,8 @@ class Artifact(BaseModel): artifact_id: StrictStr = Field(..., description="ID of the artifact") file_name: StrictStr = Field(..., description="Filename of the artifact") relative_path: Optional[StrictStr] = Field( - None, description="Relative path of the artifact") + None, description="Relative path of the artifact" + ) __properties = ["artifact_id", "file_name", "relative_path"] class Config: @@ -48,10 +49,12 @@ class Artifact(BaseModel): if not isinstance(obj, dict): return Artifact.parse_obj(obj) - _obj = Artifact.parse_obj({ - "artifact_id": obj.get("artifact_id"), - "file_name": obj.get("file_name"), - "relative_path": obj.get("relative_path"), - }) + _obj = Artifact.parse_obj( + { + "artifact_id": obj.get("artifact_id"), + "file_name": obj.get("file_name"), + "relative_path": obj.get("relative_path"), + } + ) return _obj diff --git a/swarms/chunkers/base.py b/swarms/chunkers/base.py index d243bd0d..0fabdcef 100644 --- a/swarms/chunkers/base.py +++ b/swarms/chunkers/base.py @@ -48,13 +48,15 @@ class BaseChunker(ABC): kw_only=True, ) tokenizer: OpenAITokenizer = field( - default=Factory(lambda: OpenAITokenizer( - model=OpenAITokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL)), + default=Factory( + lambda: OpenAITokenizer( + model=OpenAITokenizer.DEFAULT_OPENAI_GPT_3_CHAT_MODEL + ) + ), kw_only=True, ) max_tokens: int = field( - default=Factory(lambda self: self.tokenizer.max_tokens, - takes_self=True), + default=Factory(lambda self: self.tokenizer.max_tokens, takes_self=True), kw_only=True, ) @@ -64,9 +66,8 @@ class BaseChunker(ABC): return [TextArtifact(c) for c in self._chunk_recursively(text)] def _chunk_recursively( - self, - chunk: str, - current_separator: Optional[ChunkSeparator] = None) -> list[str]: + self, chunk: str, current_separator: Optional[ChunkSeparator] = None + ) -> list[str]: token_count = self.tokenizer.count_tokens(chunk) if token_count <= self.max_tokens: @@ -78,8 +79,7 @@ class BaseChunker(ABC): half_token_count = token_count // 2 if current_separator: - separators = self.separators[self.separators. - index(current_separator):] + separators = self.separators[self.separators.index(current_separator) :] else: separators = self.separators @@ -102,19 +102,26 @@ class BaseChunker(ABC): if separator.is_prefix: first_subchunk = separator.value + separator.value.join( - subchanks[:balance_index + 1]) + subchanks[: balance_index + 1] + ) second_subchunk = separator.value + separator.value.join( - subchanks[balance_index + 1:]) + subchanks[balance_index + 1 :] + ) else: - first_subchunk = (separator.value.join( - subchanks[:balance_index + 1]) + separator.value) + first_subchunk = ( + separator.value.join(subchanks[: balance_index + 1]) + + separator.value + ) second_subchunk = separator.value.join( - subchanks[balance_index + 1:]) + subchanks[balance_index + 1 :] + ) first_subchunk_rec = self._chunk_recursively( - first_subchunk.strip(), separator) + first_subchunk.strip(), separator + ) second_subchunk_rec = self._chunk_recursively( - second_subchunk.strip(), separator) + second_subchunk.strip(), separator + ) if first_subchunk_rec and second_subchunk_rec: return first_subchunk_rec + second_subchunk_rec diff --git a/swarms/chunkers/omni_chunker.py b/swarms/chunkers/omni_chunker.py index c4870e2b..70a11380 100644 --- a/swarms/chunkers/omni_chunker.py +++ b/swarms/chunkers/omni_chunker.py @@ -76,7 +76,8 @@ class OmniChunker: colored( f"Could not decode file with extension {file_extension}: {e}", "yellow", - )) + ) + ) return "" def chunk_content(self, content: str) -> List[str]: @@ -90,7 +91,7 @@ class OmniChunker: List[str]: The list of chunks. """ return [ - content[i:i + self.chunk_size] + content[i : i + self.chunk_size] for i in range(0, len(content), self.chunk_size) ] @@ -112,4 +113,5 @@ class OmniChunker: {self.metrics()} """, "cyan", - )) + ) + ) diff --git a/swarms/loaders/asana.py b/swarms/loaders/asana.py index 022b685b..dd14cff4 100644 --- a/swarms/loaders/asana.py +++ b/swarms/loaders/asana.py @@ -18,9 +18,9 @@ class AsanaReader(BaseReader): self.client = asana.Client.access_token(asana_token) - def load_data(self, - workspace_id: Optional[str] = None, - project_id: Optional[str] = None) -> List[Document]: + def load_data( + self, workspace_id: Optional[str] = None, project_id: Optional[str] = None + ) -> List[Document]: """Load data from the workspace. Args: @@ -31,20 +31,18 @@ class AsanaReader(BaseReader): """ if workspace_id is None and project_id is None: - raise ValueError( - "Either workspace_id or project_id must be provided") + raise ValueError("Either workspace_id or project_id must be provided") if workspace_id is not None and project_id is not None: raise ValueError( - "Only one of workspace_id or project_id should be provided") + "Only one of workspace_id or project_id should be provided" + ) results = [] if workspace_id is not None: - workspace_name = self.client.workspaces.find_by_id( - workspace_id)["name"] - projects = self.client.projects.find_all( - {"workspace": workspace_id}) + workspace_name = self.client.workspaces.find_by_id(workspace_id)["name"] + projects = self.client.projects.find_all({"workspace": workspace_id}) # Case: Only project_id is provided else: # since we've handled the other cases, this means project_id is not None @@ -52,58 +50,54 @@ class AsanaReader(BaseReader): workspace_name = projects[0]["workspace"]["name"] for project in projects: - tasks = self.client.tasks.find_all({ - "project": - project["gid"], - "opt_fields": - "name,notes,completed,completed_at,completed_by,assignee,followers,custom_fields", - }) + tasks = self.client.tasks.find_all( + { + "project": project["gid"], + "opt_fields": "name,notes,completed,completed_at,completed_by,assignee,followers,custom_fields", + } + ) for task in tasks: - stories = self.client.tasks.stories(task["gid"], - opt_fields="type,text") - comments = "\n".join([ - story["text"] - for story in stories - if story.get("type") == "comment" and "text" in story - ]) + stories = self.client.tasks.stories(task["gid"], opt_fields="type,text") + comments = "\n".join( + [ + story["text"] + for story in stories + if story.get("type") == "comment" and "text" in story + ] + ) task_metadata = { - "task_id": - task.get("gid", ""), - "name": - task.get("name", ""), + "task_id": task.get("gid", ""), + "name": task.get("name", ""), "assignee": (task.get("assignee") or {}).get("name", ""), - "completed_on": - task.get("completed_at", ""), - "completed_by": (task.get("completed_by") or - {}).get("name", ""), - "project_name": - project.get("name", ""), + "completed_on": task.get("completed_at", ""), + "completed_by": (task.get("completed_by") or {}).get("name", ""), + "project_name": project.get("name", ""), "custom_fields": [ i["display_value"] for i in task.get("custom_fields") if task.get("custom_fields") is not None ], - "workspace_name": - workspace_name, - "url": - f"https://app.asana.com/0/{project['gid']}/{task['gid']}", + "workspace_name": workspace_name, + "url": f"https://app.asana.com/0/{project['gid']}/{task['gid']}", } if task.get("followers") is not None: task_metadata["followers"] = [ - i.get("name") - for i in task.get("followers") - if "name" in i + i.get("name") for i in task.get("followers") if "name" in i ] else: task_metadata["followers"] = [] results.append( Document( - text=task.get("name", "") + " " + - task.get("notes", "") + " " + comments, + text=task.get("name", "") + + " " + + task.get("notes", "") + + " " + + comments, extra_info=task_metadata, - )) + ) + ) return results diff --git a/swarms/loaders/base.py b/swarms/loaders/base.py index 2d5c7cdb..afeeb231 100644 --- a/swarms/loaders/base.py +++ b/swarms/loaders/base.py @@ -47,8 +47,7 @@ class BaseComponent(BaseModel): # TODO: return type here not supported by current mypy version @classmethod - def from_dict(cls, data: Dict[str, Any], - **kwargs: Any) -> Self: # type: ignore + def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self: # type: ignore if isinstance(kwargs, dict): data.update(kwargs) @@ -119,10 +118,12 @@ class BaseNode(BaseComponent): class Config: allow_population_by_field_name = True - id_: str = Field(default_factory=lambda: str(uuid.uuid4()), - description="Unique ID of the node.") + id_: str = Field( + default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node." + ) embedding: Optional[List[float]] = Field( - default=None, description="Embedding of the node.") + default=None, description="Embedding of the node." + ) """" metadata fields - injected as part of the text shown to LLMs as context @@ -137,8 +138,7 @@ class BaseNode(BaseComponent): ) excluded_embed_metadata_keys: List[str] = Field( default_factory=list, - description= - "Metadata keys that are excluded from text for the embed model.", + description="Metadata keys that are excluded from text for the embed model.", ) excluded_llm_metadata_keys: List[str] = Field( default_factory=list, @@ -156,8 +156,7 @@ class BaseNode(BaseComponent): """Get Object type.""" @abstractmethod - def get_content(self, - metadata_mode: MetadataMode = MetadataMode.ALL) -> str: + def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str: """Get object content.""" @abstractmethod @@ -188,8 +187,7 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.SOURCE] if isinstance(relation, list): - raise ValueError( - "Source object must be a single RelatedNodeInfo object") + raise ValueError("Source object must be a single RelatedNodeInfo object") return relation @property @@ -200,8 +198,7 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.PREVIOUS] if not isinstance(relation, RelatedNodeInfo): - raise ValueError( - "Previous object must be a single RelatedNodeInfo object") + raise ValueError("Previous object must be a single RelatedNodeInfo object") return relation @property @@ -212,8 +209,7 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.NEXT] if not isinstance(relation, RelatedNodeInfo): - raise ValueError( - "Next object must be a single RelatedNodeInfo object") + raise ValueError("Next object must be a single RelatedNodeInfo object") return relation @property @@ -224,8 +220,7 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.PARENT] if not isinstance(relation, RelatedNodeInfo): - raise ValueError( - "Parent object must be a single RelatedNodeInfo object") + raise ValueError("Parent object must be a single RelatedNodeInfo object") return relation @property @@ -236,8 +231,7 @@ class BaseNode(BaseComponent): relation = self.relationships[NodeRelationship.CHILD] if not isinstance(relation, list): - raise ValueError( - "Child objects must be a list of RelatedNodeInfo objects.") + raise ValueError("Child objects must be a list of RelatedNodeInfo objects.") return relation @property @@ -254,10 +248,12 @@ class BaseNode(BaseComponent): return self.metadata def __str__(self) -> str: - source_text_truncated = truncate_text(self.get_content().strip(), - TRUNCATE_LENGTH) - source_text_wrapped = textwrap.fill(f"Text: {source_text_truncated}\n", - width=WRAP_WIDTH) + source_text_truncated = truncate_text( + self.get_content().strip(), TRUNCATE_LENGTH + ) + source_text_wrapped = textwrap.fill( + f"Text: {source_text_truncated}\n", width=WRAP_WIDTH + ) return f"Node ID: {self.node_id}\n{source_text_wrapped}" def get_embedding(self) -> List[float]: @@ -283,23 +279,28 @@ class BaseNode(BaseComponent): class TextNode(BaseNode): text: str = Field(default="", description="Text content of the node.") start_char_idx: Optional[int] = Field( - default=None, description="Start char index of the node.") + default=None, description="Start char index of the node." + ) end_char_idx: Optional[int] = Field( - default=None, description="End char index of the node.") + default=None, description="End char index of the node." + ) text_template: str = Field( default=DEFAULT_TEXT_NODE_TMPL, - description=("Template for how text is formatted, with {content} and " - "{metadata_str} placeholders."), + description=( + "Template for how text is formatted, with {content} and " + "{metadata_str} placeholders." + ), ) metadata_template: str = Field( default=DEFAULT_METADATA_TMPL, - description=("Template for how metadata is formatted, with {key} and " - "{value} placeholders."), + description=( + "Template for how metadata is formatted, with {key} and " + "{value} placeholders." + ), ) metadata_seperator: str = Field( default="\n", - description= - "Separator between metadata fields when converting to string.", + description="Separator between metadata fields when converting to string.", ) @classmethod @@ -313,7 +314,8 @@ class TextNode(BaseNode): metadata = values.get("metadata", {}) doc_identity = str(text) + str(metadata) values["hash"] = str( - sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest()) + sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest() + ) return values @classmethod @@ -321,15 +323,15 @@ class TextNode(BaseNode): """Get Object type.""" return ObjectType.TEXT - def get_content(self, - metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: """Get object content.""" metadata_str = self.get_metadata_str(mode=metadata_mode).strip() if not metadata_str: return self.text - return self.text_template.format(content=self.text, - metadata_str=metadata_str).strip() + return self.text_template.format( + content=self.text, metadata_str=metadata_str + ).strip() def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str: """Metadata info string.""" @@ -346,11 +348,13 @@ class TextNode(BaseNode): if key in usable_metadata_keys: usable_metadata_keys.remove(key) - return self.metadata_seperator.join([ - self.metadata_template.format(key=key, value=str(value)) - for key, value in self.metadata.items() - if key in usable_metadata_keys - ]) + return self.metadata_seperator.join( + [ + self.metadata_template.format(key=key, value=str(value)) + for key, value in self.metadata.items() + if key in usable_metadata_keys + ] + ) def set_content(self, value: str) -> None: """Set the content of the node.""" @@ -474,8 +478,7 @@ class NodeWithScore(BaseComponent): else: raise ValueError("Node must be a TextNode to get text.") - def get_content(self, - metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: return self.node.get_content(metadata_mode=metadata_mode) def get_embedding(self) -> List[float]: @@ -512,10 +515,12 @@ class Document(TextNode): return self.id_ def __str__(self) -> str: - source_text_truncated = truncate_text(self.get_content().strip(), - TRUNCATE_LENGTH) - source_text_wrapped = textwrap.fill(f"Text: {source_text_truncated}\n", - width=WRAP_WIDTH) + source_text_truncated = truncate_text( + self.get_content().strip(), TRUNCATE_LENGTH + ) + source_text_wrapped = textwrap.fill( + f"Text: {source_text_truncated}\n", width=WRAP_WIDTH + ) return f"Doc ID: {self.doc_id}\n{source_text_wrapped}" def get_doc_id(self) -> str: @@ -531,27 +536,22 @@ class Document(TextNode): """Convert struct to Haystack document format.""" from haystack.schema import Document as HaystackDocument - return HaystackDocument(content=self.text, - meta=self.metadata, - embedding=self.embedding, - id=self.id_) + return HaystackDocument( + content=self.text, meta=self.metadata, embedding=self.embedding, id=self.id_ + ) @classmethod def from_haystack_format(cls, doc: "HaystackDocument") -> "Document": """Convert struct from Haystack document format.""" - return cls(text=doc.content, - metadata=doc.meta, - embedding=doc.embedding, - id_=doc.id) + return cls( + text=doc.content, metadata=doc.meta, embedding=doc.embedding, id_=doc.id + ) def to_embedchain_format(self) -> Dict[str, Any]: """Convert struct to EmbedChain document format.""" return { "doc_id": self.id_, - "data": { - "content": self.text, - "meta_data": self.metadata - }, + "data": {"content": self.text, "meta_data": self.metadata}, } @classmethod @@ -581,8 +581,7 @@ class Document(TextNode): return cls( text=doc._text, metadata={"additional_metadata": doc._additional_metadata}, - embedding=doc._embedding.tolist() - if doc._embedding is not None else None, + embedding=doc._embedding.tolist() if doc._embedding is not None else None, id_=doc._id, ) @@ -590,10 +589,7 @@ class Document(TextNode): def example(cls) -> "Document": return Document( text=SAMPLE_TEXT, - metadata={ - "filename": "README.md", - "category": "codebase" - }, + metadata={"filename": "README.md", "category": "codebase"}, ) @classmethod diff --git a/swarms/memory/base.py b/swarms/memory/base.py index 7c08af6f..7f71c4b9 100644 --- a/swarms/memory/base.py +++ b/swarms/memory/base.py @@ -30,25 +30,32 @@ class BaseVectorStore(ABC): embedding_driver: Any futures_executor: futures.Executor = field( - default=Factory(lambda: futures.ThreadPoolExecutor()), kw_only=True) - - def upsert_text_artifacts(self, - artifacts: dict[str, list[TextArtifact]], - meta: Optional[dict] = None, - **kwargs) -> None: - execute_futures_dict({ - namespace: - self.futures_executor.submit(self.upsert_text_artifact, a, - namespace, meta, **kwargs) - for namespace, artifact_list in artifacts.items() - for a in artifact_list - }) - - def upsert_text_artifact(self, - artifact: TextArtifact, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs) -> str: + default=Factory(lambda: futures.ThreadPoolExecutor()), kw_only=True + ) + + def upsert_text_artifacts( + self, + artifacts: dict[str, list[TextArtifact]], + meta: Optional[dict] = None, + **kwargs + ) -> None: + execute_futures_dict( + { + namespace: self.futures_executor.submit( + self.upsert_text_artifact, a, namespace, meta, **kwargs + ) + for namespace, artifact_list in artifacts.items() + for a in artifact_list + } + ) + + def upsert_text_artifact( + self, + artifact: TextArtifact, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs + ) -> str: if not meta: meta = {} @@ -59,37 +66,39 @@ class BaseVectorStore(ABC): else: vector = artifact.generate_embedding(self.embedding_driver) - return self.upsert_vector(vector, - vector_id=artifact.id, - namespace=namespace, - meta=meta, - **kwargs) - - def upsert_text(self, - string: str, - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs) -> str: - return self.upsert_vector(self.embedding_driver.embed_string(string), - vector_id=vector_id, - namespace=namespace, - meta=meta if meta else {}, - **kwargs) + return self.upsert_vector( + vector, vector_id=artifact.id, namespace=namespace, meta=meta, **kwargs + ) + + def upsert_text( + self, + string: str, + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs + ) -> str: + return self.upsert_vector( + self.embedding_driver.embed_string(string), + vector_id=vector_id, + namespace=namespace, + meta=meta if meta else {}, + **kwargs + ) @abstractmethod - def upsert_vector(self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs) -> str: + def upsert_vector( + self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs + ) -> str: ... @abstractmethod - def load_entry(self, - vector_id: str, - namespace: Optional[str] = None) -> Entry: + def load_entry(self, vector_id: str, namespace: Optional[str] = None) -> Entry: ... @abstractmethod @@ -97,10 +106,12 @@ class BaseVectorStore(ABC): ... @abstractmethod - def query(self, - query: str, - count: Optional[int] = None, - namespace: Optional[str] = None, - include_vectors: bool = False, - **kwargs) -> list[QueryResult]: + def query( + self, + query: str, + count: Optional[int] = None, + namespace: Optional[str] = None, + include_vectors: bool = False, + **kwargs + ) -> list[QueryResult]: ... diff --git a/swarms/memory/chroma.py b/swarms/memory/chroma.py index 080245fb..67ba4cb2 100644 --- a/swarms/memory/chroma.py +++ b/swarms/memory/chroma.py @@ -80,8 +80,10 @@ class Chroma(VectorStore): import chromadb import chromadb.config except ImportError: - raise ImportError("Could not import chromadb python package. " - "Please install it with `pip install chromadb`.") + raise ImportError( + "Could not import chromadb python package. " + "Please install it with `pip install chromadb`." + ) if client is not None: self._client_settings = client_settings @@ -92,7 +94,8 @@ class Chroma(VectorStore): # If client_settings is provided with persist_directory specified, # then it is "in-memory and persisting to disk" mode. client_settings.persist_directory = ( - persist_directory or client_settings.persist_directory) + persist_directory or client_settings.persist_directory + ) if client_settings.persist_directory is not None: # Maintain backwards compatibility with chromadb < 0.4.0 major, minor, _ = chromadb.__version__.split(".") @@ -105,23 +108,25 @@ class Chroma(VectorStore): major, minor, _ = chromadb.__version__.split(".") if int(major) == 0 and int(minor) < 4: _client_settings = chromadb.config.Settings( - chroma_db_impl="duckdb+parquet",) + chroma_db_impl="duckdb+parquet", + ) else: - _client_settings = chromadb.config.Settings( - is_persistent=True) + _client_settings = chromadb.config.Settings(is_persistent=True) _client_settings.persist_directory = persist_directory else: _client_settings = chromadb.config.Settings() self._client_settings = _client_settings self._client = chromadb.Client(_client_settings) - self._persist_directory = (_client_settings.persist_directory or - persist_directory) + self._persist_directory = ( + _client_settings.persist_directory or persist_directory + ) self._embedding_function = embedding_function self._collection = self._client.get_or_create_collection( name=collection_name, embedding_function=self._embedding_function.embed_documents - if self._embedding_function is not None else None, + if self._embedding_function is not None + else None, metadata=collection_metadata, ) self.override_relevance_score_fn = relevance_score_fn @@ -144,8 +149,10 @@ class Chroma(VectorStore): try: import chromadb # noqa: F401 except ImportError: - raise ValueError("Could not import chromadb python package. " - "Please install it with `pip install chromadb`.") + raise ValueError( + "Could not import chromadb python package. " + "Please install it with `pip install chromadb`." + ) return self._collection.query( query_texts=query_texts, query_embeddings=query_embeddings, @@ -195,9 +202,9 @@ class Chroma(VectorStore): if non_empty_ids: metadatas = [metadatas[idx] for idx in non_empty_ids] texts_with_metadatas = [texts[idx] for idx in non_empty_ids] - embeddings_with_metadatas = ([ - embeddings[idx] for idx in non_empty_ids - ] if embeddings else None) + embeddings_with_metadatas = ( + [embeddings[idx] for idx in non_empty_ids] if embeddings else None + ) ids_with_metadata = [ids[idx] for idx in non_empty_ids] try: self._collection.upsert( @@ -218,7 +225,8 @@ class Chroma(VectorStore): if empty_ids: texts_without_metadatas = [texts[j] for j in empty_ids] embeddings_without_metadatas = ( - [embeddings[j] for j in empty_ids] if embeddings else None) + [embeddings[j] for j in empty_ids] if embeddings else None + ) ids_without_metadatas = [ids[j] for j in empty_ids] self._collection.upsert( embeddings=embeddings_without_metadatas, @@ -250,9 +258,7 @@ class Chroma(VectorStore): Returns: List[Document]: List of documents most similar to the query text. """ - docs_and_scores = self.similarity_search_with_score(query, - k, - filter=filter) + docs_and_scores = self.similarity_search_with_score(query, k, filter=filter) return [doc for doc, _ in docs_and_scores] def similarity_search_by_vector( @@ -375,7 +381,8 @@ class Chroma(VectorStore): raise ValueError( "No supported normalization function" f" for distance metric of type: {distance}." - "Consider providing relevance_score_fn to Chroma constructor.") + "Consider providing relevance_score_fn to Chroma constructor." + ) def max_marginal_relevance_search_by_vector( self, @@ -421,9 +428,7 @@ class Chroma(VectorStore): candidates = _results_to_docs(results) - selected_results = [ - r for i, r in enumerate(candidates) if i in mmr_selected - ] + selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected] return selected_results def max_marginal_relevance_search( @@ -518,8 +523,10 @@ class Chroma(VectorStore): It will also be called automatically when the object is destroyed. """ if self._persist_directory is None: - raise ValueError("You must specify a persist_directory on" - "creation to persist the collection.") + raise ValueError( + "You must specify a persist_directory on" + "creation to persist the collection." + ) import chromadb # Maintain backwards compatibility with chromadb < 0.4.0 @@ -536,8 +543,7 @@ class Chroma(VectorStore): """ return self.update_documents([document_id], [document]) - def update_documents(self, ids: List[str], - documents: List[Document]) -> None: + def update_documents(self, ids: List[str], documents: List[Document]) -> None: """Update a document in the collection. Args: @@ -552,16 +558,17 @@ class Chroma(VectorStore): ) embeddings = self._embedding_function.embed_documents(text) - if hasattr(self._collection._client, - "max_batch_size"): # for Chroma 0.4.10 and above + if hasattr( + self._collection._client, "max_batch_size" + ): # for Chroma 0.4.10 and above from chromadb.utils.batch_utils import create_batches for batch in create_batches( - api=self._collection._client, - ids=ids, - metadatas=metadata, - documents=text, - embeddings=embeddings, + api=self._collection._client, + ids=ids, + metadatas=metadata, + documents=text, + embeddings=embeddings, ): self._collection.update( ids=batch[0], @@ -621,15 +628,16 @@ class Chroma(VectorStore): ) if ids is None: ids = [str(uuid.uuid1()) for _ in texts] - if hasattr(chroma_collection._client, - "max_batch_size"): # for Chroma 0.4.10 and above + if hasattr( + chroma_collection._client, "max_batch_size" + ): # for Chroma 0.4.10 and above from chromadb.utils.batch_utils import create_batches for batch in create_batches( - api=chroma_collection._client, - ids=ids, - metadatas=metadatas, - documents=texts, + api=chroma_collection._client, + ids=ids, + metadatas=metadatas, + documents=texts, ): chroma_collection.add_texts( texts=batch[3] if batch[3] else [], @@ -637,9 +645,7 @@ class Chroma(VectorStore): ids=batch[0], ) else: - chroma_collection.add_texts(texts=texts, - metadatas=metadatas, - ids=ids) + chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids) return chroma_collection @classmethod diff --git a/swarms/memory/cosine_similarity.py b/swarms/memory/cosine_similarity.py index 9b183834..99d47368 100644 --- a/swarms/memory/cosine_similarity.py +++ b/swarms/memory/cosine_similarity.py @@ -19,7 +19,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: if X.shape[1] != Y.shape[1]: raise ValueError( f"Number of columns in X and Y must be the same. X has shape {X.shape} " - f"and Y has shape {Y.shape}.") + f"and Y has shape {Y.shape}." + ) try: import simsimd as simd @@ -32,7 +33,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: except ImportError: logger.info( "Unable to import simsimd, defaulting to NumPy implementation. If you want " - "to use simsimd please install with `pip install simsimd`.") + "to use simsimd please install with `pip install simsimd`." + ) X_norm = np.linalg.norm(X, axis=1) Y_norm = np.linalg.norm(Y, axis=1) # Ignore divide by zero errors run time warnings as those are handled below. diff --git a/swarms/memory/db.py b/swarms/memory/db.py index 8e6bad12..9f23b59f 100644 --- a/swarms/memory/db.py +++ b/swarms/memory/db.py @@ -27,7 +27,6 @@ class NotFoundException(Exception): class TaskDB(ABC): - async def create_task( self, input: Optional[str], @@ -68,9 +67,9 @@ class TaskDB(ABC): async def list_tasks(self) -> List[Task]: raise NotImplementedError - async def list_steps(self, - task_id: str, - status: Optional[Status] = None) -> List[Step]: + async def list_steps( + self, task_id: str, status: Optional[Status] = None + ) -> List[Step]: raise NotImplementedError @@ -137,8 +136,8 @@ class InMemoryTaskDB(TaskDB): async def get_artifact(self, task_id: str, artifact_id: str) -> Artifact: task = await self.get_task(task_id) artifact = next( - filter(lambda a: a.artifact_id == artifact_id, task.artifacts), - None) + filter(lambda a: a.artifact_id == artifact_id, task.artifacts), None + ) if not artifact: raise NotFoundException("Artifact", artifact_id) return artifact @@ -151,9 +150,9 @@ class InMemoryTaskDB(TaskDB): step_id: Optional[str] = None, ) -> Artifact: artifact_id = str(uuid.uuid4()) - artifact = Artifact(artifact_id=artifact_id, - file_name=file_name, - relative_path=relative_path) + artifact = Artifact( + artifact_id=artifact_id, file_name=file_name, relative_path=relative_path + ) task = await self.get_task(task_id) task.artifacts.append(artifact) @@ -166,9 +165,9 @@ class InMemoryTaskDB(TaskDB): async def list_tasks(self) -> List[Task]: return [task for task in self._tasks.values()] - async def list_steps(self, - task_id: str, - status: Optional[Status] = None) -> List[Step]: + async def list_steps( + self, task_id: str, status: Optional[Status] = None + ) -> List[Step]: task = await self.get_task(task_id) steps = task.steps if status: diff --git a/swarms/memory/ocean.py b/swarms/memory/ocean.py index 339c3596..da58c81c 100644 --- a/swarms/memory/ocean.py +++ b/swarms/memory/ocean.py @@ -63,7 +63,8 @@ class OceanDB: try: embedding_function = MultiModalEmbeddingFunction(modality=modality) collection = self.client.create_collection( - collection_name, embedding_function=embedding_function) + collection_name, embedding_function=embedding_function + ) return collection except Exception as e: logging.error(f"Failed to create collection. Error {e}") @@ -90,8 +91,7 @@ class OceanDB: try: return collection.add(documents=[document], ids=[id]) except Exception as e: - logging.error( - f"Failed to append document to the collection. Error {e}") + logging.error(f"Failed to append document to the collection. Error {e}") raise def add_documents(self, collection, documents: List[str], ids: List[str]): @@ -137,8 +137,7 @@ class OceanDB: the results of the query """ try: - results = collection.query(query_texts=query_texts, - n_results=n_results) + results = collection.query(query_texts=query_texts, n_results=n_results) return results except Exception as e: logging.error(f"Failed to query the collection. Error {e}") diff --git a/swarms/memory/pg.py b/swarms/memory/pg.py index 09534cac..bd768459 100644 --- a/swarms/memory/pg.py +++ b/swarms/memory/pg.py @@ -88,12 +88,12 @@ class PgVectorVectorStore(BaseVectorStore): create_engine_params: dict = field(factory=dict, kw_only=True) engine: Optional[Engine] = field(default=None, kw_only=True) table_name: str = field(kw_only=True) - _model: any = field(default=Factory( - lambda self: self.default_vector_model(), takes_self=True)) + _model: any = field( + default=Factory(lambda self: self.default_vector_model(), takes_self=True) + ) @connection_string.validator - def validate_connection_string(self, _, - connection_string: Optional[str]) -> None: + def validate_connection_string(self, _, connection_string: Optional[str]) -> None: # If an engine is provided, the connection string is not used. if self.engine is not None: return @@ -122,8 +122,9 @@ class PgVectorVectorStore(BaseVectorStore): If not, a connection string is used to create a new database connection here. """ if self.engine is None: - self.engine = create_engine(self.connection_string, - **self.create_engine_params) + self.engine = create_engine( + self.connection_string, **self.create_engine_params + ) def setup( self, @@ -141,12 +142,14 @@ class PgVectorVectorStore(BaseVectorStore): if create_schema: self._model.metadata.create_all(self.engine) - def upsert_vector(self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs) -> str: + def upsert_vector( + self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs + ) -> str: """Inserts or updates a vector in the collection.""" with Session(self.engine) as session: obj = self._model( @@ -161,9 +164,9 @@ class PgVectorVectorStore(BaseVectorStore): return str(obj.id) - def load_entry(self, - vector_id: str, - namespace: Optional[str] = None) -> BaseVectorStore.Entry: + def load_entry( + self, vector_id: str, namespace: Optional[str] = None + ) -> BaseVectorStore.Entry: """Retrieves a specific vector entry from the collection based on its identifier and optional namespace.""" with Session(self.engine) as session: result = session.get(self._model, vector_id) @@ -176,8 +179,8 @@ class PgVectorVectorStore(BaseVectorStore): ) def load_entries( - self, - namespace: Optional[str] = None) -> list[BaseVectorStore.Entry]: + self, namespace: Optional[str] = None + ) -> list[BaseVectorStore.Entry]: """Retrieves all vector entries from the collection, optionally filtering to only those that match the provided namespace. """ @@ -194,16 +197,19 @@ class PgVectorVectorStore(BaseVectorStore): vector=result.vector, namespace=result.namespace, meta=result.meta, - ) for result in results + ) + for result in results ] - def query(self, - query: str, - count: Optional[int] = BaseVectorStore.DEFAULT_QUERY_COUNT, - namespace: Optional[str] = None, - include_vectors: bool = False, - distance_metric: str = "cosine_distance", - **kwargs) -> list[BaseVectorStore.QueryResult]: + def query( + self, + query: str, + count: Optional[int] = BaseVectorStore.DEFAULT_QUERY_COUNT, + namespace: Optional[str] = None, + include_vectors: bool = False, + distance_metric: str = "cosine_distance", + **kwargs + ) -> list[BaseVectorStore.QueryResult]: """Performs a search on the collection to find vectors similar to the provided input vector, optionally filtering to only those that match the provided namespace. """ @@ -239,7 +245,8 @@ class PgVectorVectorStore(BaseVectorStore): score=result[1], meta=result[0].meta, namespace=result[0].namespace, - ) for result in results + ) + for result in results ] def default_vector_model(self) -> any: diff --git a/swarms/memory/pinecone.py b/swarms/memory/pinecone.py index 0269aa38..2374f12a 100644 --- a/swarms/memory/pinecone.py +++ b/swarms/memory/pinecone.py @@ -102,12 +102,14 @@ class PineconeVectorStoreStore(BaseVector): self.index = pinecone.Index(self.index_name) - def upsert_vector(self, - vector: list[float], - vector_id: Optional[str] = None, - namespace: Optional[str] = None, - meta: Optional[dict] = None, - **kwargs) -> str: + def upsert_vector( + self, + vector: list[float], + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs + ) -> str: """Upsert vector""" vector_id = vector_id if vector_id else str_to_hash(str(vector)) @@ -118,12 +120,10 @@ class PineconeVectorStoreStore(BaseVector): return vector_id def load_entry( - self, - vector_id: str, - namespace: Optional[str] = None) -> Optional[BaseVector.Entry]: + self, vector_id: str, namespace: Optional[str] = None + ) -> Optional[BaseVector.Entry]: """Load entry""" - result = self.index.fetch(ids=[vector_id], - namespace=namespace).to_dict() + result = self.index.fetch(ids=[vector_id], namespace=namespace).to_dict() vectors = list(result["vectors"].values()) if len(vectors) > 0: @@ -138,8 +138,7 @@ class PineconeVectorStoreStore(BaseVector): else: return None - def load_entries(self, - namespace: Optional[str] = None) -> list[BaseVector.Entry]: + def load_entries(self, namespace: Optional[str] = None) -> list[BaseVector.Entry]: """Load entries""" # This is a hacky way to query up to 10,000 values from Pinecone. Waiting on an official API for fetching # all values from a namespace: @@ -158,18 +157,20 @@ class PineconeVectorStoreStore(BaseVector): vector=r["values"], meta=r["metadata"], namespace=results["namespace"], - ) for r in results["matches"] + ) + for r in results["matches"] ] def query( - self, - query: str, - count: Optional[int] = None, - namespace: Optional[str] = None, - include_vectors: bool = False, - # PineconeVectorStoreStorageDriver-specific params: - include_metadata=True, - **kwargs) -> list[BaseVector.QueryResult]: + self, + query: str, + count: Optional[int] = None, + namespace: Optional[str] = None, + include_vectors: bool = False, + # PineconeVectorStoreStorageDriver-specific params: + include_metadata=True, + **kwargs + ) -> list[BaseVector.QueryResult]: """Query vectors""" vector = self.embedding_driver.embed_string(query) @@ -189,14 +190,12 @@ class PineconeVectorStoreStore(BaseVector): score=r["score"], meta=r["metadata"], namespace=results["namespace"], - ) for r in results["matches"] + ) + for r in results["matches"] ] def create_index(self, name: str, **kwargs) -> None: """Create index""" - params = { - "name": name, - "dimension": self.embedding_driver.dimensions - } | kwargs + params = {"name": name, "dimension": self.embedding_driver.dimensions} | kwargs pinecone.create_index(**params) diff --git a/swarms/memory/schemas.py b/swarms/memory/schemas.py index ce54208d..bbc71bc2 100644 --- a/swarms/memory/schemas.py +++ b/swarms/memory/schemas.py @@ -20,9 +20,9 @@ class Artifact(BaseModel): description="Id of the artifact", example="b225e278-8b4c-4f99-a696-8facf19f0e56", ) - file_name: str = Field(..., - description="Filename of the artifact", - example="main.py") + file_name: str = Field( + ..., description="Filename of the artifact", example="main.py" + ) relative_path: Optional[str] = Field( None, description="Relative path of the artifact in the agent's workspace", @@ -50,8 +50,7 @@ class StepInput(BaseModel): class StepOutput(BaseModel): __root__: Any = Field( ..., - description= - "Output that the task step has produced. Any value is allowed.", + description="Output that the task step has produced. Any value is allowed.", example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}', ) @@ -82,9 +81,9 @@ class Task(TaskRequestBody): class StepRequestBody(BaseModel): - input: Optional[str] = Field(None, - description="Input prompt for the step.", - example="Washington") + input: Optional[str] = Field( + None, description="Input prompt for the step.", example="Washington" + ) additional_input: Optional[StepInput] = None @@ -105,19 +104,22 @@ class Step(StepRequestBody): description="The ID of the task step.", example="6bb1801a-fd80-45e8-899a-4dd723cc602e", ) - name: Optional[str] = Field(None, - description="The name of the task step.", - example="Write to file") + name: Optional[str] = Field( + None, description="The name of the task step.", example="Write to file" + ) status: Status = Field(..., description="The status of the task step.") output: Optional[str] = Field( None, description="Output of the task step.", - example= - ("I am going to use the write_to_file command and write Washington to a file" - " called output.txt best_score: best_score = equation_score idx_to_add = i @@ -56,8 +57,8 @@ def maximal_marginal_relevance( def filter_complex_metadata( documents: List[Document], *, - allowed_types: Tuple[Type, - ...] = (str, bool, int, float)) -> List[Document]: + allowed_types: Tuple[Type, ...] = (str, bool, int, float) +) -> List[Document]: """Filter out metadata types that are not supported for a vector store.""" updated_documents = [] for document in documents: diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 634fa030..30ec22ce 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -41,24 +41,21 @@ def xor_args(*arg_groups: Tuple[str, ...]) -> Callable: """Validate specified keyword args are mutually exclusive.""" def decorator(func: Callable) -> Callable: - @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: """Validate exactly one arg in each group is not None.""" counts = [ - sum(1 - for arg in arg_group - if kwargs.get(arg) is not None) + sum(1 for arg in arg_group if kwargs.get(arg) is not None) for arg_group in arg_groups ] invalid_groups = [i for i, count in enumerate(counts) if count != 1] if invalid_groups: - invalid_group_names = [ - ", ".join(arg_groups[i]) for i in invalid_groups - ] - raise ValueError("Exactly one argument in each of the following" - " groups must be defined:" - f" {', '.join(invalid_group_names)}") + invalid_group_names = [", ".join(arg_groups[i]) for i in invalid_groups] + raise ValueError( + "Exactly one argument in each of the following" + " groups must be defined:" + f" {', '.join(invalid_group_names)}" + ) return func(*args, **kwargs) return wrapper @@ -108,10 +105,9 @@ def mock_now(dt_value): # type: ignore datetime.datetime = real_datetime -def guard_import(module_name: str, - *, - pip_name: Optional[str] = None, - package: Optional[str] = None) -> Any: +def guard_import( + module_name: str, *, pip_name: Optional[str] = None, package: Optional[str] = None +) -> Any: """Dynamically imports a module and raises a helpful exception if the module is not installed.""" try: @@ -119,7 +115,8 @@ def guard_import(module_name: str, except ImportError: raise ImportError( f"Could not import {module_name} python package. " - f"Please install it with `pip install {pip_name or module_name}`.") + f"Please install it with `pip install {pip_name or module_name}`." + ) return module @@ -135,19 +132,23 @@ def check_package_version( if lt_version is not None and imported_version >= parse(lt_version): raise ValueError( f"Expected {package} version to be < {lt_version}. Received " - f"{imported_version}.") + f"{imported_version}." + ) if lte_version is not None and imported_version > parse(lte_version): raise ValueError( f"Expected {package} version to be <= {lte_version}. Received " - f"{imported_version}.") + f"{imported_version}." + ) if gt_version is not None and imported_version <= parse(gt_version): raise ValueError( f"Expected {package} version to be > {gt_version}. Received " - f"{imported_version}.") + f"{imported_version}." + ) if gte_version is not None and imported_version < parse(gte_version): raise ValueError( f"Expected {package} version to be >= {gte_version}. Received " - f"{imported_version}.") + f"{imported_version}." + ) def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]: @@ -179,17 +180,19 @@ def build_extra_kwargs( if field_name in extra_kwargs: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: - warnings.warn(f"""WARNING! {field_name} is not default parameter. + warnings.warn( + f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""") + Please confirm that {field_name} is what you intended.""" + ) extra_kwargs[field_name] = values.pop(field_name) - invalid_model_kwargs = all_required_field_names.intersection( - extra_kwargs.keys()) + invalid_model_kwargs = all_required_field_names.intersection(extra_kwargs.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " - "Instead they were passed in as part of `model_kwargs` parameter.") + "Instead they were passed in as part of `model_kwargs` parameter." + ) return extra_kwargs @@ -238,16 +241,17 @@ class _AnthropicCommon(BaseLanguageModel): def build_extra(cls, values: Dict) -> Dict: extra = values.get("model_kwargs", {}) all_required_field_names = get_pydantic_field_names(cls) - values["model_kwargs"] = build_extra_kwargs(extra, values, - all_required_field_names) + values["model_kwargs"] = build_extra_kwargs( + extra, values, all_required_field_names + ) return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["anthropic_api_key"] = convert_to_secret_str( - get_from_dict_or_env(values, "anthropic_api_key", - "ANTHROPIC_API_KEY")) + get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY") + ) # Get custom api url from environment. values["anthropic_api_url"] = get_from_dict_or_env( values, @@ -277,7 +281,8 @@ class _AnthropicCommon(BaseLanguageModel): except ImportError: raise ImportError( "Could not import anthropic python package. " - "Please it install it with `pip install anthropic`.") + "Please it install it with `pip install anthropic`." + ) return values @property @@ -300,8 +305,7 @@ class _AnthropicCommon(BaseLanguageModel): """Get the identifying parameters.""" return {**{}, **self._default_params} - def _get_anthropic_stop(self, - stop: Optional[List[str]] = None) -> List[str]: + def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]: if not self.HUMAN_PROMPT or not self.AI_PROMPT: raise NameError("Please ensure the anthropic package is loaded") @@ -368,8 +372,7 @@ class Anthropic(LLM, _AnthropicCommon): return prompt # Already wrapped. # Guard against common errors in specifying wrong number of newlines. - corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, - prompt) + corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt) if n_subs == 1: return corrected_prompt @@ -402,10 +405,9 @@ class Anthropic(LLM, _AnthropicCommon): """ if self.streaming: completion = "" - for chunk in self._stream(prompt=prompt, - stop=stop, - run_manager=run_manager, - **kwargs): + for chunk in self._stream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): completion += chunk.text return completion @@ -431,10 +433,9 @@ class Anthropic(LLM, _AnthropicCommon): """Call out to Anthropic's completion endpoint asynchronously.""" if self.streaming: completion = "" - async for chunk in self._astream(prompt=prompt, - stop=stop, - run_manager=run_manager, - **kwargs): + async for chunk in self._astream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): completion += chunk.text return completion @@ -475,10 +476,8 @@ class Anthropic(LLM, _AnthropicCommon): params = {**self._default_params, **kwargs} for token in self.client.completions.create( - prompt=self._wrap_prompt(prompt), - stop_sequences=stop, - stream=True, - **params): + prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params + ): chunk = GenerationChunk(text=token.completion) yield chunk if run_manager: @@ -510,10 +509,10 @@ class Anthropic(LLM, _AnthropicCommon): params = {**self._default_params, **kwargs} async for token in await self.async_client.completions.create( - prompt=self._wrap_prompt(prompt), - stop_sequences=stop, - stream=True, - **params, + prompt=self._wrap_prompt(prompt), + stop_sequences=stop, + stream=True, + **params, ): chunk = GenerationChunk(text=token.completion) yield chunk diff --git a/swarms/models/bioclip.py b/swarms/models/bioclip.py index d7052ef3..c2b4bfa5 100644 --- a/swarms/models/bioclip.py +++ b/swarms/models/bioclip.py @@ -97,8 +97,9 @@ class BioClip: self.preprocess_val, ) = open_clip.create_model_and_transforms(model_path) self.tokenizer = open_clip.get_tokenizer(model_path) - self.device = (torch.device("cuda") - if torch.cuda.is_available() else torch.device("cpu")) + self.device = ( + torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + ) self.model.to(self.device) self.model.eval() @@ -109,17 +110,18 @@ class BioClip: template: str = "this is a photo of ", context_length: int = 256, ): - image = torch.stack([self.preprocess_val(Image.open(img_path)) - ]).to(self.device) - texts = self.tokenizer([template + l for l in labels], - context_length=context_length).to(self.device) + image = torch.stack([self.preprocess_val(Image.open(img_path))]).to(self.device) + texts = self.tokenizer( + [template + l for l in labels], context_length=context_length + ).to(self.device) with torch.no_grad(): - image_features, text_features, logit_scale = self.model( - image, texts) - logits = ((logit_scale * - image_features @ text_features.t()).detach().softmax( - dim=-1)) + image_features, text_features, logit_scale = self.model(image, texts) + logits = ( + (logit_scale * image_features @ text_features.t()) + .detach() + .softmax(dim=-1) + ) sorted_indices = torch.argsort(logits, dim=-1, descending=True) logits = logits.cpu().numpy() sorted_indices = sorted_indices.cpu().numpy() @@ -137,8 +139,11 @@ class BioClip: fig, ax = plt.subplots(figsize=(5, 5)) ax.imshow(img) ax.axis("off") - title = (metadata["filename"] + "\n" + "\n".join( - [f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()])) + title = ( + metadata["filename"] + + "\n" + + "\n".join([f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()]) + ) ax.set_title(title, fontsize=14) plt.tight_layout() plt.show() diff --git a/swarms/models/biogpt.py b/swarms/models/biogpt.py index ebec10b9..83c31e55 100644 --- a/swarms/models/biogpt.py +++ b/swarms/models/biogpt.py @@ -102,9 +102,9 @@ class BioGPT: list[dict]: A list of generated texts. """ set_seed(42) - generator = pipeline("text-generation", - model=self.model, - tokenizer=self.tokenizer) + generator = pipeline( + "text-generation", model=self.model, tokenizer=self.tokenizer + ) out = generator( text, max_length=self.max_length, @@ -149,11 +149,13 @@ class BioGPT: inputs = self.tokenizer(sentence, return_tensors="pt") set_seed(42) with torch.no_grad(): - beam_output = self.model.generate(**inputs, - min_length=self.min_length, - max_length=self.max_length, - num_beams=num_beams, - early_stopping=early_stopping) + beam_output = self.model.generate( + **inputs, + min_length=self.min_length, + max_length=self.max_length, + num_beams=num_beams, + early_stopping=early_stopping + ) return self.tokenizer.decode(beam_output[0], skip_special_tokens=True) # Feature 1: Set a new tokenizer and model diff --git a/swarms/models/dalle3.py b/swarms/models/dalle3.py index 788bae62..c24f262d 100644 --- a/swarms/models/dalle3.py +++ b/swarms/models/dalle3.py @@ -124,10 +124,13 @@ class Dalle3: # Handling exceptions and printing the errors details print( colored( - (f"Error running Dalle3: {error} try optimizing your api key and" - " or try again"), + ( + f"Error running Dalle3: {error} try optimizing your api key and" + " or try again" + ), "red", - )) + ) + ) raise error def create_variations(self, img: str): @@ -154,19 +157,22 @@ class Dalle3: """ try: - response = self.client.images.create_variation(img=open(img, "rb"), - n=self.n, - size=self.size) + response = self.client.images.create_variation( + img=open(img, "rb"), n=self.n, size=self.size + ) img = response.data[0].url return img except (Exception, openai.OpenAIError) as error: print( colored( - (f"Error running Dalle3: {error} try optimizing your api key and" - " or try again"), + ( + f"Error running Dalle3: {error} try optimizing your api key and" + " or try again" + ), "red", - )) + ) + ) print(colored(f"Error running Dalle3: {error.http_status}", "red")) print(colored(f"Error running Dalle3: {error.error}", "red")) raise error diff --git a/swarms/models/distilled_whisperx.py b/swarms/models/distilled_whisperx.py index 8fc5b99a..0a60aaac 100644 --- a/swarms/models/distilled_whisperx.py +++ b/swarms/models/distilled_whisperx.py @@ -18,7 +18,6 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1): """ def decorator(func): - @wraps(func) async def wrapper(*args, **kwargs): retries = max_retries @@ -29,9 +28,7 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1): retries -= 1 if retries <= 0: raise - print( - f"Retry after exception: {e}, Attempts remaining: {retries}" - ) + print(f"Retry after exception: {e}, Attempts remaining: {retries}") await asyncio.sleep(delay) return wrapper @@ -65,8 +62,7 @@ class DistilWhisperModel: def __init__(self, model_id="distil-whisper/distil-large-v2"): self.device = "cuda:0" if torch.cuda.is_available() else "cpu" - self.torch_dtype = torch.float16 if torch.cuda.is_available( - ) else torch.float32 + self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 self.model_id = model_id self.model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, @@ -123,14 +119,14 @@ class DistilWhisperModel: try: with torch.no_grad(): # Load the whole audio file, but process and transcribe it in chunks - audio_input = self.processor.audio_file_to_array( - audio_file_path) + audio_input = self.processor.audio_file_to_array(audio_file_path) sample_rate = audio_input.sampling_rate total_duration = len(audio_input.array) / sample_rate chunks = [ - audio_input.array[i:i + sample_rate * chunk_duration] - for i in range(0, len(audio_input.array), sample_rate * - chunk_duration) + audio_input.array[i : i + sample_rate * chunk_duration] + for i in range( + 0, len(audio_input.array), sample_rate * chunk_duration + ) ] print(colored("Starting real-time transcription...", "green")) @@ -143,22 +139,22 @@ class DistilWhisperModel: return_tensors="pt", padding=True, ) - processed_inputs = processed_inputs.input_values.to( - self.device) + processed_inputs = processed_inputs.input_values.to(self.device) # Generate transcription for the chunk logits = self.model.generate(processed_inputs) transcription = self.processor.batch_decode( - logits, skip_special_tokens=True)[0] + logits, skip_special_tokens=True + )[0] # Print the chunk's transcription print( - colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow") + - transcription) + colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow") + + transcription + ) # Wait for the chunk's duration to simulate real-time processing time.sleep(chunk_duration) except Exception as e: - print(colored(f"An error occurred during transcription: {e}", - "red")) + print(colored(f"An error occurred during transcription: {e}", "red")) diff --git a/swarms/models/fastvit.py b/swarms/models/fastvit.py index 370569fb..a2d6bc0a 100644 --- a/swarms/models/fastvit.py +++ b/swarms/models/fastvit.py @@ -11,8 +11,7 @@ from pydantic import BaseModel, StrictFloat, StrictInt, validator DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Load the classes for image classification -with open(os.path.join(os.path.dirname(__file__), - "fast_vit_classes.json")) as f: +with open(os.path.join(os.path.dirname(__file__), "fast_vit_classes.json")) as f: FASTVIT_IMAGENET_1K_CLASSES = json.load(f) @@ -22,8 +21,7 @@ class ClassificationResult(BaseModel): @validator("class_id", "confidence", pre=True, each_item=True) def check_list_contents(cls, v): - assert isinstance(v, int) or isinstance( - v, float), "must be integer or float" + assert isinstance(v, int) or isinstance(v, float), "must be integer or float" return v @@ -49,16 +47,16 @@ class FastViT: """ def __init__(self): - self.model = timm.create_model("hf_hub:timm/fastvit_s12.apple_in1k", - pretrained=True).to(DEVICE) + self.model = timm.create_model( + "hf_hub:timm/fastvit_s12.apple_in1k", pretrained=True + ).to(DEVICE) data_config = timm.data.resolve_model_data_config(self.model) - self.transforms = timm.data.create_transform(**data_config, - is_training=False) + self.transforms = timm.data.create_transform(**data_config, is_training=False) self.model.eval() - def __call__(self, - img: str, - confidence_threshold: float = 0.5) -> ClassificationResult: + def __call__( + self, img: str, confidence_threshold: float = 0.5 + ) -> ClassificationResult: """classifies the input image and returns the top k classes and their probabilities""" img = Image.open(img).convert("RGB") img_tensor = self.transforms(img).unsqueeze(0).to(DEVICE) @@ -67,8 +65,9 @@ class FastViT: probabilities = torch.nn.functional.softmax(output, dim=1) # Get top k classes and their probabilities - top_probs, top_classes = torch.topk(probabilities, - k=FASTVIT_IMAGENET_1K_CLASSES) + top_probs, top_classes = torch.topk( + probabilities, k=FASTVIT_IMAGENET_1K_CLASSES + ) # Filter by confidence threshold mask = top_probs > confidence_threshold diff --git a/swarms/models/fuyu.py b/swarms/models/fuyu.py index 63108835..dd664f51 100644 --- a/swarms/models/fuyu.py +++ b/swarms/models/fuyu.py @@ -45,9 +45,9 @@ class Fuyu: self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path) self.image_processor = FuyuImageProcessor() - self.processor = FuyuProcessor(image_processor=self.image_processor, - tokenizer=self.tokenizer, - **kwargs) + self.processor = FuyuProcessor( + image_processor=self.image_processor, tokenizer=self.tokenizer, **kwargs + ) self.model = FuyuForCausalLM.from_pretrained( pretrained_path, device_map=device_map, @@ -62,17 +62,15 @@ class Fuyu: def __call__(self, text: str, img: str): """Call the model with text and img paths""" image_pil = Image.open(img) - model_inputs = self.processor(text=text, - images=[image_pil], - device=self.device_map) + model_inputs = self.processor( + text=text, images=[image_pil], device=self.device_map + ) for k, v in model_inputs.items(): model_inputs[k] = v.to(self.device_map) - output = self.model.generate(**model_inputs, - max_new_tokens=self.max_new_tokens) - text = self.processor.batch_decode(output[:, -7:], - skip_special_tokens=True) + output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens) + text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True) return print(str(text)) def get_img_from_web(self, img_url: str): diff --git a/swarms/models/gpt4v.py b/swarms/models/gpt4v.py index 251744e8..d1d5ce1f 100644 --- a/swarms/models/gpt4v.py +++ b/swarms/models/gpt4v.py @@ -69,7 +69,9 @@ class GPT4Vision: quality: str = "low" # Max tokens to use for the API request, the maximum might be 3,000 but we don't know max_tokens: int = 200 - client = OpenAI(api_key=openai_api_key,) + client = OpenAI( + api_key=openai_api_key, + ) dashboard: bool = True call_limit: int = 1 period_seconds: int = 60 @@ -88,8 +90,9 @@ class GPT4Vision: return base64.b64encode(image_file.read()).decode("utf-8") @sleep_and_retry - @limits(calls=call_limit, - period=period_seconds) # Rate limit of 10 calls per minute + @limits( + calls=call_limit, period=period_seconds + ) # Rate limit of 10 calls per minute def run(self, task: str, img: str): """ Run the GPT-4 Vision model @@ -105,22 +108,20 @@ class GPT4Vision: try: response = self.client.chat.completions.create( model="gpt-4-vision-preview", - messages=[{ - "role": - "user", - "content": [ - { - "type": "text", - "text": task - }, - { - "type": "image_url", - "image_url": { - "url": str(img), + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": task}, + { + "type": "image_url", + "image_url": { + "url": str(img), + }, }, - }, - ], - }], + ], + } + ], max_tokens=self.max_tokens, ) @@ -160,22 +161,20 @@ class GPT4Vision: try: response = await self.client.chat.completions.create( model="gpt-4-vision-preview", - messages=[{ - "role": - "user", - "content": [ - { - "type": "text", - "text": task - }, - { - "type": "image_url", - "image_url": { - "url": img, + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": task}, + { + "type": "image_url", + "image_url": { + "url": img, + }, }, - }, - ], - }], + ], + } + ], max_tokens=self.max_tokens, ) @@ -190,14 +189,12 @@ class GPT4Vision: """Process a batch of tasks and images""" with concurrent.futures.ThreadPoolExecutor() as executor: futures = [ - executor.submit(self.run, task, img) - for task, img in tasks_images + executor.submit(self.run, task, img) for task, img in tasks_images ] results = [future.result() for future in futures] return results - async def run_batch_async(self, - tasks_images: List[Tuple[str, str]]) -> List[str]: + async def run_batch_async(self, tasks_images: List[Tuple[str, str]]) -> List[str]: """Process a batch of tasks and images asynchronously""" loop = asyncio.get_event_loop() futures = [ @@ -207,7 +204,8 @@ class GPT4Vision: return await asyncio.gather(*futures) async def run_batch_async_with_retries( - self, tasks_images: List[Tuple[str, str]]) -> List[str]: + self, tasks_images: List[Tuple[str, str]] + ) -> List[str]: """Process a batch of tasks and images asynchronously with retries""" loop = asyncio.get_event_loop() futures = [ @@ -231,7 +229,8 @@ class GPT4Vision: """, "green", - )) + ) + ) return dashboard def health_check(self): diff --git a/swarms/models/huggingface.py b/swarms/models/huggingface.py index a84cc960..9279fea4 100644 --- a/swarms/models/huggingface.py +++ b/swarms/models/huggingface.py @@ -47,8 +47,9 @@ class HuggingfaceLLM: **kwargs, ): self.logger = logging.getLogger(__name__) - self.device = (device if device else - ("cuda" if torch.cuda.is_available() else "cpu")) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -57,8 +58,9 @@ class HuggingfaceLLM: self.model, self.tokenizer = None, None if self.distributed: - assert (torch.cuda.device_count() > - 1), "You need more than 1 gpu for distributed processing" + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -73,17 +75,17 @@ class HuggingfaceLLM: try: self.tokenizer = AutoTokenizer.from_pretrained( - self.model_id, *args, **kwargs) + self.model_id, *args, **kwargs + ) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config, *args, **kwargs) + self.model_id, quantization_config=bnb_config, *args, **kwargs + ) self.model # .to(self.device) except Exception as e: # self.logger.error(f"Failed to load the model or the tokenizer: {e}") # raise - print( - colored(f"Failed to load the model and or the tokenizer: {e}", - "red")) + print(colored(f"Failed to load the model and or the tokenizer: {e}", "red")) def print_error(self, error: str): """Print error""" @@ -95,18 +97,20 @@ class HuggingfaceLLM: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = (BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config else None) + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, - quantization_config=bnb_config).to(self.device) + self.model_id, quantization_config=bnb_config + ).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error( - f"Failed to load the model or the tokenizer: {error}") + self.logger.error(f"Failed to load the model or the tokenizer: {error}") raise def run(self, task: str): @@ -127,8 +131,7 @@ class HuggingfaceLLM: self.print_dashboard(task) try: - inputs = self.tokenizer.encode(task, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) # self.log.start() @@ -137,36 +140,39 @@ class HuggingfaceLLM: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) except Exception as e: print( colored( - (f"HuggingfaceLLM could not generate text because of error: {e}," - " try optimizing your arguments"), + ( + f"HuggingfaceLLM could not generate text because of error: {e}," + " try optimizing your arguments" + ), "red", - )) + ) + ) raise async def run_async(self, task: str, *args, **kwargs) -> str: @@ -210,8 +216,7 @@ class HuggingfaceLLM: self.print_dashboard(task) try: - inputs = self.tokenizer.encode(task, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(task, return_tensors="pt").to(self.device) # self.log.start() @@ -220,26 +225,26 @@ class HuggingfaceLLM: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs @@ -300,7 +305,8 @@ class HuggingfaceLLM: """, "red", - )) + ) + ) print(dashboard) diff --git a/swarms/models/idefics.py b/swarms/models/idefics.py index 41b8823d..73cb4991 100644 --- a/swarms/models/idefics.py +++ b/swarms/models/idefics.py @@ -65,8 +65,9 @@ class Idefics: torch_dtype=torch.bfloat16, max_length=100, ): - self.device = (device if device else - ("cuda" if torch.cuda.is_available() else "cpu")) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) self.model = IdeficsForVisionText2Text.from_pretrained( checkpoint, torch_dtype=torch_dtype, @@ -95,17 +96,21 @@ class Idefics: list A list of generated text strings. """ - inputs = (self.processor( - prompts, add_end_of_utterance_token=False, return_tensors="pt").to( - self.device) if batched_mode else self.processor( - prompts[0], return_tensors="pt").to(self.device)) + inputs = ( + self.processor( + prompts, add_end_of_utterance_token=False, return_tensors="pt" + ).to(self.device) + if batched_mode + else self.processor(prompts[0], return_tensors="pt").to(self.device) + ) exit_condition = self.processor.tokenizer( - "", add_special_tokens=False).input_ids + "", add_special_tokens=False + ).input_ids bad_words_ids = self.processor.tokenizer( - ["", "", "", add_special_tokens=False).input_ids + "", add_special_tokens=False + ).input_ids bad_words_ids = self.processor.tokenizer( - ["", "", " - 1), "You need more than 1 gpu for distributed processing" + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -81,9 +83,8 @@ class JinaEmbeddings: try: self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, - quantization_config=bnb_config, - trust_remote_code=True) + self.model_id, quantization_config=bnb_config, trust_remote_code=True + ) self.model # .to(self.device) except Exception as e: @@ -96,8 +97,11 @@ class JinaEmbeddings: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = (BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config else None) + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) self.model = AutoModelForCausalLM.from_pretrained( self.model_id, @@ -108,8 +112,7 @@ class JinaEmbeddings: if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error( - f"Failed to load the model or the tokenizer: {error}") + self.logger.error(f"Failed to load the model or the tokenizer: {error}") raise def run(self, task: str): diff --git a/swarms/models/kosmos2.py b/swarms/models/kosmos2.py index 9a1eafba..12d5638a 100644 --- a/swarms/models/kosmos2.py +++ b/swarms/models/kosmos2.py @@ -14,8 +14,11 @@ class Detections(BaseModel): @root_validator def check_length(cls, values): - assert (len(values.get("xyxy")) == len(values.get("class_id")) == len( - values.get("confidence"))), "All fields must have the same length." + assert ( + len(values.get("xyxy")) + == len(values.get("class_id")) + == len(values.get("confidence")) + ), "All fields must have the same length." return values @validator("xyxy", "class_id", "confidence", pre=True, each_item=True) @@ -36,9 +39,11 @@ class Kosmos2(BaseModel): @classmethod def initialize(cls): model = AutoModelForVision2Seq.from_pretrained( - "ydshieh/kosmos-2-patch14-224", trust_remote_code=True) + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True + ) processor = AutoProcessor.from_pretrained( - "ydshieh/kosmos-2-patch14-224", trust_remote_code=True) + "ydshieh/kosmos-2-patch14-224", trust_remote_code=True + ) return cls(model=model, processor=processor) def __call__(self, img: str) -> Detections: @@ -46,12 +51,11 @@ class Kosmos2(BaseModel): prompt = "An image of" inputs = self.processor(text=prompt, images=image, return_tensors="pt") - outputs = self.model.generate(**inputs, - use_cache=True, - max_new_tokens=64) + outputs = self.model.generate(**inputs, use_cache=True, max_new_tokens=64) - generated_text = self.processor.batch_decode( - outputs, skip_special_tokens=True)[0] + generated_text = self.processor.batch_decode(outputs, skip_special_tokens=True)[ + 0 + ] # The actual processing of generated_text to entities would go here # For the purpose of this example, assume a mock function 'extract_entities' exists: @@ -62,8 +66,8 @@ class Kosmos2(BaseModel): return detections def extract_entities( - self, - text: str) -> List[Tuple[str, Tuple[float, float, float, float]]]: + self, text: str + ) -> List[Tuple[str, Tuple[float, float, float, float]]]: # Placeholder function for entity extraction # This should be replaced with the actual method of extracting entities return [] @@ -76,19 +80,19 @@ class Kosmos2(BaseModel): if not entities: return Detections.empty() - class_ids = [0] * len( - entities) # Replace with actual class ID extraction logic - xyxys = [( - e[1][0] * image.width, - e[1][1] * image.height, - e[1][2] * image.width, - e[1][3] * image.height, - ) for e in entities] + class_ids = [0] * len(entities) # Replace with actual class ID extraction logic + xyxys = [ + ( + e[1][0] * image.width, + e[1][1] * image.height, + e[1][2] * image.width, + e[1][3] * image.height, + ) + for e in entities + ] confidences = [1.0] * len(entities) # Placeholder confidence - return Detections(xyxy=xyxys, - class_id=class_ids, - confidence=confidences) + return Detections(xyxy=xyxys, class_id=class_ids, confidence=confidences) # Usage: diff --git a/swarms/models/kosmos_two.py b/swarms/models/kosmos_two.py index 402ad73d..596886f3 100644 --- a/swarms/models/kosmos_two.py +++ b/swarms/models/kosmos_two.py @@ -46,9 +46,11 @@ class Kosmos: model_name="ydshieh/kosmos-2-patch14-224", ): self.model = AutoModelForVision2Seq.from_pretrained( - model_name, trust_remote_code=True) - self.processor = AutoProcessor.from_pretrained(model_name, - trust_remote_code=True) + model_name, trust_remote_code=True + ) + self.processor = AutoProcessor.from_pretrained( + model_name, trust_remote_code=True + ) def get_image(self, url): """Image""" @@ -71,7 +73,8 @@ class Kosmos: skip_special_tokens=True, )[0] processed_text, entities = self.processor.post_process_generation( - generated_texts) + generated_texts + ) def __call__(self, prompt, image): """Run call""" @@ -90,7 +93,8 @@ class Kosmos: skip_special_tokens=True, )[0] processed_text, entities = self.processor.post_process_generation( - generated_texts) + generated_texts + ) # tasks def multimodal_grounding(self, phrase, image_url): @@ -141,10 +145,12 @@ class Kosmos: elif isinstance(image, torch.Tensor): # pdb.set_trace() image_tensor = image.cpu() - reverse_norm_mean = torch.tensor( - [0.48145466, 0.4578275, 0.40821073])[:, None, None] - reverse_norm_std = torch.tensor( - [0.26862954, 0.26130258, 0.27577711])[:, None, None] + reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[ + :, None, None + ] + reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[ + :, None, None + ] image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean pil_img = T.ToPILImage()(image_tensor) image_h = pil_img.height @@ -163,9 +169,9 @@ class Kosmos: # thickness of text text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) box_line = 3 - (c_width, text_height), _ = cv2.getTextSize("F", - cv2.FONT_HERSHEY_COMPLEX, - text_size, text_line) + (c_width, text_height), _ = cv2.getTextSize( + "F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line + ) base_height = int(text_height * 0.675) text_offset_original = text_height - base_height text_spaces = 3 @@ -181,8 +187,9 @@ class Kosmos: # draw bbox # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) - new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), - (orig_x2, orig_y2), color, box_line) + new_image = cv2.rectangle( + new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line + ) l_o, r_o = ( box_line // 2 + box_line % 2, @@ -193,15 +200,19 @@ class Kosmos: y1 = orig_y1 - l_o if y1 < text_height + text_offset_original + 2 * text_spaces: - y1 = (orig_y1 + r_o + text_height + text_offset_original + - 2 * text_spaces) + y1 = ( + orig_y1 + + r_o + + text_height + + text_offset_original + + 2 * text_spaces + ) x1 = orig_x1 + r_o # add text background - (text_width, - text_height), _ = cv2.getTextSize(f" {entity_name}", - cv2.FONT_HERSHEY_COMPLEX, - text_size, text_line) + (text_width, text_height), _ = cv2.getTextSize( + f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line + ) text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = ( x1, y1 - (text_height + text_offset_original + 2 * text_spaces), @@ -211,19 +222,23 @@ class Kosmos: for prev_bbox in previous_bboxes: while is_overlapping( - (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), - prev_bbox): - text_bg_y1 += (text_height + text_offset_original + - 2 * text_spaces) - text_bg_y2 += (text_height + text_offset_original + - 2 * text_spaces) + (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox + ): + text_bg_y1 += ( + text_height + text_offset_original + 2 * text_spaces + ) + text_bg_y2 += ( + text_height + text_offset_original + 2 * text_spaces + ) y1 += text_height + text_offset_original + 2 * text_spaces if text_bg_y2 >= image_h: text_bg_y1 = max( 0, - image_h - (text_height + text_offset_original + - 2 * text_spaces), + image_h + - ( + text_height + text_offset_original + 2 * text_spaces + ), ) text_bg_y2 = image_h y1 = image_h @@ -240,9 +255,9 @@ class Kosmos: # white bg_color = [255, 255, 255] new_image[i, j] = ( - alpha * new_image[i, j] + - (1 - alpha) * np.array(bg_color)).astype( - np.uint8) + alpha * new_image[i, j] + + (1 - alpha) * np.array(bg_color) + ).astype(np.uint8) cv2.putText( new_image, @@ -255,8 +270,7 @@ class Kosmos: cv2.LINE_AA, ) # previous_locations.append((x1, y1)) - previous_bboxes.append( - (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) + previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]]) if save_path: diff --git a/swarms/models/llava.py b/swarms/models/llava.py index 7f49ad4a..6f8019bc 100644 --- a/swarms/models/llava.py +++ b/swarms/models/llava.py @@ -48,8 +48,9 @@ class MultiModalLlava: revision=revision, ).to(self.device) - self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, - use_fast=True) + self.tokenizer = AutoTokenizer.from_pretrained( + model_name_or_path, use_fast=True + ) self.pipe = pipeline( "text-generation", model=self.model, diff --git a/swarms/models/mistral.py b/swarms/models/mistral.py index f14d9e39..7f48a0d6 100644 --- a/swarms/models/mistral.py +++ b/swarms/models/mistral.py @@ -49,8 +49,7 @@ class Mistral: # Check if the specified device is available if not torch.cuda.is_available() and device == "cuda": - raise ValueError( - "CUDA is not available. Please choose a different device.") + raise ValueError("CUDA is not available. Please choose a different device.") # Load the model and tokenizer self.model = None @@ -71,8 +70,7 @@ class Mistral: """Run the model on a given task.""" try: - model_inputs = self.tokenizer([task], - return_tensors="pt").to(self.device) + model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device) generated_ids = self.model.generate( **model_inputs, max_length=self.max_length, @@ -89,8 +87,7 @@ class Mistral: """Run the model on a given task.""" try: - model_inputs = self.tokenizer([task], - return_tensors="pt").to(self.device) + model_inputs = self.tokenizer([task], return_tensors="pt").to(self.device) generated_ids = self.model.generate( **model_inputs, max_length=self.max_length, diff --git a/swarms/models/mpt.py b/swarms/models/mpt.py index 9fb6c90b..035e2b54 100644 --- a/swarms/models/mpt.py +++ b/swarms/models/mpt.py @@ -26,10 +26,7 @@ class MPT7B: """ - def __init__(self, - model_name: str, - tokenizer_name: str, - max_tokens: int = 100): + def __init__(self, model_name: str, tokenizer_name: str, max_tokens: int = 100): # Loading model and tokenizer details self.model_name = model_name self.tokenizer_name = tokenizer_name @@ -40,9 +37,11 @@ class MPT7B: self.logger = logging.getLogger(__name__) config = AutoModelForCausalLM.from_pretrained( - model_name, trust_remote_code=True).config + model_name, trust_remote_code=True + ).config self.model = AutoModelForCausalLM.from_pretrained( - model_name, config=config, trust_remote_code=True) + model_name, config=config, trust_remote_code=True + ) # Initializing a text-generation pipeline self.pipe = pipeline( @@ -115,10 +114,9 @@ class MPT7B: """ with torch.autocast("cuda", dtype=torch.bfloat16): - return self.pipe(prompt, - max_new_tokens=self.max_tokens, - do_sample=True, - use_cache=True)[0]["generated_text"] + return self.pipe( + prompt, max_new_tokens=self.max_tokens, do_sample=True, use_cache=True + )[0]["generated_text"] async def generate_async(self, prompt: str) -> str: """Generate Async""" diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index 4de1d952..f156981c 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -41,10 +41,8 @@ class Nougat: self.min_length = min_length self.max_new_tokens = max_new_tokens - self.processor = NougatProcessor.from_pretrained( - self.model_name_or_path) - self.model = VisionEncoderDecoderModel.from_pretrained( - self.model_name_or_path) + self.processor = NougatProcessor.from_pretrained(self.model_name_or_path) + self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name_or_path) self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model.to(self.device) @@ -65,10 +63,8 @@ class Nougat: max_new_tokens=self.max_new_tokens, ) - sequence = self.processor.batch_decode(outputs, - skip_special_tokens=True)[0] - sequence = self.processor.post_process_generation(sequence, - fix_markdown=False) + sequence = self.processor.batch_decode(outputs, skip_special_tokens=True)[0] + sequence = self.processor.post_process_generation(sequence, fix_markdown=False) out = print(sequence) return out @@ -76,7 +72,8 @@ class Nougat: def clean_nougat_output(raw_output): # Define the pattern to extract the relevant data daily_balance_pattern = ( - r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*") + r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*" + ) # Find all matches of the pattern matches = re.findall(daily_balance_pattern, raw_output) diff --git a/swarms/models/openai_assistant.py b/swarms/models/openai_assistant.py index 37b41191..6d0c518f 100644 --- a/swarms/models/openai_assistant.py +++ b/swarms/models/openai_assistant.py @@ -55,9 +55,9 @@ class OpenAIAssistant: return thread def add_message_to_thread(self, thread_id: str, message: str): - message = self.client.beta.threads.add_message(thread_id=thread_id, - role=self.user, - content=message) + message = self.client.beta.threads.add_message( + thread_id=thread_id, role=self.user, content=message + ) return message def run(self, task: str): @@ -67,7 +67,8 @@ class OpenAIAssistant: instructions=self.instructions, ) - out = self.client.beta.threads.runs.retrieve(thread_id=run.thread_id, - run_id=run.id) + out = self.client.beta.threads.runs.retrieve( + thread_id=run.thread_id, run_id=run.id + ) return out diff --git a/swarms/models/openai_embeddings.py b/swarms/models/openai_embeddings.py index 8eeb009d..81dea550 100644 --- a/swarms/models/openai_embeddings.py +++ b/swarms/models/openai_embeddings.py @@ -28,10 +28,9 @@ from tenacity import ( from swarms.models.embeddings_base import Embeddings -def get_from_dict_or_env(values: dict, - key: str, - env_key: str, - default: Any = None) -> Any: +def get_from_dict_or_env( + values: dict, key: str, env_key: str, default: Any = None +) -> Any: import os return values.get(key) or os.getenv(env_key) or default @@ -44,8 +43,7 @@ def get_pydantic_field_names(cls: Any) -> Set[str]: logger = logging.getLogger(__name__) -def _create_retry_decorator( - embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: +def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]: import llm min_seconds = 4 @@ -56,11 +54,13 @@ def _create_retry_decorator( reraise=True, stop=stop_after_attempt(embeddings.max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), - retry=(retry_if_exception_type(llm.error.Timeout) | - retry_if_exception_type(llm.error.APIError) | - retry_if_exception_type(llm.error.APIConnectionError) | - retry_if_exception_type(llm.error.RateLimitError) | - retry_if_exception_type(llm.error.ServiceUnavailableError)), + retry=( + retry_if_exception_type(llm.error.Timeout) + | retry_if_exception_type(llm.error.APIError) + | retry_if_exception_type(llm.error.APIConnectionError) + | retry_if_exception_type(llm.error.RateLimitError) + | retry_if_exception_type(llm.error.ServiceUnavailableError) + ), before_sleep=before_sleep_log(logger, logging.WARNING), ) @@ -76,16 +76,17 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any: reraise=True, stop=stop_after_attempt(embeddings.max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), - retry=(retry_if_exception_type(llm.error.Timeout) | - retry_if_exception_type(llm.error.APIError) | - retry_if_exception_type(llm.error.APIConnectionError) | - retry_if_exception_type(llm.error.RateLimitError) | - retry_if_exception_type(llm.error.ServiceUnavailableError)), + retry=( + retry_if_exception_type(llm.error.Timeout) + | retry_if_exception_type(llm.error.APIError) + | retry_if_exception_type(llm.error.APIConnectionError) + | retry_if_exception_type(llm.error.RateLimitError) + | retry_if_exception_type(llm.error.ServiceUnavailableError) + ), before_sleep=before_sleep_log(logger, logging.WARNING), ) def wrap(func: Callable) -> Callable: - async def wrapped_f(*args: Any, **kwargs: Any) -> Callable: async for _ in async_retrying: return await func(*args, **kwargs) @@ -117,8 +118,7 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: return _embed_with_retry(**kwargs) -async def async_embed_with_retry(embeddings: OpenAIEmbeddings, - **kwargs: Any) -> Any: +async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: """Use tenacity to retry the embedding call.""" @_async_retry_decorator(embeddings) @@ -225,11 +225,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): warnings.warn( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""") + Please confirm that {field_name} is what you intended.""" + ) extra[field_name] = values.pop(field_name) - invalid_model_kwargs = all_required_field_names.intersection( - extra.keys()) + invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " @@ -242,9 +242,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env(values, - "openai_api_key", - "OPENAI_API_KEY") + values["openai_api_key"] = get_from_dict_or_env( + values, "openai_api_key", "OPENAI_API_KEY" + ) values["openai_api_base"] = get_from_dict_or_env( values, "openai_api_base", @@ -284,8 +284,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings): values["client"] = llm.Embedding except ImportError: - raise ImportError("Could not import openai python package. " - "Please install it with `pip install openai`.") + raise ImportError( + "Could not import openai python package. " + "Please install it with `pip install openai`." + ) return values @property @@ -313,11 +315,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): return openai_args def _get_len_safe_embeddings( - self, - texts: List[str], - *, - engine: str, - chunk_size: Optional[int] = None) -> List[List[float]]: + self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None + ) -> List[List[float]]: embeddings: List[List[float]] = [[] for _ in range(len(texts))] try: import tiktoken @@ -325,7 +324,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to for OpenAIEmbeddings. " - "Please install it with `pip install tiktoken`.") + "Please install it with `pip install tiktoken`." + ) tokens = [] indices = [] @@ -333,8 +333,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning( - "Warning: model not found. Using cl100k_base encoding.") + logger.warning("Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) for i, text in enumerate(texts): @@ -348,7 +347,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): disallowed_special=self.disallowed_special, ) for j in range(0, len(token), self.embedding_ctx_length): - tokens.append(token[j:j + self.embedding_ctx_length]) + tokens.append(token[j : j + self.embedding_ctx_length]) indices.append(i) batched_embeddings: List[List[float]] = [] @@ -367,7 +366,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in _iter: response = embed_with_retry( self, - input=tokens[i:i + _chunk_size], + input=tokens[i : i + _chunk_size], **self._invocation_params, ) batched_embeddings.extend(r["embedding"] for r in response["data"]) @@ -385,11 +384,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): self, input="", **self._invocation_params, - )["data"][0]["embedding"] + )[ + "data" + ][0]["embedding"] else: - average = np.average(_result, - axis=0, - weights=num_tokens_in_batch[i]) + average = np.average(_result, axis=0, weights=num_tokens_in_batch[i]) embeddings[i] = (average / np.linalg.norm(average)).tolist() return embeddings @@ -397,11 +396,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): # please refer to # https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb async def _aget_len_safe_embeddings( - self, - texts: List[str], - *, - engine: str, - chunk_size: Optional[int] = None) -> List[List[float]]: + self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None + ) -> List[List[float]]: embeddings: List[List[float]] = [[] for _ in range(len(texts))] try: import tiktoken @@ -409,7 +405,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to for OpenAIEmbeddings. " - "Please install it with `pip install tiktoken`.") + "Please install it with `pip install tiktoken`." + ) tokens = [] indices = [] @@ -417,8 +414,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning( - "Warning: model not found. Using cl100k_base encoding.") + logger.warning("Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) for i, text in enumerate(texts): @@ -432,7 +428,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): disallowed_special=self.disallowed_special, ) for j in range(0, len(token), self.embedding_ctx_length): - tokens.append(token[j:j + self.embedding_ctx_length]) + tokens.append(token[j : j + self.embedding_ctx_length]) indices.append(i) batched_embeddings: List[List[float]] = [] @@ -440,7 +436,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in range(0, len(tokens), _chunk_size): response = await async_embed_with_retry( self, - input=tokens[i:i + _chunk_size], + input=tokens[i : i + _chunk_size], **self._invocation_params, ) batched_embeddings.extend(r["embedding"] for r in response["data"]) @@ -454,22 +450,22 @@ class OpenAIEmbeddings(BaseModel, Embeddings): for i in range(len(texts)): _result = results[i] if len(_result) == 0: - average = (await async_embed_with_retry( - self, - input="", - **self._invocation_params, - ))["data"][0]["embedding"] + average = ( + await async_embed_with_retry( + self, + input="", + **self._invocation_params, + ) + )["data"][0]["embedding"] else: - average = np.average(_result, - axis=0, - weights=num_tokens_in_batch[i]) + average = np.average(_result, axis=0, weights=num_tokens_in_batch[i]) embeddings[i] = (average / np.linalg.norm(average)).tolist() return embeddings - def embed_documents(self, - texts: List[str], - chunk_size: Optional[int] = 0) -> List[List[float]]: + def embed_documents( + self, texts: List[str], chunk_size: Optional[int] = 0 + ) -> List[List[float]]: """Call out to OpenAI's embedding endpoint for embedding search docs. Args: @@ -485,9 +481,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): return self._get_len_safe_embeddings(texts, engine=self.deployment) async def aembed_documents( - self, - texts: List[str], - chunk_size: Optional[int] = 0) -> List[List[float]]: + self, texts: List[str], chunk_size: Optional[int] = 0 + ) -> List[List[float]]: """Call out to OpenAI's embedding endpoint async for embedding search docs. Args: @@ -500,8 +495,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): """ # NOTE: to keep things simple, we assume the list may contain texts longer # than the maximum context and use length-safe embedding function. - return await self._aget_len_safe_embeddings(texts, - engine=self.deployment) + return await self._aget_len_safe_embeddings(texts, engine=self.deployment) def embed_query(self, text: str) -> List[float]: """Call out to OpenAI's embedding endpoint for embedding query text. diff --git a/swarms/models/openai_models.py b/swarms/models/openai_models.py index 128169a3..4b0cc91d 100644 --- a/swarms/models/openai_models.py +++ b/swarms/models/openai_models.py @@ -33,8 +33,9 @@ from langchain.utils.utils import build_extra_kwargs logger = logging.getLogger(__name__) -def update_token_usage(keys: Set[str], response: Dict[str, Any], - token_usage: Dict[str, Any]) -> None: +def update_token_usage( + keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any] +) -> None: """Update token usage.""" _keys_to_use = keys.intersection(response["usage"]) for _key in _keys_to_use: @@ -45,42 +46,44 @@ def update_token_usage(keys: Set[str], response: Dict[str, Any], def _stream_response_to_generation_chunk( - stream_response: Dict[str, Any],) -> GenerationChunk: + stream_response: Dict[str, Any], +) -> GenerationChunk: """Convert a stream response to a generation chunk.""" return GenerationChunk( text=stream_response["choices"][0]["text"], generation_info=dict( - finish_reason=stream_response["choices"][0].get( - "finish_reason", None), + finish_reason=stream_response["choices"][0].get("finish_reason", None), logprobs=stream_response["choices"][0].get("logprobs", None), ), ) -def _update_response(response: Dict[str, Any], - stream_response: Dict[str, Any]) -> None: +def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None: """Update response from the stream response.""" response["choices"][0]["text"] += stream_response["choices"][0]["text"] response["choices"][0]["finish_reason"] = stream_response["choices"][0].get( - "finish_reason", None) - response["choices"][0]["logprobs"] = stream_response["choices"][0][ - "logprobs"] + "finish_reason", None + ) + response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"] def _streaming_response_template() -> Dict[str, Any]: return { - "choices": [{ - "text": "", - "finish_reason": None, - "logprobs": None, - }] + "choices": [ + { + "text": "", + "finish_reason": None, + "logprobs": None, + } + ] } def _create_retry_decorator( llm: Union[BaseOpenAI, OpenAIChat], - run_manager: Optional[Union[AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun]] = None, + run_manager: Optional[ + Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] + ] = None, ) -> Callable[[Any], Any]: import openai @@ -91,9 +94,9 @@ def _create_retry_decorator( openai.error.RateLimitError, openai.error.ServiceUnavailableError, ] - return create_base_retry_decorator(error_types=errors, - max_retries=llm.max_retries, - run_manager=run_manager) + return create_base_retry_decorator( + error_types=errors, max_retries=llm.max_retries, run_manager=run_manager + ) def completion_with_retry( @@ -203,8 +206,7 @@ class BaseOpenAI(BaseLLM): API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.""" - def __new__(cls, - **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore + def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore """Initialize the OpenAI object.""" data.get("model_name", "") return super().__new__(cls) @@ -219,16 +221,17 @@ class BaseOpenAI(BaseLLM): """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) - values["model_kwargs"] = build_extra_kwargs(extra, values, - all_required_field_names) + values["model_kwargs"] = build_extra_kwargs( + extra, values, all_required_field_names + ) return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env(values, - "openai_api_key", - "OPENAI_API_KEY") + values["openai_api_key"] = get_from_dict_or_env( + values, "openai_api_key", "OPENAI_API_KEY" + ) values["openai_api_base"] = get_from_dict_or_env( values, "openai_api_base", @@ -252,8 +255,10 @@ class BaseOpenAI(BaseLLM): values["client"] = openai.Completion except ImportError: - raise ImportError("Could not import openai python package. " - "Please install it with `pip install openai`.") + raise ImportError( + "Could not import openai python package. " + "Please install it with `pip install openai`." + ) if values["streaming"] and values["n"] > 1: raise ValueError("Cannot stream results when n > 1.") if values["streaming"] and values["best_of"] > 1: @@ -290,10 +295,9 @@ class BaseOpenAI(BaseLLM): ) -> Iterator[GenerationChunk]: params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutates params - for stream_resp in completion_with_retry(self, - prompt=prompt, - run_manager=run_manager, - **params): + for stream_resp in completion_with_retry( + self, prompt=prompt, run_manager=run_manager, **params + ): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk if run_manager: @@ -302,7 +306,8 @@ class BaseOpenAI(BaseLLM): chunk=chunk, verbose=self.verbose, logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info else None, + if chunk.generation_info + else None, ) async def _astream( @@ -315,7 +320,8 @@ class BaseOpenAI(BaseLLM): params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutate params async for stream_resp in await acompletion_with_retry( - self, prompt=prompt, run_manager=run_manager, **params): + self, prompt=prompt, run_manager=run_manager, **params + ): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk if run_manager: @@ -324,7 +330,8 @@ class BaseOpenAI(BaseLLM): chunk=chunk, verbose=self.verbose, logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info else None, + if chunk.generation_info + else None, ) def _generate( @@ -360,32 +367,30 @@ class BaseOpenAI(BaseLLM): for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: - raise ValueError( - "Cannot stream results with multiple prompts.") + raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None - for chunk in self._stream(_prompts[0], stop, run_manager, - **kwargs): + for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs): if generation is None: generation = chunk else: generation += chunk assert generation is not None - choices.append({ - "text": - generation.text, - "finish_reason": - generation.generation_info.get("finish_reason") - if generation.generation_info else None, - "logprobs": - generation.generation_info.get("logprobs") - if generation.generation_info else None, - }) + choices.append( + { + "text": generation.text, + "finish_reason": generation.generation_info.get("finish_reason") + if generation.generation_info + else None, + "logprobs": generation.generation_info.get("logprobs") + if generation.generation_info + else None, + } + ) else: - response = completion_with_retry(self, - prompt=_prompts, - run_manager=run_manager, - **params) + response = completion_with_retry( + self, prompt=_prompts, run_manager=run_manager, **params + ) choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) return self.create_llm_result(choices, prompts, token_usage) @@ -409,32 +414,32 @@ class BaseOpenAI(BaseLLM): for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: - raise ValueError( - "Cannot stream results with multiple prompts.") + raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None - async for chunk in self._astream(_prompts[0], stop, run_manager, - **kwargs): + async for chunk in self._astream( + _prompts[0], stop, run_manager, **kwargs + ): if generation is None: generation = chunk else: generation += chunk assert generation is not None - choices.append({ - "text": - generation.text, - "finish_reason": - generation.generation_info.get("finish_reason") - if generation.generation_info else None, - "logprobs": - generation.generation_info.get("logprobs") - if generation.generation_info else None, - }) + choices.append( + { + "text": generation.text, + "finish_reason": generation.generation_info.get("finish_reason") + if generation.generation_info + else None, + "logprobs": generation.generation_info.get("logprobs") + if generation.generation_info + else None, + } + ) else: - response = await acompletion_with_retry(self, - prompt=_prompts, - run_manager=run_manager, - **params) + response = await acompletion_with_retry( + self, prompt=_prompts, run_manager=run_manager, **params + ) choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) return self.create_llm_result(choices, prompts, token_usage) @@ -448,35 +453,39 @@ class BaseOpenAI(BaseLLM): """Get the sub prompts for llm call.""" if stop is not None: if "stop" in params: - raise ValueError( - "`stop` found in both the input and default params.") + raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop if params["max_tokens"] == -1: if len(prompts) != 1: raise ValueError( - "max_tokens set to -1 not supported for multiple inputs.") + "max_tokens set to -1 not supported for multiple inputs." + ) params["max_tokens"] = self.max_tokens_for_prompt(prompts[0]) sub_prompts = [ - prompts[i:i + self.batch_size] + prompts[i : i + self.batch_size] for i in range(0, len(prompts), self.batch_size) ] return sub_prompts - def create_llm_result(self, choices: Any, prompts: List[str], - token_usage: Dict[str, int]) -> LLMResult: + def create_llm_result( + self, choices: Any, prompts: List[str], token_usage: Dict[str, int] + ) -> LLMResult: """Create the LLMResult from the choices and prompts.""" generations = [] for i, _ in enumerate(prompts): - sub_choices = choices[i * self.n:(i + 1) * self.n] - generations.append([ - Generation( - text=choice["text"], - generation_info=dict( - finish_reason=choice.get("finish_reason"), - logprobs=choice.get("logprobs"), - ), - ) for choice in sub_choices - ]) + sub_choices = choices[i * self.n : (i + 1) * self.n] + generations.append( + [ + Generation( + text=choice["text"], + generation_info=dict( + finish_reason=choice.get("finish_reason"), + logprobs=choice.get("logprobs"), + ), + ) + for choice in sub_choices + ] + ) llm_output = {"token_usage": token_usage, "model_name": self.model_name} return LLMResult(generations=generations, llm_output=llm_output) @@ -518,14 +527,14 @@ class BaseOpenAI(BaseLLM): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to calculate get_num_tokens. " - "Please install it with `pip install tiktoken`.") + "Please install it with `pip install tiktoken`." + ) model_name = self.tiktoken_model_name or self.model_name try: enc = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning( - "Warning: model not found. Using cl100k_base encoding.") + logger.warning("Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" enc = tiktoken.get_encoding(model) @@ -587,7 +596,8 @@ class BaseOpenAI(BaseLLM): if context_size is None: raise ValueError( f"Unknown model: {modelname}. Please provide a valid OpenAI model name." - "Known models are: " + ", ".join(model_token_mapping.keys())) + "Known models are: " + ", ".join(model_token_mapping.keys()) + ) return context_size @@ -665,15 +675,14 @@ class AzureOpenAI(BaseOpenAI): "OPENAI_API_VERSION", ) values["openai_api_type"] = get_from_dict_or_env( - values, "openai_api_type", "OPENAI_API_TYPE", "azure") + values, "openai_api_type", "OPENAI_API_TYPE", "azure" + ) return values @property def _identifying_params(self) -> Mapping[str, Any]: return { - **{ - "deployment_name": self.deployment_name - }, + **{"deployment_name": self.deployment_name}, **super()._identifying_params, } @@ -738,9 +747,7 @@ class OpenAIChat(BaseLLM): @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = { - field.alias for field in cls.__fields__.values() - } + all_required_field_names = {field.alias for field in cls.__fields__.values()} extra = values.get("model_kwargs", {}) for field_name in list(values): @@ -754,8 +761,9 @@ class OpenAIChat(BaseLLM): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - openai_api_key = get_from_dict_or_env(values, "openai_api_key", - "OPENAI_API_KEY") + openai_api_key = get_from_dict_or_env( + values, "openai_api_key", "OPENAI_API_KEY" + ) openai_api_base = get_from_dict_or_env( values, "openai_api_base", @@ -768,10 +776,9 @@ class OpenAIChat(BaseLLM): "OPENAI_PROXY", default="", ) - openai_organization = get_from_dict_or_env(values, - "openai_organization", - "OPENAI_ORGANIZATION", - default="") + openai_organization = get_from_dict_or_env( + values, "openai_organization", "OPENAI_ORGANIZATION", default="" + ) try: import openai @@ -786,15 +793,18 @@ class OpenAIChat(BaseLLM): "https": openai_proxy, } # type: ignore[assignment] # noqa: E501 except ImportError: - raise ImportError("Could not import openai python package. " - "Please install it with `pip install openai`.") + raise ImportError( + "Could not import openai python package. " + "Please install it with `pip install openai`." + ) try: values["client"] = openai.ChatCompletion except AttributeError: raise ValueError( "`openai` has no `ChatCompletion` attribute, this is likely " "due to an old version of the openai package. Try upgrading it " - "with `pip install --upgrade openai`.") + "with `pip install --upgrade openai`." + ) return values @property @@ -802,27 +812,18 @@ class OpenAIChat(BaseLLM): """Get the default parameters for calling OpenAI API.""" return self.model_kwargs - def _get_chat_params(self, - prompts: List[str], - stop: Optional[List[str]] = None) -> Tuple: + def _get_chat_params( + self, prompts: List[str], stop: Optional[List[str]] = None + ) -> Tuple: if len(prompts) > 1: raise ValueError( f"OpenAIChat currently only supports single prompt, got {prompts}" ) - messages = self.prefix_messages + [{ - "role": "user", - "content": prompts[0] - }] - params: Dict[str, Any] = { - **{ - "model": self.model_name - }, - **self._default_params - } + messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}] + params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params} if stop is not None: if "stop" in params: - raise ValueError( - "`stop` found in both the input and default params.") + raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop if params.get("max_tokens") == -1: # for ChatGPT api, omitting max_tokens is equivalent to having no limit @@ -838,10 +839,9 @@ class OpenAIChat(BaseLLM): ) -> Iterator[GenerationChunk]: messages, params = self._get_chat_params([prompt], stop) params = {**params, **kwargs, "stream": True} - for stream_resp in completion_with_retry(self, - messages=messages, - run_manager=run_manager, - **params): + for stream_resp in completion_with_retry( + self, messages=messages, run_manager=run_manager, **params + ): token = stream_resp["choices"][0]["delta"].get("content", "") chunk = GenerationChunk(text=token) yield chunk @@ -858,7 +858,8 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params([prompt], stop) params = {**params, **kwargs, "stream": True} async for stream_resp in await acompletion_with_retry( - self, messages=messages, run_manager=run_manager, **params): + self, messages=messages, run_manager=run_manager, **params + ): token = stream_resp["choices"][0]["delta"].get("content", "") chunk = GenerationChunk(text=token) yield chunk @@ -884,19 +885,17 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = completion_with_retry(self, - messages=messages, - run_manager=run_manager, - **params) + full_response = completion_with_retry( + self, messages=messages, run_manager=run_manager, **params + ) llm_output = { "token_usage": full_response["usage"], "model_name": self.model_name, } return LLMResult( - generations=[[ - Generation( - text=full_response["choices"][0]["message"]["content"]) - ]], + generations=[ + [Generation(text=full_response["choices"][0]["message"]["content"])] + ], llm_output=llm_output, ) @@ -909,8 +908,7 @@ class OpenAIChat(BaseLLM): ) -> LLMResult: if self.streaming: generation: Optional[GenerationChunk] = None - async for chunk in self._astream(prompts[0], stop, run_manager, - **kwargs): + async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs): if generation is None: generation = chunk else: @@ -920,19 +918,17 @@ class OpenAIChat(BaseLLM): messages, params = self._get_chat_params(prompts, stop) params = {**params, **kwargs} - full_response = await acompletion_with_retry(self, - messages=messages, - run_manager=run_manager, - **params) + full_response = await acompletion_with_retry( + self, messages=messages, run_manager=run_manager, **params + ) llm_output = { "token_usage": full_response["usage"], "model_name": self.model_name, } return LLMResult( - generations=[[ - Generation( - text=full_response["choices"][0]["message"]["content"]) - ]], + generations=[ + [Generation(text=full_response["choices"][0]["message"]["content"])] + ], llm_output=llm_output, ) @@ -957,7 +953,8 @@ class OpenAIChat(BaseLLM): raise ImportError( "Could not import tiktoken python package. " "This is needed in order to calculate get_num_tokens. " - "Please install it with `pip install tiktoken`.") + "Please install it with `pip install tiktoken`." + ) enc = tiktoken.encoding_for_model(self.model_name) return enc.encode( diff --git a/swarms/models/openai_tokenizer.py b/swarms/models/openai_tokenizer.py index 26ec9221..9ff1fa08 100644 --- a/swarms/models/openai_tokenizer.py +++ b/swarms/models/openai_tokenizer.py @@ -71,15 +71,16 @@ class OpenAITokenizer(BaseTokenizer): @property def max_tokens(self) -> int: - tokens = next(v for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() - if self.model.startswith(k)) + tokens = next( + v + for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() + if self.model.startswith(k) + ) offset = 0 if self.model in self.EMBEDDING_MODELS else self.TOKEN_OFFSET return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset - def count_tokens(self, - text: str | list, - model: Optional[str] = None) -> int: + def count_tokens(self, text: str | list, model: Optional[str] = None) -> int: """ Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb @@ -95,12 +96,12 @@ class OpenAITokenizer(BaseTokenizer): encoding = tiktoken.get_encoding("cl100k_base") if model in { - "gpt-3.5-turbo-0613", - "gpt-3.5-turbo-16k-0613", - "gpt-4-0314", - "gpt-4-32k-0314", - "gpt-4-0613", - "gpt-4-32k-0613", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", }: tokens_per_message = 3 tokens_per_name = 1 @@ -112,18 +113,21 @@ class OpenAITokenizer(BaseTokenizer): elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: logging.info( "gpt-3.5-turbo may update over time. Returning num tokens assuming" - " gpt-3.5-turbo-0613.") + " gpt-3.5-turbo-0613." + ) return self.count_tokens(text, model="gpt-3.5-turbo-0613") elif "gpt-4" in model: logging.info( "gpt-4 may update over time. Returning num tokens assuming" - " gpt-4-0613.") + " gpt-4-0613." + ) return self.count_tokens(text, model="gpt-4-0613") else: raise NotImplementedError( f"""token_count() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for - information on how messages are converted to tokens.""") + information on how messages are converted to tokens.""" + ) num_tokens = 0 @@ -140,5 +144,5 @@ class OpenAITokenizer(BaseTokenizer): return num_tokens else: return len( - self.encoding.encode(text, - allowed_special=set(self.stop_sequences))) + self.encoding.encode(text, allowed_special=set(self.stop_sequences)) + ) diff --git a/swarms/models/palm.py b/swarms/models/palm.py index c551c288..ec8aafd6 100644 --- a/swarms/models/palm.py +++ b/swarms/models/palm.py @@ -26,7 +26,8 @@ def _create_retry_decorator() -> Callable[[Any], Any]: except ImportError: raise ImportError( "Could not import google-api-core python package. " - "Please install it with `pip install google-api-core`.") + "Please install it with `pip install google-api-core`." + ) multiplier = 2 min_seconds = 1 @@ -36,15 +37,12 @@ def _create_retry_decorator() -> Callable[[Any], Any]: return retry( reraise=True, stop=stop_after_attempt(max_retries), - wait=wait_exponential(multiplier=multiplier, - min=min_seconds, - max=max_seconds), - retry=(retry_if_exception_type( - google.api_core.exceptions.ResourceExhausted) | - retry_if_exception_type( - google.api_core.exceptions.ServiceUnavailable) | - retry_if_exception_type( - google.api_core.exceptions.GoogleAPIError)), + wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds), + retry=( + retry_if_exception_type(google.api_core.exceptions.ResourceExhausted) + | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable) + | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError) + ), before_sleep=before_sleep_log(logger, logging.WARNING), ) @@ -66,8 +64,7 @@ def _strip_erroneous_leading_spaces(text: str) -> str: The PaLM API will sometimes erroneously return a single leading space in all lines > 1. This function strips that space. """ - has_leading_space = all( - not line or line[0] == " " for line in text.split("\n")[1:]) + has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:]) if has_leading_space: return text.replace("\n ", "\n") else: @@ -100,8 +97,9 @@ class GooglePalm(BaseLLM, BaseModel): @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate api key, python package exists.""" - google_api_key = get_from_dict_or_env(values, "google_api_key", - "GOOGLE_API_KEY") + google_api_key = get_from_dict_or_env( + values, "google_api_key", "GOOGLE_API_KEY" + ) try: import google.generativeai as genai @@ -109,12 +107,12 @@ class GooglePalm(BaseLLM, BaseModel): except ImportError: raise ImportError( "Could not import google-generativeai python package. " - "Please install it with `pip install google-generativeai`.") + "Please install it with `pip install google-generativeai`." + ) values["client"] = genai - if values["temperature"] is not None and not 0 <= values[ - "temperature"] <= 1: + if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: @@ -123,8 +121,7 @@ class GooglePalm(BaseLLM, BaseModel): if values["top_k"] is not None and values["top_k"] <= 0: raise ValueError("top_k must be positive") - if values["max_output_tokens"] is not None and values[ - "max_output_tokens"] <= 0: + if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0: raise ValueError("max_output_tokens must be greater than zero") return values diff --git a/swarms/models/pegasus.py b/swarms/models/pegasus.py index c2571f72..e388d40c 100644 --- a/swarms/models/pegasus.py +++ b/swarms/models/pegasus.py @@ -33,10 +33,9 @@ class PegasusEmbedding: """ - def __init__(self, - modality: str, - multi_process: bool = False, - n_processes: int = 4): + def __init__( + self, modality: str, multi_process: bool = False, n_processes: int = 4 + ): self.modality = modality self.multi_process = multi_process self.n_processes = n_processes @@ -44,7 +43,8 @@ class PegasusEmbedding: self.pegasus = Pegasus(modality, multi_process, n_processes) except Exception as e: logging.error( - f"Failed to initialize Pegasus with modality: {modality}: {e}") + f"Failed to initialize Pegasus with modality: {modality}: {e}" + ) raise def embed(self, data: Union[str, list[str]]): diff --git a/swarms/models/simple_ada.py b/swarms/models/simple_ada.py index fbb7c066..7eb923b4 100644 --- a/swarms/models/simple_ada.py +++ b/swarms/models/simple_ada.py @@ -21,4 +21,6 @@ def get_ada_embeddings(text: str, model: str = "text-embedding-ada-002"): return openai.Embedding.create( input=[text], model=model, - )["data"][0]["embedding"] + )["data"][ + 0 + ]["embedding"] diff --git a/swarms/models/speecht5.py b/swarms/models/speecht5.py index d1b476b9..e98036ac 100644 --- a/swarms/models/speecht5.py +++ b/swarms/models/speecht5.py @@ -90,17 +90,17 @@ class SpeechT5: self.processor = SpeechT5Processor.from_pretrained(self.model_name) self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name) self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name) - self.embeddings_dataset = load_dataset(self.dataset_name, - split="validation") + self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") def __call__(self, text: str, speaker_id: float = 7306): """Call the model on some text and return the speech.""" speaker_embedding = torch.tensor( - self.embeddings_dataset[speaker_id]["xvector"]).unsqueeze(0) + self.embeddings_dataset[speaker_id]["xvector"] + ).unsqueeze(0) inputs = self.processor(text=text, return_tensors="pt") - speech = self.model.generate_speech(inputs["input_ids"], - speaker_embedding, - vocoder=self.vocoder) + speech = self.model.generate_speech( + inputs["input_ids"], speaker_embedding, vocoder=self.vocoder + ) return speech def save_speech(self, speech, filename="speech.wav"): @@ -121,8 +121,7 @@ class SpeechT5: def set_embeddings_dataset(self, dataset_name): """Set the embeddings dataset to a new dataset.""" self.dataset_name = dataset_name - self.embeddings_dataset = load_dataset(self.dataset_name, - split="validation") + self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") # Feature 1: Get sampling rate def get_sampling_rate(self): diff --git a/swarms/models/timm.py b/swarms/models/timm.py index 5b17c76c..5d9b965a 100644 --- a/swarms/models/timm.py +++ b/swarms/models/timm.py @@ -50,8 +50,9 @@ class TimmModel: in_chans=model_info.in_chans, ) - def __call__(self, model_info: TimmModelInfo, - input_tensor: torch.Tensor) -> torch.Size: + def __call__( + self, model_info: TimmModelInfo, input_tensor: torch.Tensor + ) -> torch.Size: """ Create and run a model specified by `model_info` on `input_tensor`. diff --git a/swarms/models/trocr.py b/swarms/models/trocr.py index 1b9e72e7..f4a4156d 100644 --- a/swarms/models/trocr.py +++ b/swarms/models/trocr.py @@ -10,8 +10,9 @@ import requests class TrOCR: - - def __init__(self,): + def __init__( + self, + ): pass def __call__(self): diff --git a/swarms/models/vilt.py b/swarms/models/vilt.py index 4725a317..f95d265c 100644 --- a/swarms/models/vilt.py +++ b/swarms/models/vilt.py @@ -23,9 +23,11 @@ class Vilt: def __init__(self): self.processor = ViltProcessor.from_pretrained( - "dandelin/vilt-b32-finetuned-vqa") + "dandelin/vilt-b32-finetuned-vqa" + ) self.model = ViltForQuestionAnswering.from_pretrained( - "dandelin/vilt-b32-finetuned-vqa") + "dandelin/vilt-b32-finetuned-vqa" + ) def __call__(self, text: str, image_url: str): """ diff --git a/swarms/models/wizard_storytelling.py b/swarms/models/wizard_storytelling.py index 929fe10e..49ffb70d 100644 --- a/swarms/models/wizard_storytelling.py +++ b/swarms/models/wizard_storytelling.py @@ -33,8 +33,7 @@ class WizardLLMStoryTeller: def __init__( self, - model_id: - str = "TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", + model_id: str = "TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", device: str = None, max_length: int = 500, quantize: bool = False, @@ -45,8 +44,9 @@ class WizardLLMStoryTeller: decoding=False, ): self.logger = logging.getLogger(__name__) - self.device = (device if device else - ("cuda" if torch.cuda.is_available() else "cpu")) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -56,8 +56,9 @@ class WizardLLMStoryTeller: # self.log = Logging() if self.distributed: - assert (torch.cuda.device_count() > - 1), "You need more than 1 gpu for distributed processing" + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -73,7 +74,8 @@ class WizardLLMStoryTeller: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, quantization_config=bnb_config) + self.model_id, quantization_config=bnb_config + ) self.model # .to(self.device) except Exception as e: @@ -86,18 +88,20 @@ class WizardLLMStoryTeller: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = (BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config else None) + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, - quantization_config=bnb_config).to(self.device) + self.model_id, quantization_config=bnb_config + ).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error( - f"Failed to load the model or the tokenizer: {error}") + self.logger.error(f"Failed to load the model or the tokenizer: {error}") raise def run(self, prompt_text: str): @@ -116,8 +120,9 @@ class WizardLLMStoryTeller: max_length = self.max_length try: - inputs = self.tokenizer.encode(prompt_text, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) # self.log.start() @@ -126,26 +131,26 @@ class WizardLLMStoryTeller: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) @@ -169,8 +174,9 @@ class WizardLLMStoryTeller: max_length = self.max_ try: - inputs = self.tokenizer.encode(prompt_text, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) # self.log.start() @@ -179,26 +185,26 @@ class WizardLLMStoryTeller: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs diff --git a/swarms/models/yarn_mistral.py b/swarms/models/yarn_mistral.py index e3120e20..ebe107a2 100644 --- a/swarms/models/yarn_mistral.py +++ b/swarms/models/yarn_mistral.py @@ -44,8 +44,9 @@ class YarnMistral128: decoding=False, ): self.logger = logging.getLogger(__name__) - self.device = (device if device else - ("cuda" if torch.cuda.is_available() else "cpu")) + self.device = ( + device if device else ("cuda" if torch.cuda.is_available() else "cpu") + ) self.model_id = model_id self.max_length = max_length self.verbose = verbose @@ -55,8 +56,9 @@ class YarnMistral128: # self.log = Logging() if self.distributed: - assert (torch.cuda.device_count() > - 1), "You need more than 1 gpu for distributed processing" + assert ( + torch.cuda.device_count() > 1 + ), "You need more than 1 gpu for distributed processing" bnb_config = None if quantize: @@ -91,18 +93,20 @@ class YarnMistral128: try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) - bnb_config = (BitsAndBytesConfig(**self.quantization_config) - if self.quantization_config else None) + bnb_config = ( + BitsAndBytesConfig(**self.quantization_config) + if self.quantization_config + else None + ) self.model = AutoModelForCausalLM.from_pretrained( - self.model_id, - quantization_config=bnb_config).to(self.device) + self.model_id, quantization_config=bnb_config + ).to(self.device) if self.distributed: self.model = DDP(self.model) except Exception as error: - self.logger.error( - f"Failed to load the model or the tokenizer: {error}") + self.logger.error(f"Failed to load the model or the tokenizer: {error}") raise def run(self, prompt_text: str): @@ -121,8 +125,9 @@ class YarnMistral128: max_length = self.max_length try: - inputs = self.tokenizer.encode(prompt_text, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) # self.log.start() @@ -131,26 +136,26 @@ class YarnMistral128: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs return self.tokenizer.decode(outputs[0], skip_special_tokens=True) @@ -197,8 +202,9 @@ class YarnMistral128: max_length = self.max_ try: - inputs = self.tokenizer.encode(prompt_text, - return_tensors="pt").to(self.device) + inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to( + self.device + ) # self.log.start() @@ -207,26 +213,26 @@ class YarnMistral128: for _ in range(max_length): output_sequence = [] - outputs = self.model.generate(inputs, - max_length=len(inputs) + - 1, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=len(inputs) + 1, do_sample=True + ) output_tokens = outputs[0][-1] output_sequence.append(output_tokens.item()) # print token in real-time print( - self.tokenizer.decode([output_tokens], - skip_special_tokens=True), + self.tokenizer.decode( + [output_tokens], skip_special_tokens=True + ), end="", flush=True, ) inputs = outputs else: with torch.no_grad(): - outputs = self.model.generate(inputs, - max_length=max_length, - do_sample=True) + outputs = self.model.generate( + inputs, max_length=max_length, do_sample=True + ) del inputs diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index 0ed23f19..4fca5211 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -28,8 +28,7 @@ class Zephyr: model_name: str = "HuggingFaceH4/zephyr-7b-alpha", tokenize: bool = False, add_generation_prompt: bool = True, - system_prompt: - str = "You are a friendly chatbot who always responds in the style of a pirate", + system_prompt: str = "You are a friendly chatbot who always responds in the style of a pirate", max_new_tokens: int = 300, temperature: float = 0.5, top_k: float = 50, diff --git a/swarms/prompts/agent_output_parser.py b/swarms/prompts/agent_output_parser.py index e00db22d..27f8ac24 100644 --- a/swarms/prompts/agent_output_parser.py +++ b/swarms/prompts/agent_output_parser.py @@ -24,8 +24,9 @@ class AgentOutputParser(BaseAgentOutputParser): @staticmethod def _preprocess_json_input(input_str: str) -> str: - corrected_str = re.sub(r'(? dict: diff --git a/swarms/prompts/agent_prompt.py b/swarms/prompts/agent_prompt.py index aa84ebf8..c4897193 100644 --- a/swarms/prompts/agent_prompt.py +++ b/swarms/prompts/agent_prompt.py @@ -13,23 +13,13 @@ class PromptGenerator: self.performance_evaluation: List[str] = [] self.response_format = { "thoughts": { - "text": - "thought", - "reasoning": - "reasoning", - "plan": - "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": - "constructive self-criticism", - "speak": - "thoughts summary to say to user", - }, - "command": { - "name": "command name", - "args": { - "arg name": "value" - } + "text": "thought", + "reasoning": "reasoning", + "plan": "- short bulleted\n- list that conveys\n- long-term plan", + "criticism": "constructive self-criticism", + "speak": "thoughts summary to say to user", }, + "command": {"name": "command name", "args": {"arg name": "value"}}, } def add_constraint(self, constraint: str) -> None: @@ -82,6 +72,7 @@ class PromptGenerator: f"Performance Evaluation:\n{''.join(self.performance_evaluation)}\n\n" "You should only respond in JSON format as described below " f"\nResponse Format: \n{formatted_response_format} " - "\nEnsure the response can be parsed by Python json.loads") + "\nEnsure the response can be parsed by Python json.loads" + ) return prompt_string diff --git a/swarms/prompts/agent_prompts.py b/swarms/prompts/agent_prompts.py index 3de5bcb2..8d145fc0 100644 --- a/swarms/prompts/agent_prompts.py +++ b/swarms/prompts/agent_prompts.py @@ -7,21 +7,25 @@ def generate_agent_role_prompt(agent): "Finance Agent": ( "You are a seasoned finance analyst AI assistant. Your primary goal is to" " compose comprehensive, astute, impartial, and methodically arranged" - " financial reports based on provided data and trends."), + " financial reports based on provided data and trends." + ), "Travel Agent": ( "You are a world-travelled AI tour guide assistant. Your main purpose is to" " draft engaging, insightful, unbiased, and well-structured travel reports" " on given locations, including history, attractions, and cultural" - " insights."), + " insights." + ), "Academic Research Agent": ( "You are an AI academic research assistant. Your primary responsibility is" " to create thorough, academically rigorous, unbiased, and systematically" " organized reports on a given research topic, following the standards of" - " scholarly work."), + " scholarly work." + ), "Default Agent": ( "You are an AI critical thinker research assistant. Your sole purpose is to" " write well written, critically acclaimed, objective and structured" - " reports on given text."), + " reports on given text." + ), } return prompts.get(agent, "No such agent") @@ -40,7 +44,8 @@ def generate_report_prompt(question, research_summary): " focus on the answer to the question, should be well structured, informative," " in depth, with facts and numbers if available, a minimum of 1,200 words and" " with markdown syntax and apa format. Write all source urls at the end of the" - " report in apa format") + " report in apa format" + ) def generate_search_queries_prompt(question): @@ -52,7 +57,8 @@ def generate_search_queries_prompt(question): return ( "Write 4 google search queries to search online that form an objective opinion" f' from the following: "{question}"You must respond with a list of strings in' - ' the following format: ["query 1", "query 2", "query 3", "query 4"]') + ' the following format: ["query 1", "query 2", "query 3", "query 4"]' + ) def generate_resource_report_prompt(question, research_summary): @@ -74,7 +80,8 @@ def generate_resource_report_prompt(question, research_summary): " significance of each source. Ensure that the report is well-structured," " informative, in-depth, and follows Markdown syntax. Include relevant facts," " figures, and numbers whenever available. The report should have a minimum" - " length of 1,200 words.") + " length of 1,200 words." + ) def generate_outline_report_prompt(question, research_summary): @@ -91,7 +98,8 @@ def generate_outline_report_prompt(question, research_summary): " research report, including the main sections, subsections, and key points to" " be covered. The research report should be detailed, informative, in-depth," " and a minimum of 1,200 words. Use appropriate Markdown syntax to format the" - " outline and ensure readability.") + " outline and ensure readability." + ) def generate_concepts_prompt(question, research_summary): @@ -106,7 +114,8 @@ def generate_concepts_prompt(question, research_summary): " main concepts to learn for a research report on the following question or" f' topic: "{question}". The outline should provide a well-structured' " frameworkYou must respond with a list of strings in the following format:" - ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]') + ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]' + ) def generate_lesson_prompt(concept): @@ -122,7 +131,8 @@ def generate_lesson_prompt(concept): f"generate a comprehensive lesson about {concept} in Markdown syntax. This" f" should include the definitionof {concept}, its historical background and" " development, its applications or uses in differentfields, and notable events" - f" or facts related to {concept}.") + f" or facts related to {concept}." + ) return prompt diff --git a/swarms/prompts/base.py b/swarms/prompts/base.py index 8bb77236..54a0bc3f 100644 --- a/swarms/prompts/base.py +++ b/swarms/prompts/base.py @@ -11,9 +11,9 @@ if TYPE_CHECKING: from langchain.prompts.chat import ChatPromptTemplate -def get_buffer_string(messages: Sequence[BaseMessage], - human_prefix: str = "Human", - ai_prefix: str = "AI") -> str: +def get_buffer_string( + messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI" +) -> str: """Convert sequence of Messages to strings and concatenate them into one string. Args: @@ -88,9 +88,9 @@ class BaseMessage(Serializable): class BaseMessageChunk(BaseMessage): - - def _merge_kwargs_dict(self, left: Dict[str, Any], - right: Dict[str, Any]) -> Dict[str, Any]: + def _merge_kwargs_dict( + self, left: Dict[str, Any], right: Dict[str, Any] + ) -> Dict[str, Any]: """Merge additional_kwargs from another BaseMessageChunk into this one.""" merged = left.copy() for k, v in right.items(): @@ -99,7 +99,8 @@ class BaseMessageChunk(BaseMessage): elif not isinstance(merged[k], type(v)): raise ValueError( f'additional_kwargs["{k}"] already exists in this message,' - " but with a different type.") + " but with a different type." + ) elif isinstance(merged[k], str): merged[k] += v elif isinstance(merged[k], dict): @@ -118,12 +119,15 @@ class BaseMessageChunk(BaseMessage): return self.__class__( content=self.content + other.content, additional_kwargs=self._merge_kwargs_dict( - self.additional_kwargs, other.additional_kwargs), + self.additional_kwargs, other.additional_kwargs + ), ) else: - raise TypeError('unsupported operand type(s) for +: "' - f"{self.__class__.__name__}" - f'" and "{other.__class__.__name__}"') + raise TypeError( + 'unsupported operand type(s) for +: "' + f"{self.__class__.__name__}" + f'" and "{other.__class__.__name__}"' + ) class HumanMessage(BaseMessage): diff --git a/swarms/prompts/chat_prompt.py b/swarms/prompts/chat_prompt.py index 5f48488f..b0330e24 100644 --- a/swarms/prompts/chat_prompt.py +++ b/swarms/prompts/chat_prompt.py @@ -66,10 +66,9 @@ class SystemMessage(Message): of input messages. """ - def __init__(self, - content: str, - role: str = "System", - additional_kwargs: Dict = None): + def __init__( + self, content: str, role: str = "System", additional_kwargs: Dict = None + ): super().__init__(content, role, additional_kwargs) def get_type(self) -> str: @@ -107,9 +106,9 @@ class ChatMessage(Message): return "chat" -def get_buffer_string(messages: Sequence[Message], - human_prefix: str = "Human", - ai_prefix: str = "AI") -> str: +def get_buffer_string( + messages: Sequence[Message], human_prefix: str = "Human", ai_prefix: str = "AI" +) -> str: string_messages = [] for m in messages: message = f"{m.role}: {m.content}" diff --git a/swarms/prompts/debate.py b/swarms/prompts/debate.py index 5a6be762..a11c7af4 100644 --- a/swarms/prompts/debate.py +++ b/swarms/prompts/debate.py @@ -38,6 +38,7 @@ def debate_monitor(game_description, word_limit, character_names): return prompt -def generate_character_header(game_description, topic, character_name, - character_description): +def generate_character_header( + game_description, topic, character_name, character_description +): pass diff --git a/swarms/prompts/multi_modal_prompts.py b/swarms/prompts/multi_modal_prompts.py index dc2bccd5..b552b68d 100644 --- a/swarms/prompts/multi_modal_prompts.py +++ b/swarms/prompts/multi_modal_prompts.py @@ -1,6 +1,7 @@ ERROR_PROMPT = ( "An error has occurred for the following text: \n{promptedQuery} Please explain" - " this error.\n {e}") + " this error.\n {e}" +) IMAGE_PROMPT = """ provide a figure named {filename}. The description is: {description}. diff --git a/swarms/prompts/python.py b/swarms/prompts/python.py index cd34e9bd..9d1f4a1e 100644 --- a/swarms/prompts/python.py +++ b/swarms/prompts/python.py @@ -3,25 +3,30 @@ PY_REFLEXION_COMPLETION_INSTRUCTION = ( "You are a Python writing assistant. You will be given your past function" " implementation, a series of unit tests, and a hint to change the implementation" " appropriately. Write your full implementation (restate the function" - " signature).\n\n-----") + " signature).\n\n-----" +) PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = ( "You are a Python writing assistant. You will be given a function implementation" " and a series of unit tests. Your goal is to write a few sentences to explain why" " your implementation is wrong as indicated by the tests. You will need this as a" " hint when you try again later. Only provide the few sentence description in your" - " answer, not the implementation.\n\n-----") + " answer, not the implementation.\n\n-----" +) USE_PYTHON_CODEBLOCK_INSTRUCTION = ( "Use a Python code block to write your response. For" - " example:\n```python\nprint('Hello world!')\n```") + " example:\n```python\nprint('Hello world!')\n```" +) PY_SIMPLE_CHAT_INSTRUCTION = ( "You are an AI that only responds with python code, NOT ENGLISH. You will be given" " a function signature and its docstring by the user. Write your full" - " implementation (restate the function signature).") + " implementation (restate the function signature)." +) PY_SIMPLE_CHAT_INSTRUCTION_V2 = ( "You are an AI that only responds with only python code. You will be given a" " function signature and its docstring by the user. Write your full implementation" - " (restate the function signature).") + " (restate the function signature)." +) PY_REFLEXION_CHAT_INSTRUCTION = ( "You are an AI Python assistant. You will be given your past function" " implementation, a series of unit tests, and a hint to change the implementation" @@ -31,7 +36,8 @@ PY_REFLEXION_CHAT_INSTRUCTION_V2 = ( "You are an AI Python assistant. You will be given your previous implementation of" " a function, a series of unit tests results, and your self-reflection on your" " previous implementation. Write your full implementation (restate the function" - " signature).") + " signature)." +) PY_REFLEXION_FEW_SHOT_ADD = '''Example 1: [previous impl]: ```python @@ -169,14 +175,16 @@ PY_SELF_REFLECTION_CHAT_INSTRUCTION = ( " implementation and a series of unit tests. Your goal is to write a few sentences" " to explain why your implementation is wrong as indicated by the tests. You will" " need this as a hint when you try again later. Only provide the few sentence" - " description in your answer, not the implementation.") + " description in your answer, not the implementation." +) PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = ( "You are a Python programming assistant. You will be given a function" " implementation and a series of unit test results. Your goal is to write a few" " sentences to explain why your implementation is wrong as indicated by the tests." " You will need this as guidance when you try again later. Only provide the few" " sentence description in your answer, not the implementation. You will be given a" - " few examples by the user.") + " few examples by the user." +) PY_SELF_REFLECTION_FEW_SHOT = """Example 1: [function impl]: ```python diff --git a/swarms/prompts/sales.py b/swarms/prompts/sales.py index 6660e084..4f04f7fc 100644 --- a/swarms/prompts/sales.py +++ b/swarms/prompts/sales.py @@ -3,29 +3,36 @@ conversation_stages = { "Introduction: Start the conversation by introducing yourself and your company." " Be polite and respectful while keeping the tone of the conversation" " professional. Your greeting should be welcoming. Always clarify in your" - " greeting the reason why you are contacting the prospect."), + " greeting the reason why you are contacting the prospect." + ), "2": ( "Qualification: Qualify the prospect by confirming if they are the right person" " to talk to regarding your product/service. Ensure that they have the" - " authority to make purchasing decisions."), + " authority to make purchasing decisions." + ), "3": ( "Value proposition: Briefly explain how your product/service can benefit the" " prospect. Focus on the unique selling points and value proposition of your" - " product/service that sets it apart from competitors."), + " product/service that sets it apart from competitors." + ), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" - " pain points. Listen carefully to their responses and take notes."), - "5": ("Solution presentation: Based on the prospect's needs, present your" - " product/service as the solution that can address their pain points." - ), - "6": - ("Objection handling: Address any objections that the prospect may have" - " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims."), + " pain points. Listen carefully to their responses and take notes." + ), + "5": ( + "Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": ( + "Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims." + ), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has been" - " discussed and reiterate the benefits."), + " discussed and reiterate the benefits." + ), } SALES_AGENT_TOOLS_PROMPT = """ diff --git a/swarms/prompts/sales_prompts.py b/swarms/prompts/sales_prompts.py index ce5303b3..3f2b9f2b 100644 --- a/swarms/prompts/sales_prompts.py +++ b/swarms/prompts/sales_prompts.py @@ -49,27 +49,34 @@ conversation_stages = { "Introduction: Start the conversation by introducing yourself and your company." " Be polite and respectful while keeping the tone of the conversation" " professional. Your greeting should be welcoming. Always clarify in your" - " greeting the reason why you are contacting the prospect."), + " greeting the reason why you are contacting the prospect." + ), "2": ( "Qualification: Qualify the prospect by confirming if they are the right person" " to talk to regarding your product/service. Ensure that they have the" - " authority to make purchasing decisions."), + " authority to make purchasing decisions." + ), "3": ( "Value proposition: Briefly explain how your product/service can benefit the" " prospect. Focus on the unique selling points and value proposition of your" - " product/service that sets it apart from competitors."), + " product/service that sets it apart from competitors." + ), "4": ( "Needs analysis: Ask open-ended questions to uncover the prospect's needs and" - " pain points. Listen carefully to their responses and take notes."), - "5": ("Solution presentation: Based on the prospect's needs, present your" - " product/service as the solution that can address their pain points." - ), - "6": - ("Objection handling: Address any objections that the prospect may have" - " regarding your product/service. Be prepared to provide evidence or" - " testimonials to support your claims."), + " pain points. Listen carefully to their responses and take notes." + ), + "5": ( + "Solution presentation: Based on the prospect's needs, present your" + " product/service as the solution that can address their pain points." + ), + "6": ( + "Objection handling: Address any objections that the prospect may have" + " regarding your product/service. Be prepared to provide evidence or" + " testimonials to support your claims." + ), "7": ( "Close: Ask for the sale by proposing a next step. This could be a demo, a" " trial or a meeting with decision-makers. Ensure to summarize what has been" - " discussed and reiterate the benefits."), + " discussed and reiterate the benefits." + ), } diff --git a/swarms/schemas/typings.py b/swarms/schemas/typings.py index f59b16f7..2d848736 100644 --- a/swarms/schemas/typings.py +++ b/swarms/schemas/typings.py @@ -18,11 +18,13 @@ class ChatbotError(Exception): def __init__(self, *args: object) -> None: if SUPPORT_ADD_NOTES: - super().add_note(( - "Please check that the input is correct, or you can resolve this" - " issue by filing an issue"),) super().add_note( - "Project URL: https://github.com/acheong08/ChatGPT") + ( + "Please check that the input is correct, or you can resolve this" + " issue by filing an issue" + ), + ) + super().add_note("Project URL: https://github.com/acheong08/ChatGPT") super().__init__(*args) diff --git a/swarms/structs/document.py b/swarms/structs/document.py index 505df6ae..b87d3d91 100644 --- a/swarms/structs/document.py +++ b/swarms/structs/document.py @@ -63,8 +63,9 @@ class BaseDocumentTransformer(ABC): """ # noqa: E501 @abstractmethod - def transform_documents(self, documents: Sequence[Document], - **kwargs: Any) -> Sequence[Document]: + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: """Transform a list of documents. Args: @@ -74,8 +75,9 @@ class BaseDocumentTransformer(ABC): A list of transformed Documents. """ - async def atransform_documents(self, documents: Sequence[Document], - **kwargs: Any) -> Sequence[Document]: + async def atransform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: """Asynchronously transform a list of documents. Args: @@ -85,4 +87,5 @@ class BaseDocumentTransformer(ABC): A list of transformed Documents. """ return await asyncio.get_running_loop().run_in_executor( - None, partial(self.transform_documents, **kwargs), documents) + None, partial(self.transform_documents, **kwargs), documents + ) diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py index a3633a2c..8d89fd89 100644 --- a/swarms/structs/flow.py +++ b/swarms/structs/flow.py @@ -100,7 +100,7 @@ class Flow: self, llm: Any, # template: str, - max_loops = 5, + max_loops=5, stopping_condition: Optional[Callable[[str], bool]] = None, loop_interval: int = 1, retry_attempts: int = 3, @@ -188,7 +188,8 @@ class Flow: value = self.llm.__dict__.get(name, "Unknown") params_str_list.append( - f" {name.capitalize().replace('_', ' ')}: {value}") + f" {name.capitalize().replace('_', ' ')}: {value}" + ) return "\n".join(params_str_list) @@ -196,7 +197,7 @@ class Flow: """ Take the history and truncate it to fit into the model context length """ - truncated_history = self.memory[-1][-self.context_length:] + truncated_history = self.memory[-1][-self.context_length :] self.memory[-1] = truncated_history def add_task_to_memory(self, task: str): @@ -246,7 +247,8 @@ class Flow: ---------------------------------------- """, "green", - )) + ) + ) # print(dashboard) @@ -256,17 +258,18 @@ class Flow: print(colored("Initializing Autonomous Agent...", "yellow")) # print(colored("Loading modules...", "yellow")) # print(colored("Modules loaded successfully.", "green")) - print(colored("Autonomous Agent Activated.", "cyan", - attrs=["bold"])) - print(colored("All systems operational. Executing task...", - "green")) + print(colored("Autonomous Agent Activated.", "cyan", attrs=["bold"])) + print(colored("All systems operational. Executing task...", "green")) except Exception as error: print( colored( - ("Error activating autonomous agent. Try optimizing your" - " parameters..."), + ( + "Error activating autonomous agent. Try optimizing your" + " parameters..." + ), "red", - )) + ) + ) print(error) def run(self, task: str, **kwargs): @@ -296,7 +299,7 @@ class Flow: loop_count = 0 # for i in range(self.max_loops): - while self.max_loops == 'auto' or loop_count < self.max_loops: + while self.max_loops == "auto" or loop_count < self.max_loops: loop_count += 1 print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue")) print("\n") @@ -315,8 +318,7 @@ class Flow: while attempt < self.retry_attempts: try: response = self.llm( - task - **kwargs, + task**kwargs, ) if self.interactive: print(f"AI: {response}") @@ -344,7 +346,7 @@ class Flow: if self.return_history: return response, history - return response + return response async def arun(self, task: str, **kwargs): """ @@ -373,7 +375,7 @@ class Flow: loop_count = 0 # for i in range(self.max_loops): - while self.max_loops == 'auto' or loop_count < self.max_loops: + while self.max_loops == "auto" or loop_count < self.max_loops: loop_count += 1 print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue")) print("\n") @@ -392,8 +394,7 @@ class Flow: while attempt < self.retry_attempts: try: response = self.llm( - task - **kwargs, + task**kwargs, ) if self.interactive: print(f"AI: {response}") @@ -421,7 +422,7 @@ class Flow: if self.return_history: return response, history - return response + return response def _run(self, **kwargs: Any) -> str: """Generate a result using the provided keyword args.""" @@ -460,9 +461,7 @@ class Flow: Args: tasks (List[str]): A list of tasks to run. """ - task_coroutines = [ - self.run_async(task, **kwargs) for task in tasks - ] + task_coroutines = [self.run_async(task, **kwargs) for task in tasks] completed_tasks = await asyncio.gather(*task_coroutines) return completed_tasks @@ -575,9 +574,7 @@ class Flow: import boto3 s3 = boto3.client("s3") - s3.put_object(Bucket=bucket_name, - Key=object_name, - Body=json.dumps(self.memory)) + s3.put_object(Bucket=bucket_name, Key=object_name, Body=json.dumps(self.memory)) print(f"Backed up memory to S3: {bucket_name}/{object_name}") def analyze_feedback(self): @@ -681,7 +678,7 @@ class Flow: def get_llm_params(self): """ Extracts and returns the parameters of the llm object for serialization. - It assumes that the llm object has an __init__ method + It assumes that the llm object has an __init__ method with parameters that can be used to recreate it. """ if not hasattr(self.llm, "__init__"): @@ -697,8 +694,8 @@ class Flow: if hasattr(self.llm, name): value = getattr(self.llm, name) if isinstance( - value, - (str, int, float, bool, list, dict, tuple, type(None))): + value, (str, int, float, bool, list, dict, tuple, type(None)) + ): llm_params[name] = value else: llm_params[name] = str( @@ -758,10 +755,7 @@ class Flow: print(f"Flow state loaded from {file_path}") - def retry_on_failure(self, - function, - retries: int = 3, - retry_delay: int = 1): + def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1): """Retry wrapper for LLM calls.""" attempt = 0 while attempt < retries: diff --git a/swarms/structs/nonlinear_workflow.py b/swarms/structs/nonlinear_workflow.py index 140c0d7b..2357f614 100644 --- a/swarms/structs/nonlinear_workflow.py +++ b/swarms/structs/nonlinear_workflow.py @@ -8,10 +8,9 @@ class Task: Task is a unit of work that can be executed by an agent """ - def __init__(self, - id: str, - parents: List["Task"] = None, - children: List["Task"] = None): + def __init__( + self, id: str, parents: List["Task"] = None, children: List["Task"] = None + ): self.id = id self.parents = parents self.children = children @@ -80,8 +79,7 @@ class NonLinearWorkflow: for task in ordered_tasks: if task.can_execute: - future = self.executor.submit(self.agents.run, - task.task_string) + future = self.executor.submit(self.agents.run, task.task_string) futures_list[future] = task for future in as_completed(futures_list): @@ -97,8 +95,7 @@ class NonLinearWorkflow: def to_graph(self) -> Dict[str, set[str]]: """Convert the workflow to a graph""" graph = { - task.id: set(child.id for child in task.children) - for task in self.tasks + task.id: set(child.id for child in task.children) for task in self.tasks } return graph diff --git a/swarms/structs/sequential_workflow.py b/swarms/structs/sequential_workflow.py index 8dd5abbd..8c7d9760 100644 --- a/swarms/structs/sequential_workflow.py +++ b/swarms/structs/sequential_workflow.py @@ -61,12 +61,13 @@ class Task: if isinstance(self.flow, Flow): # Add a prompt to notify the Flow of the sequential workflow if "prompt" in self.kwargs: - self.kwargs["prompt"] += (f"\n\nPrevious output: {self.result}" - if self.result else "") + self.kwargs["prompt"] += ( + f"\n\nPrevious output: {self.result}" if self.result else "" + ) else: self.kwargs["prompt"] = f"Main task: {self.description}" + ( - f"\n\nPrevious output: {self.result}" - if self.result else "") + f"\n\nPrevious output: {self.result}" if self.result else "" + ) self.result = self.flow.run(*self.args, **self.kwargs) else: self.result = self.flow(*self.args, **self.kwargs) @@ -110,8 +111,7 @@ class SequentialWorkflow: restore_state_filepath: Optional[str] = None dashboard: bool = False - def add(self, task: str, flow: Union[Callable, Flow], *args, - **kwargs) -> None: + def add(self, task: str, flow: Union[Callable, Flow], *args, **kwargs) -> None: """ Add a task to the workflow. @@ -127,7 +127,8 @@ class SequentialWorkflow: # Append the task to the tasks list self.tasks.append( - Task(description=task, flow=flow, args=list(args), kwargs=kwargs)) + Task(description=task, flow=flow, args=list(args), kwargs=kwargs) + ) def reset_workflow(self) -> None: """Resets the workflow by clearing the results of each task.""" @@ -179,9 +180,8 @@ class SequentialWorkflow: raise ValueError(f"Task {task_description} not found in workflow.") def save_workflow_state( - self, - filepath: Optional[str] = "sequential_workflow_state.json", - **kwargs) -> None: + self, filepath: Optional[str] = "sequential_workflow_state.json", **kwargs + ) -> None: """ Saves the workflow state to a json file. @@ -202,13 +202,16 @@ class SequentialWorkflow: with open(filepath, "w") as f: # Saving the state as a json for simplicuty state = { - "tasks": [{ - "description": task.description, - "args": task.args, - "kwargs": task.kwargs, - "result": task.result, - "history": task.history, - } for task in self.tasks], + "tasks": [ + { + "description": task.description, + "args": task.args, + "kwargs": task.kwargs, + "result": task.result, + "history": task.history, + } + for task in self.tasks + ], "max_loops": self.max_loops, } json.dump(state, f, indent=4) @@ -220,7 +223,8 @@ class SequentialWorkflow: Sequential Workflow Initializing...""", "green", attrs=["bold", "underline"], - )) + ) + ) def workflow_dashboard(self, **kwargs) -> None: """ @@ -259,7 +263,8 @@ class SequentialWorkflow: """, "cyan", attrs=["bold", "underline"], - )) + ) + ) def workflow_shutdown(self, **kwargs) -> None: print( @@ -268,7 +273,8 @@ class SequentialWorkflow: Sequential Workflow Shutdown...""", "red", attrs=["bold", "underline"], - )) + ) + ) def add_objective_to_workflow(self, task: str, **kwargs) -> None: print( @@ -277,7 +283,8 @@ class SequentialWorkflow: Adding Objective to Workflow...""", "green", attrs=["bold", "underline"], - )) + ) + ) task = Task( description=task, @@ -342,12 +349,13 @@ class SequentialWorkflow: if "task" not in task.kwargs: raise ValueError( "The 'task' argument is required for the Flow flow" - f" execution in '{task.description}'") + f" execution in '{task.description}'" + ) # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") - task.result = task.flow.run(flow_task_arg, - *task.args, - **task.kwargs) + task.result = task.flow.run( + flow_task_arg, *task.args, **task.kwargs + ) else: # If it's not a Flow instance, call the flow directly task.result = task.flow(*task.args, **task.kwargs) @@ -365,17 +373,19 @@ class SequentialWorkflow: # Autosave the workflow state if self.autosave: - self.save_workflow_state( - "sequential_workflow_state.json") + self.save_workflow_state("sequential_workflow_state.json") except Exception as e: print( colored( - (f"Error initializing the Sequential workflow: {e} try" - " optimizing your inputs like the flow class and task" - " description"), + ( + f"Error initializing the Sequential workflow: {e} try" + " optimizing your inputs like the flow class and task" + " description" + ), "red", attrs=["bold", "underline"], - )) + ) + ) async def arun(self) -> None: """ @@ -395,11 +405,13 @@ class SequentialWorkflow: if "task" not in task.kwargs: raise ValueError( "The 'task' argument is required for the Flow flow" - f" execution in '{task.description}'") + f" execution in '{task.description}'" + ) # Separate the 'task' argument from other kwargs flow_task_arg = task.kwargs.pop("task") task.result = await task.flow.arun( - flow_task_arg, *task.args, **task.kwargs) + flow_task_arg, *task.args, **task.kwargs + ) else: # If it's not a Flow instance, call the flow directly task.result = await task.flow(*task.args, **task.kwargs) @@ -417,5 +429,4 @@ class SequentialWorkflow: # Autosave the workflow state if self.autosave: - self.save_workflow_state( - "sequential_workflow_state.json") + self.save_workflow_state("sequential_workflow_state.json") diff --git a/swarms/structs/task.py b/swarms/structs/task.py index 6824bf0e..80f95d4d 100644 --- a/swarms/structs/task.py +++ b/swarms/structs/task.py @@ -13,7 +13,6 @@ from swarms.artifacts.error_artifact import ErrorArtifact class BaseTask(ABC): - class State(Enum): PENDING = 1 EXECUTING = 2 @@ -34,15 +33,11 @@ class BaseTask(ABC): @property def parents(self) -> List[BaseTask]: - return [ - self.structure.find_task(parent_id) for parent_id in self.parent_ids - ] + return [self.structure.find_task(parent_id) for parent_id in self.parent_ids] @property def children(self) -> List[BaseTask]: - return [ - self.structure.find_task(child_id) for child_id in self.child_ids - ] + return [self.structure.find_task(child_id) for child_id in self.child_ids] def __rshift__(self, child: BaseTask) -> BaseTask: return self.add_child(child) @@ -123,7 +118,8 @@ class BaseTask(ABC): def can_execute(self) -> bool: return self.state == self.State.PENDING and all( - parent.is_finished() for parent in self.parents) + parent.is_finished() for parent in self.parents + ) def reset(self) -> BaseTask: self.state = self.State.PENDING @@ -136,10 +132,10 @@ class BaseTask(ABC): class Task(BaseModel): - input: Optional[StrictStr] = Field(None, - description="Input prompt for the task") + input: Optional[StrictStr] = Field(None, description="Input prompt for the task") additional_input: Optional[Any] = Field( - None, description="Input parameters for the task. Any value is allowed") + None, description="Input parameters for the task. Any value is allowed" + ) task_id: StrictStr = Field(..., description="ID of the task") class Config: diff --git a/swarms/structs/workflow.py b/swarms/structs/workflow.py index e4a841ed..762ee6cc 100644 --- a/swarms/structs/workflow.py +++ b/swarms/structs/workflow.py @@ -65,13 +65,11 @@ class Workflow: def context(self, task: Task) -> Dict[str, Any]: """Context in tasks""" return { - "parent_output": - task.parents[0].output - if task.parents and task.parents[0].output else None, - "parent": - task.parents[0] if task.parents else None, - "child": - task.children[0] if task.children else None, + "parent_output": task.parents[0].output + if task.parents and task.parents[0].output + else None, + "parent": task.parents[0] if task.parents else None, + "child": task.children[0] if task.children else None, } def __run_from_task(self, task: Optional[Task]) -> None: diff --git a/swarms/swarms/autoscaler.py b/swarms/swarms/autoscaler.py index d0aaa598..5f6bedde 100644 --- a/swarms/swarms/autoscaler.py +++ b/swarms/swarms/autoscaler.py @@ -87,8 +87,7 @@ class AutoScaler: while True: sleep(60) # check minute pending_tasks = self.task_queue.qsize() - active_agents = sum( - [1 for agent in self.agents_pool if agent.is_busy()]) + active_agents = sum([1 for agent in self.agents_pool if agent.is_busy()]) if pending_tasks / len(self.agents_pool) > self.busy_threshold: self.scale_up() diff --git a/swarms/swarms/base.py b/swarms/swarms/base.py index 6d8e0163..e99c9b38 100644 --- a/swarms/swarms/base.py +++ b/swarms/swarms/base.py @@ -117,9 +117,7 @@ class AbstractSwarm(ABC): pass @abstractmethod - def broadcast(self, - message: str, - sender: Optional["AbstractWorker"] = None): + def broadcast(self, message: str, sender: Optional["AbstractWorker"] = None): """Broadcast a message to all workers""" pass diff --git a/swarms/swarms/battle_royal.py b/swarms/swarms/battle_royal.py index 7b5c2a99..2a02186e 100644 --- a/swarms/swarms/battle_royal.py +++ b/swarms/swarms/battle_royal.py @@ -77,15 +77,19 @@ class BattleRoyalSwarm: # Check for clashes and handle them for i, worker1 in enumerate(self.workers): for j, worker2 in enumerate(self.workers): - if (i != j and worker1.is_within_proximity(worker2) and - set(worker1.teams) != set(worker2.teams)): + if ( + i != j + and worker1.is_within_proximity(worker2) + and set(worker1.teams) != set(worker2.teams) + ): winner, loser = self.clash(worker1, worker2, question) print(f"Worker {winner.id} won over Worker {loser.id}") def communicate(self, sender: Worker, reciever: Worker, message: str): """Communicate a message from one worker to another.""" if sender.is_within_proximity(reciever) or any( - team in sender.teams for team in reciever.teams): + team in sender.teams for team in reciever.teams + ): pass def clash(self, worker1: Worker, worker2: Worker, question: str): diff --git a/swarms/swarms/god_mode.py b/swarms/swarms/god_mode.py index 7f302318..fe842f0a 100644 --- a/swarms/swarms/god_mode.py +++ b/swarms/swarms/god_mode.py @@ -49,8 +49,9 @@ class GodMode: table.append([f"LLM {i+1}", response]) print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), - "cyan")) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" + ) + ) def run_all(self, task): """Run the task on all LLMs""" @@ -73,15 +74,18 @@ class GodMode: table.append([f"LLM {i+1}", response]) print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), - "cyan")) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" + ) + ) # New Features def save_responses_to_file(self, filename): """Save responses to file""" with open(filename, "w") as file: - table = [[f"LLM {i+1}", response] - for i, response in enumerate(self.last_responses)] + table = [ + [f"LLM {i+1}", response] + for i, response in enumerate(self.last_responses) + ] file.write(tabulate(table, headers=["LLM", "Response"])) @classmethod @@ -101,9 +105,11 @@ class GodMode: for i, task in enumerate(self.task_history): print(f"{i + 1}. {task}") print("\nLast Responses:") - table = [[f"LLM {i+1}", response] - for i, response in enumerate(self.last_responses)] + table = [ + [f"LLM {i+1}", response] for i, response in enumerate(self.last_responses) + ] print( colored( - tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), - "cyan")) + tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan" + ) + ) diff --git a/swarms/swarms/groupchat.py b/swarms/swarms/groupchat.py index 842ebac9..6be43a89 100644 --- a/swarms/swarms/groupchat.py +++ b/swarms/swarms/groupchat.py @@ -33,8 +33,7 @@ class GroupChat: def next_agent(self, agent: Flow) -> Flow: """Return the next agent in the list.""" - return self.agents[(self.agent_names.index(agent.name) + 1) % - len(self.agents)] + return self.agents[(self.agent_names.index(agent.name) + 1) % len(self.agents)] def select_speaker_msg(self): """Return the message for selecting the next speaker.""" @@ -55,17 +54,24 @@ class GroupChat: if n_agents < 3: logger.warning( f"GroupChat is underpopulated with {n_agents} agents. Direct" - " communication would be more efficient.") + " communication would be more efficient." + ) name = selector.generate_reply( - self.format_history(self.messages + [{ - "role": - "system", - "content": - ("Read the above conversation. Then select the next most" - f" suitable role from {self.agent_names} to play. Only" - " return the role."), - }])) + self.format_history( + self.messages + + [ + { + "role": "system", + "content": ( + "Read the above conversation. Then select the next most" + f" suitable role from {self.agent_names} to play. Only" + " return the role." + ), + } + ] + ) + ) try: return self.agent_by_name(name["content"]) except ValueError: @@ -73,7 +79,8 @@ class GroupChat: def _participant_roles(self): return "\n".join( - [f"{agent.name}: {agent.system_message}" for agent in self.agents]) + [f"{agent.name}: {agent.system_message}" for agent in self.agents] + ) def format_history(self, messages: List[Dict]) -> str: formatted_messages = [] @@ -84,21 +91,19 @@ class GroupChat: class GroupChatManager: - def __init__(self, groupchat: GroupChat, selector: Flow): self.groupchat = groupchat self.selector = selector def __call__(self, task: str): - self.groupchat.messages.append({ - "role": self.selector.name, - "content": task - }) + self.groupchat.messages.append({"role": self.selector.name, "content": task}) for i in range(self.groupchat.max_round): - speaker = self.groupchat.select_speaker(last_speaker=self.selector, - selector=self.selector) + speaker = self.groupchat.select_speaker( + last_speaker=self.selector, selector=self.selector + ) reply = speaker.generate_reply( - self.groupchat.format_history(self.groupchat.messages)) + self.groupchat.format_history(self.groupchat.messages) + ) self.groupchat.messages.append(reply) print(reply) if i == self.groupchat.max_round - 1: diff --git a/swarms/swarms/multi_agent_collab.py b/swarms/swarms/multi_agent_collab.py index a3b79d7f..9a5f27bc 100644 --- a/swarms/swarms/multi_agent_collab.py +++ b/swarms/swarms/multi_agent_collab.py @@ -5,16 +5,16 @@ from langchain.output_parsers import RegexParser # utils class BidOutputParser(RegexParser): - def get_format_instructions(self) -> str: return ( "Your response should be an integrater delimited by angled brackets like" - " this: ") + " this: " + ) -bid_parser = BidOutputParser(regex=r"<(\d+)>", - output_keys=["bid"], - default_output_key="bid") +bid_parser = BidOutputParser( + regex=r"<(\d+)>", output_keys=["bid"], default_output_key="bid" +) def select_next_speaker(step: int, agents, director) -> int: @@ -29,7 +29,6 @@ def select_next_speaker(step: int, agents, director) -> int: # main class MultiAgentCollaboration: - def __init__( self, agents, diff --git a/swarms/swarms/multi_agent_debate.py b/swarms/swarms/multi_agent_debate.py index 1c7ebdf9..4bba3619 100644 --- a/swarms/swarms/multi_agent_debate.py +++ b/swarms/swarms/multi_agent_debate.py @@ -46,6 +46,7 @@ class MultiAgentDebate: def format_results(self, results): formatted_results = "\n".join( - [f"Agent responded: {result['response']}" for result in results]) + [f"Agent responded: {result['response']}" for result in results] + ) return formatted_results diff --git a/swarms/swarms/orchestrate.py b/swarms/swarms/orchestrate.py index d47771ab..f522911b 100644 --- a/swarms/swarms/orchestrate.py +++ b/swarms/swarms/orchestrate.py @@ -111,8 +111,7 @@ class Orchestrator: self.chroma_client = chromadb.Client() - self.collection = self.chroma_client.create_collection( - name=collection_name) + self.collection = self.chroma_client.create_collection(name=collection_name) self.current_tasks = {} @@ -138,8 +137,9 @@ class Orchestrator: result = self.worker.run(task["content"]) # using the embed method to get the vector representation of the result - vector_representation = self.embed(result, self.api_key, - self.model_name) + vector_representation = self.embed( + result, self.api_key, self.model_name + ) self.collection.add( embeddings=[vector_representation], @@ -154,7 +154,8 @@ class Orchestrator: except Exception as error: logging.error( f"Failed to process task {id(task)} by agent {id(agent)}. Error:" - f" {error}") + f" {error}" + ) finally: with self.condition: self.agents.put(agent) @@ -162,7 +163,8 @@ class Orchestrator: def embed(self, input, api_key, model_name): openai = embedding_functions.OpenAIEmbeddingFunction( - api_key=api_key, model_name=model_name) + api_key=api_key, model_name=model_name + ) embedding = openai(input) return embedding @@ -173,13 +175,13 @@ class Orchestrator: try: # Query the vector database for documents created by the agents - results = self.collection.query(query_texts=[str(agent_id)], - n_results=10) + results = self.collection.query(query_texts=[str(agent_id)], n_results=10) return results except Exception as e: logging.error( - f"Failed to retrieve results from agent {agent_id}. Error {e}") + f"Failed to retrieve results from agent {agent_id}. Error {e}" + ) raise # @abstractmethod @@ -210,8 +212,7 @@ class Orchestrator: self.collection.add(documents=[result], ids=[str(id(result))]) except Exception as e: - logging.error( - f"Failed to append the agent output to database. Error: {e}") + logging.error(f"Failed to append the agent output to database. Error: {e}") raise def run(self, objective: str): @@ -224,8 +225,8 @@ class Orchestrator: self.task_queue.append(objective) results = [ - self.assign_task(agent_id, task) for agent_id, task in zip( - range(len(self.agents)), self.task_queue) + self.assign_task(agent_id, task) + for agent_id, task in zip(range(len(self.agents)), self.task_queue) ] for result in results: diff --git a/swarms/swarms/simple_swarm.py b/swarms/swarms/simple_swarm.py index a382c0d7..7e806215 100644 --- a/swarms/swarms/simple_swarm.py +++ b/swarms/swarms/simple_swarm.py @@ -2,7 +2,6 @@ from queue import Queue, PriorityQueue class SimpleSwarm: - def __init__( self, llm, diff --git a/swarms/tools/autogpt.py b/swarms/tools/autogpt.py index 270504aa..cf5450e6 100644 --- a/swarms/tools/autogpt.py +++ b/swarms/tools/autogpt.py @@ -8,7 +8,8 @@ import torch from langchain.agents import tool from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent from langchain.chains.qa_with_sources.loading import ( - BaseCombineDocumentsChain,) + BaseCombineDocumentsChain, +) from langchain.docstore.document import Document from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.tools import BaseTool @@ -36,10 +37,9 @@ def pushd(new_dir): @tool -def process_csv(llm, - csv_file_path: str, - instructions: str, - output_path: Optional[str] = None) -> str: +def process_csv( + llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None +) -> str: """Process a CSV by with pandas in a limited REPL.\ Only use this after writing data to disk as a csv file.\ Any figures must be saved to disk to be viewed by the human.\ @@ -49,10 +49,7 @@ def process_csv(llm, df = pd.read_csv(csv_file_path) except Exception as e: return f"Error: {e}" - agent = create_pandas_dataframe_agent(llm, - df, - max_iterations=30, - verbose=False) + agent = create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=False) if output_path is not None: instructions += f" Save output to disk at {output_path}" try: @@ -82,8 +79,7 @@ async def async_load_playwright(url: str) -> str: text = soup.get_text() lines = (line.strip() for line in text.splitlines()) - chunks = ( - phrase.strip() for line in lines for phrase in line.split(" ")) + chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) results = "\n".join(chunk for chunk in chunks if chunk) except Exception as e: results = f"Error: {e}" @@ -117,7 +113,8 @@ class WebpageQATool(BaseTool): "Browse a webpage and retrieve the information relevant to the question." ) text_splitter: RecursiveCharacterTextSplitter = Field( - default_factory=_get_text_splitter) + default_factory=_get_text_splitter + ) qa_chain: BaseCombineDocumentsChain def _run(self, url: str, question: str) -> str: @@ -128,12 +125,9 @@ class WebpageQATool(BaseTool): results = [] # TODO: Handle this with a MapReduceChain for i in range(0, len(web_docs), 4): - input_docs = web_docs[i:i + 4] + input_docs = web_docs[i : i + 4] window_result = self.qa_chain( - { - "input_documents": input_docs, - "question": question - }, + {"input_documents": input_docs, "question": question}, return_only_outputs=True, ) results.append(f"Response from window {i} - {window_result}") @@ -141,10 +135,7 @@ class WebpageQATool(BaseTool): Document(page_content="\n".join(results), metadata={"source": url}) ] return self.qa_chain( - { - "input_documents": results_docs, - "question": question - }, + {"input_documents": results_docs, "question": question}, return_only_outputs=True, ) @@ -180,17 +171,18 @@ def VQAinference(self, inputs): torch_dtype = torch.float16 if "cuda" in device else torch.float32 processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", torch_dtype=torch_dtype).to(device) + "Salesforce/blip-vqa-base", torch_dtype=torch_dtype + ).to(device) image_path, question = inputs.split(",") raw_image = Image.open(image_path).convert("RGB") - inputs = processor(raw_image, question, - return_tensors="pt").to(device, torch_dtype) + inputs = processor(raw_image, question, return_tensors="pt").to(device, torch_dtype) out = model.generate(**inputs) answer = processor.decode(out[0], skip_special_tokens=True) logger.debug( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}") + f" Question: {question}, Output Answer: {answer}" + ) return answer diff --git a/swarms/tools/mm_models.py b/swarms/tools/mm_models.py index fd115bd6..58fe11e5 100644 --- a/swarms/tools/mm_models.py +++ b/swarms/tools/mm_models.py @@ -25,14 +25,13 @@ from swarms.utils.main import BaseHandler, get_new_image_name class MaskFormer: - def __init__(self, device): print("Initializing MaskFormer to %s" % device) self.device = device - self.processor = CLIPSegProcessor.from_pretrained( - "CIDAS/clipseg-rd64-refined") + self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") self.model = CLIPSegForImageSegmentation.from_pretrained( - "CIDAS/clipseg-rd64-refined").to(device) + "CIDAS/clipseg-rd64-refined" + ).to(device) def inference(self, image_path, text): threshold = 0.5 @@ -40,10 +39,9 @@ class MaskFormer: padding = 20 original_image = Image.open(image_path) image = original_image.resize((512, 512)) - inputs = self.processor(text=text, - images=image, - padding="max_length", - return_tensors="pt").to(self.device) + inputs = self.processor( + text=text, images=image, padding="max_length", return_tensors="pt" + ).to(self.device) with torch.no_grad(): outputs = self.model(**inputs) mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold @@ -54,7 +52,8 @@ class MaskFormer: mask_array = np.zeros_like(mask, dtype=bool) for idx in true_indices: padded_slice = tuple( - slice(max(0, i - padding), i + padding + 1) for i in idx) + slice(max(0, i - padding), i + padding + 1) for i in idx + ) mask_array[padded_slice] = True visual_mask = (mask_array * 255).astype(np.uint8) image_mask = Image.fromarray(visual_mask) @@ -62,7 +61,6 @@ class MaskFormer: class ImageEditing: - def __init__(self, device): print("Initializing ImageEditing to %s" % device) self.device = device @@ -77,24 +75,25 @@ class ImageEditing: @tool( name="Remove Something From The Photo", - description= - ("useful when you want to remove and object or something from the photo " - "from its description or location. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the object need to be removed. "), + description=( + "useful when you want to remove and object or something from the photo " + "from its description or location. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the object need to be removed. " + ), ) def inference_remove(self, inputs): image_path, to_be_removed_txt = inputs.split(",") - return self.inference_replace( - f"{image_path},{to_be_removed_txt},background") + return self.inference_replace(f"{image_path},{to_be_removed_txt},background") @tool( name="Replace Something From The Photo", - description= - ("useful when you want to replace an object from the object description or" - " location with another object from its description. The input to this tool" - " should be a comma separated string of three, representing the image_path," - " the object to be replaced, the object to be replaced with "), + description=( + "useful when you want to replace an object from the object description or" + " location with another object from its description. The input to this tool" + " should be a comma separated string of three, representing the image_path," + " the object to be replaced, the object to be replaced with " + ), ) def inference_replace(self, inputs): image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",") @@ -106,21 +105,22 @@ class ImageEditing: image=original_image.resize((512, 512)), mask_image=mask_image.resize((512, 512)), ).images[0] - updated_image_path = get_new_image_name(image_path, - func_name="replace-something") + updated_image_path = get_new_image_name( + image_path, func_name="replace-something" + ) updated_image = updated_image.resize(original_size) updated_image.save(updated_image_path) logger.debug( f"\nProcessed ImageEditing, Input Image: {image_path}, Replace" f" {to_be_replaced_txt} to {replace_with_txt}, Output Image:" - f" {updated_image_path}") + f" {updated_image_path}" + ) return updated_image_path class InstructPix2Pix: - def __init__(self, device): print("Initializing InstructPix2Pix to %s" % device) self.device = device @@ -131,56 +131,60 @@ class InstructPix2Pix: torch_dtype=self.torch_dtype, ).to(device) self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( - self.pipe.scheduler.config) + self.pipe.scheduler.config + ) @tool( name="Instruct Image Using Text", - description= - ("useful when you want to the style of the image to be like the text. " - "like: make it look like a painting. or make it like a robot. " - "The input to this tool should be a comma separated string of two, " - "representing the image_path and the text. "), + description=( + "useful when you want to the style of the image to be like the text. " + "like: make it look like a painting. or make it like a robot. " + "The input to this tool should be a comma separated string of two, " + "representing the image_path and the text. " + ), ) def inference(self, inputs): """Change style of image.""" logger.debug("===> Starting InstructPix2Pix Inference") image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:]) original_image = Image.open(image_path) - image = self.pipe(text, - image=original_image, - num_inference_steps=40, - image_guidance_scale=1.2).images[0] + image = self.pipe( + text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2 + ).images[0] updated_image_path = get_new_image_name(image_path, func_name="pix2pix") image.save(updated_image_path) logger.debug( f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:" - f" {text}, Output Image: {updated_image_path}") + f" {text}, Output Image: {updated_image_path}" + ) return updated_image_path class Text2Image: - def __init__(self, device): print("Initializing Text2Image to %s" % device) self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.pipe = StableDiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype) + "runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype + ) self.pipe.to(device) self.a_prompt = "best quality, extremely detailed" self.n_prompt = ( "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, " - "fewer digits, cropped, worst quality, low quality") + "fewer digits, cropped, worst quality, low quality" + ) @tool( name="Generate Image From User Input Text", - description= - ("useful when you want to generate an image from a user input text and save" - " it to a file. like: generate an image of an object or something, or" - " generate an image that includes some objects. The input to this tool" - " should be a string, representing the text used to generate image. "), + description=( + "useful when you want to generate an image from a user input text and save" + " it to a file. like: generate an image of an object or something, or" + " generate an image that includes some objects. The input to this tool" + " should be a string, representing the text used to generate image. " + ), ) def inference(self, text): image_filename = os.path.join("image", str(uuid.uuid4())[0:8] + ".png") @@ -190,59 +194,59 @@ class Text2Image: logger.debug( f"\nProcessed Text2Image, Input Text: {text}, Output Image:" - f" {image_filename}") + f" {image_filename}" + ) return image_filename class VisualQuestionAnswering: - def __init__(self, device): print("Initializing VisualQuestionAnswering to %s" % device) self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.device = device - self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-vqa-base") + self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") self.model = BlipForQuestionAnswering.from_pretrained( - "Salesforce/blip-vqa-base", - torch_dtype=self.torch_dtype).to(self.device) + "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype + ).to(self.device) @tool( name="Answer Question About The Image", - description= - ("useful when you need an answer for a question based on an image. like:" - " what is the background color of the last image, how many cats in this" - " figure, what is in this figure. The input to this tool should be a comma" - " separated string of two, representing the image_path and the question" + description=( + "useful when you need an answer for a question based on an image. like:" + " what is the background color of the last image, how many cats in this" + " figure, what is in this figure. The input to this tool should be a comma" + " separated string of two, representing the image_path and the question" ), ) def inference(self, inputs): image_path, question = inputs.split(",") raw_image = Image.open(image_path).convert("RGB") - inputs = self.processor(raw_image, question, - return_tensors="pt").to(self.device, - self.torch_dtype) + inputs = self.processor(raw_image, question, return_tensors="pt").to( + self.device, self.torch_dtype + ) out = self.model.generate(**inputs) answer = self.processor.decode(out[0], skip_special_tokens=True) logger.debug( f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input" - f" Question: {question}, Output Answer: {answer}") + f" Question: {question}, Output Answer: {answer}" + ) return answer class ImageCaptioning(BaseHandler): - def __init__(self, device): print("Initializing ImageCaptioning to %s" % device) self.device = device self.torch_dtype = torch.float16 if "cuda" in device else torch.float32 self.processor = BlipProcessor.from_pretrained( - "Salesforce/blip-image-captioning-base") + "Salesforce/blip-image-captioning-base" + ) self.model = BlipForConditionalGeneration.from_pretrained( - "Salesforce/blip-image-captioning-base", - torch_dtype=self.torch_dtype).to(self.device) + "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype + ).to(self.device) def handle(self, filename: str): img = Image.open(filename) @@ -254,13 +258,14 @@ class ImageCaptioning(BaseHandler): img.save(filename, "PNG") print(f"Resize image form {width}x{height} to {width_new}x{height_new}") - inputs = self.processor(Image.open(filename), - return_tensors="pt").to(self.device, - self.torch_dtype) + inputs = self.processor(Image.open(filename), return_tensors="pt").to( + self.device, self.torch_dtype + ) out = self.model.generate(**inputs) description = self.processor.decode(out[0], skip_special_tokens=True) print( f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text:" - f" {description}") + f" {description}" + ) return IMAGE_PROMPT.format(filename=filename, description=description) diff --git a/swarms/tools/stt.py b/swarms/tools/stt.py index da9d7f27..cfe3e656 100644 --- a/swarms/tools/stt.py +++ b/swarms/tools/stt.py @@ -9,7 +9,6 @@ from pytube import YouTube class SpeechToText: - def __init__( self, video_url, @@ -62,15 +61,14 @@ class SpeechToText: compute_type = "float16" # 1. Transcribe with original Whisper (batched) ๐Ÿ—ฃ๏ธ - model = whisperx.load_model("large-v2", - device, - compute_type=compute_type) + model = whisperx.load_model("large-v2", device, compute_type=compute_type) audio = whisperx.load_audio(audio_file) result = model.transcribe(audio, batch_size=batch_size) # 2. Align Whisper output ๐Ÿ” model_a, metadata = whisperx.load_align_model( - language_code=result["language"], device=device) + language_code=result["language"], device=device + ) result = whisperx.align( result["segments"], model_a, @@ -82,7 +80,8 @@ class SpeechToText: # 3. Assign speaker labels ๐Ÿท๏ธ diarize_model = whisperx.DiarizationPipeline( - use_auth_token=self.hf_api_key, device=device) + use_auth_token=self.hf_api_key, device=device + ) diarize_model(audio_file) try: @@ -99,7 +98,8 @@ class SpeechToText: # 2. Align Whisper output ๐Ÿ” model_a, metadata = whisperx.load_align_model( - language_code=result["language"], device=self.device) + language_code=result["language"], device=self.device + ) result = whisperx.align( result["segments"], @@ -112,7 +112,8 @@ class SpeechToText: # 3. Assign speaker labels ๐Ÿท๏ธ diarize_model = whisperx.DiarizationPipeline( - use_auth_token=self.hf_api_key, device=self.device) + use_auth_token=self.hf_api_key, device=self.device + ) diarize_model(audio_file) diff --git a/swarms/tools/tool.py b/swarms/tools/tool.py index 29b0f5de..f7e85204 100644 --- a/swarms/tools/tool.py +++ b/swarms/tools/tool.py @@ -34,8 +34,9 @@ class SchemaAnnotationError(TypeError): """Raised when 'args_schema' is missing or has an incorrect type annotation.""" -def _create_subset_model(name: str, model: BaseModel, - field_names: list) -> Type[BaseModel]: +def _create_subset_model( + name: str, model: BaseModel, field_names: list +) -> Type[BaseModel]: """Create a pydantic model with only a subset of model's fields.""" fields = {} for field_name in field_names: @@ -51,11 +52,7 @@ def _get_filtered_args( """Get the arguments from a function's signature.""" schema = inferred_model.schema()["properties"] valid_keys = signature(func).parameters - return { - k: schema[k] - for k in valid_keys - if k not in ("run_manager", "callbacks") - } + return {k: schema[k] for k in valid_keys if k not in ("run_manager", "callbacks")} class _SchemaConfig: @@ -85,8 +82,9 @@ def create_schema_from_function( del inferred_model.__fields__["callbacks"] # Pydantic adds placeholder virtual fields we need to strip valid_properties = _get_filtered_args(inferred_model, func) - return _create_subset_model(f"{model_name}Schema", inferred_model, - list(valid_properties)) + return _create_subset_model( + f"{model_name}Schema", inferred_model, list(valid_properties) + ) class ToolException(Exception): @@ -127,7 +125,8 @@ class ChildTool(BaseTool): "Expected annotation of 'Type[BaseModel]'" f" but got '{args_schema_type}'.\n" "Expected class looks like:\n" - f"{typehint_mandate}") + f"{typehint_mandate}" + ) name: str """The unique name of the tool that clearly communicates its purpose.""" @@ -148,8 +147,7 @@ class ChildTool(BaseTool): callbacks: Callbacks = Field(default=None, exclude=True) """Callbacks to be called during tool execution.""" - callback_manager: Optional[BaseCallbackManager] = Field(default=None, - exclude=True) + callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """Deprecated. Please use callbacks instead.""" tags: Optional[List[str]] = None """Optional list of tags associated with the tool. Defaults to None @@ -164,8 +162,9 @@ class ChildTool(BaseTool): You can use these to eg identify a specific instance of a tool with its use case. """ - handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], - str]]] = False + handle_tool_error: Optional[ + Union[bool, str, Callable[[ToolException], str]] + ] = False """Handle the content of the ToolException thrown.""" class Config(Serializable.Config): @@ -245,9 +244,7 @@ class ChildTool(BaseTool): else: if input_args is not None: result = input_args.parse_obj(tool_input) - return { - k: v for k, v in result.dict().items() if k in tool_input - } + return {k: v for k, v in result.dict().items() if k in tool_input} return tool_input @root_validator() @@ -289,8 +286,7 @@ class ChildTool(BaseTool): *args, ) - def _to_args_and_kwargs(self, - tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: + def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: # For backwards compatibility, if run_input is a string, # pass as a positional argument. if isinstance(tool_input, str): @@ -329,10 +325,7 @@ class ChildTool(BaseTool): # TODO: maybe also pass through run_manager is _run supports kwargs new_arg_supported = signature(self._run).parameters.get("run_manager") run_manager = callback_manager.on_tool_start( - { - "name": self.name, - "description": self.description - }, + {"name": self.name, "description": self.description}, tool_input if isinstance(tool_input, str) else str(tool_input), color=start_color, name=run_name, @@ -342,7 +335,9 @@ class ChildTool(BaseTool): tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) observation = ( self._run(*tool_args, run_manager=run_manager, **tool_kwargs) - if new_arg_supported else self._run(*tool_args, **tool_kwargs)) + if new_arg_supported + else self._run(*tool_args, **tool_kwargs) + ) except ToolException as e: if not self.handle_tool_error: run_manager.on_tool_error(e) @@ -359,20 +354,19 @@ class ChildTool(BaseTool): else: raise ValueError( "Got unexpected type of `handle_tool_error`. Expected bool, str " - f"or callable. Received: {self.handle_tool_error}") - run_manager.on_tool_end(str(observation), - color="red", - name=self.name, - **kwargs) + f"or callable. Received: {self.handle_tool_error}" + ) + run_manager.on_tool_end( + str(observation), color="red", name=self.name, **kwargs + ) return observation except (Exception, KeyboardInterrupt) as e: run_manager.on_tool_error(e) raise e else: - run_manager.on_tool_end(str(observation), - color=color, - name=self.name, - **kwargs) + run_manager.on_tool_end( + str(observation), color=color, name=self.name, **kwargs + ) return observation async def arun( @@ -405,10 +399,7 @@ class ChildTool(BaseTool): ) new_arg_supported = signature(self._arun).parameters.get("run_manager") run_manager = await callback_manager.on_tool_start( - { - "name": self.name, - "description": self.description - }, + {"name": self.name, "description": self.description}, tool_input if isinstance(tool_input, str) else str(tool_input), color=start_color, name=run_name, @@ -417,10 +408,11 @@ class ChildTool(BaseTool): try: # We then call the tool on the tool input to get an observation tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) - observation = (await self._arun(*tool_args, - run_manager=run_manager, - **tool_kwargs) if new_arg_supported - else await self._arun(*tool_args, **tool_kwargs)) + observation = ( + await self._arun(*tool_args, run_manager=run_manager, **tool_kwargs) + if new_arg_supported + else await self._arun(*tool_args, **tool_kwargs) + ) except ToolException as e: if not self.handle_tool_error: await run_manager.on_tool_error(e) @@ -437,20 +429,19 @@ class ChildTool(BaseTool): else: raise ValueError( "Got unexpected type of `handle_tool_error`. Expected bool, str " - f"or callable. Received: {self.handle_tool_error}") - await run_manager.on_tool_end(str(observation), - color="red", - name=self.name, - **kwargs) + f"or callable. Received: {self.handle_tool_error}" + ) + await run_manager.on_tool_end( + str(observation), color="red", name=self.name, **kwargs + ) return observation except (Exception, KeyboardInterrupt) as e: await run_manager.on_tool_error(e) raise e else: - await run_manager.on_tool_end(str(observation), - color=color, - name=self.name, - **kwargs) + await run_manager.on_tool_end( + str(observation), color=color, name=self.name, **kwargs + ) return observation def __call__(self, tool_input: str, callbacks: Callbacks = None) -> str: @@ -477,7 +468,8 @@ class Tool(BaseTool): if not self.coroutine: # If the tool does not implement async, fall back to default implementation return await asyncio.get_running_loop().run_in_executor( - None, partial(self.invoke, input, config, **kwargs)) + None, partial(self.invoke, input, config, **kwargs) + ) return await super().ainvoke(input, config, **kwargs) @@ -492,8 +484,7 @@ class Tool(BaseTool): # assume it takes a single string input. return {"tool_input": {"type": "string"}} - def _to_args_and_kwargs(self, - tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: + def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]: """Convert tool input to pydantic model.""" args, kwargs = super()._to_args_and_kwargs(tool_input) # For backwards compatibility. The tool must be run with a single input @@ -512,13 +503,16 @@ class Tool(BaseTool): ) -> Any: """Use the tool.""" if self.func: - new_argument_supported = signature( - self.func).parameters.get("callbacks") - return (self.func( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) if new_argument_supported else self.func(*args, **kwargs)) + new_argument_supported = signature(self.func).parameters.get("callbacks") + return ( + self.func( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) + if new_argument_supported + else self.func(*args, **kwargs) + ) raise NotImplementedError("Tool does not support sync") async def _arun( @@ -529,27 +523,31 @@ class Tool(BaseTool): ) -> Any: """Use the tool asynchronously.""" if self.coroutine: - new_argument_supported = signature( - self.coroutine).parameters.get("callbacks") - return (await self.coroutine( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) if new_argument_supported else await self.coroutine( - *args, **kwargs)) + new_argument_supported = signature(self.coroutine).parameters.get( + "callbacks" + ) + return ( + await self.coroutine( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) + if new_argument_supported + else await self.coroutine(*args, **kwargs) + ) else: return await asyncio.get_running_loop().run_in_executor( - None, partial(self._run, run_manager=run_manager, **kwargs), - *args) + None, partial(self._run, run_manager=run_manager, **kwargs), *args + ) # TODO: this is for backwards compatibility, remove in future - def __init__(self, name: str, func: Optional[Callable], description: str, - **kwargs: Any) -> None: + def __init__( + self, name: str, func: Optional[Callable], description: str, **kwargs: Any + ) -> None: """Initialize tool.""" - super(Tool, self).__init__(name=name, - func=func, - description=description, - **kwargs) + super(Tool, self).__init__( + name=name, func=func, description=description, **kwargs + ) @classmethod def from_function( @@ -559,8 +557,9 @@ class Tool(BaseTool): description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, - coroutine: Optional[Callable[..., Awaitable[ - Any]]] = None, # This is last for compatibility, but should be after func + coroutine: Optional[ + Callable[..., Awaitable[Any]] + ] = None, # This is last for compatibility, but should be after func **kwargs: Any, ) -> Tool: """Initialize tool from a function.""" @@ -598,7 +597,8 @@ class StructuredTool(BaseTool): if not self.coroutine: # If the tool does not implement async, fall back to default implementation return await asyncio.get_running_loop().run_in_executor( - None, partial(self.invoke, input, config, **kwargs)) + None, partial(self.invoke, input, config, **kwargs) + ) return await super().ainvoke(input, config, **kwargs) @@ -617,13 +617,16 @@ class StructuredTool(BaseTool): ) -> Any: """Use the tool.""" if self.func: - new_argument_supported = signature( - self.func).parameters.get("callbacks") - return (self.func( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) if new_argument_supported else self.func(*args, **kwargs)) + new_argument_supported = signature(self.func).parameters.get("callbacks") + return ( + self.func( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) + if new_argument_supported + else self.func(*args, **kwargs) + ) raise NotImplementedError("Tool does not support sync") async def _arun( @@ -634,14 +637,18 @@ class StructuredTool(BaseTool): ) -> str: """Use the tool asynchronously.""" if self.coroutine: - new_argument_supported = signature( - self.coroutine).parameters.get("callbacks") - return (await self.coroutine( - *args, - callbacks=run_manager.get_child() if run_manager else None, - **kwargs, - ) if new_argument_supported else await self.coroutine( - *args, **kwargs)) + new_argument_supported = signature(self.coroutine).parameters.get( + "callbacks" + ) + return ( + await self.coroutine( + *args, + callbacks=run_manager.get_child() if run_manager else None, + **kwargs, + ) + if new_argument_supported + else await self.coroutine(*args, **kwargs) + ) return await asyncio.get_running_loop().run_in_executor( None, partial(self._run, run_manager=run_manager, **kwargs), @@ -698,7 +705,8 @@ class StructuredTool(BaseTool): description = description or source_function.__doc__ if description is None: raise ValueError( - "Function must have a docstring if description not provided.") + "Function must have a docstring if description not provided." + ) # Description example: # search_api(query: str) - Searches the API for the query. @@ -706,8 +714,7 @@ class StructuredTool(BaseTool): description = f"{name}{sig} - {description.strip()}" _args_schema = args_schema if _args_schema is None and infer_schema: - _args_schema = create_schema_from_function(f"{name}Schema", - source_function) + _args_schema = create_schema_from_function(f"{name}Schema", source_function) return cls( name=name, func=func, @@ -755,7 +762,6 @@ def tool( """ def _make_with_name(tool_name: str) -> Callable: - def _make_tool(dec_func: Union[Callable, Runnable]) -> BaseTool: if isinstance(dec_func, Runnable): runnable = dec_func @@ -763,13 +769,14 @@ def tool( if runnable.input_schema.schema().get("type") != "object": raise ValueError("Runnable must have an object schema.") - async def ainvoke_wrapper(callbacks: Optional[Callbacks] = None, - **kwargs: Any) -> Any: - return await runnable.ainvoke(kwargs, - {"callbacks": callbacks}) + async def ainvoke_wrapper( + callbacks: Optional[Callbacks] = None, **kwargs: Any + ) -> Any: + return await runnable.ainvoke(kwargs, {"callbacks": callbacks}) - def invoke_wrapper(callbacks: Optional[Callbacks] = None, - **kwargs: Any) -> Any: + def invoke_wrapper( + callbacks: Optional[Callbacks] = None, **kwargs: Any + ) -> Any: return runnable.invoke(kwargs, {"callbacks": callbacks}) coroutine = ainvoke_wrapper @@ -802,7 +809,8 @@ def tool( if func.__doc__ is None: raise ValueError( "Function must have a docstring if " - "description not provided and infer_schema is False.") + "description not provided and infer_schema is False." + ) return Tool( name=tool_name, func=func, @@ -813,8 +821,7 @@ def tool( return _make_tool - if len(args) == 2 and isinstance(args[0], str) and isinstance( - args[1], Runnable): + if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable): return _make_with_name(args[0])(args[1]) elif len(args) == 1 and isinstance(args[0], str): # if the argument is a string, then we use the string as the tool name diff --git a/swarms/tools/tool_registry.py b/swarms/tools/tool_registry.py index 3354646a..5aa544e9 100644 --- a/swarms/tools/tool_registry.py +++ b/swarms/tools/tool_registry.py @@ -6,7 +6,6 @@ FuncToolBuilder = Callable[[], ToolBuilder] class ToolsRegistry: - def __init__(self) -> None: self.tools: Dict[str, FuncToolBuilder] = {} @@ -19,7 +18,8 @@ class ToolsRegistry: if isinstance(ret, tool): return ret raise ValueError( - "Tool builder {} did not return a Tool instance".format(tool_name)) + "Tool builder {} did not return a Tool instance".format(tool_name) + ) def list_tools(self) -> List[str]: return list(self.tools.keys()) @@ -29,7 +29,6 @@ tools_registry = ToolsRegistry() def register(tool_name): - def decorator(tool: FuncToolBuilder): tools_registry.register(tool_name, tool) return tool diff --git a/swarms/utils/code_interpreter.py b/swarms/utils/code_interpreter.py index c89ac7a7..80eb6700 100644 --- a/swarms/utils/code_interpreter.py +++ b/swarms/utils/code_interpreter.py @@ -118,19 +118,14 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): # Most of the time it doesn't matter, but we should figure out why it happens frequently with: # applescript yield {"output": traceback.format_exc()} - yield { - "output": f"Retrying... ({retry_count}/{max_retries})" - } + yield {"output": f"Retrying... ({retry_count}/{max_retries})"} yield {"output": "Restarting process."} self.start_process() retry_count += 1 if retry_count > max_retries: - yield { - "output": - "Maximum retries reached. Could not execute code." - } + yield {"output": "Maximum retries reached. Could not execute code."} return while True: @@ -139,8 +134,7 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter): else: time.sleep(0.1) try: - output = self.output_queue.get( - timeout=0.3) # Waits for 0.3 seconds + output = self.output_queue.get(timeout=0.3) # Waits for 0.3 seconds yield output except queue.Empty: if self.done.is_set(): diff --git a/swarms/utils/decorators.py b/swarms/utils/decorators.py index 2f22528b..8a5a5d56 100644 --- a/swarms/utils/decorators.py +++ b/swarms/utils/decorators.py @@ -6,7 +6,6 @@ import warnings def log_decorator(func): - def wrapper(*args, **kwargs): logging.info(f"Entering {func.__name__}") result = func(*args, **kwargs) @@ -17,7 +16,6 @@ def log_decorator(func): def error_decorator(func): - def wrapper(*args, **kwargs): try: return func(*args, **kwargs) @@ -29,22 +27,18 @@ def error_decorator(func): def timing_decorator(func): - def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() - logging.info( - f"{func.__name__} executed in {end_time - start_time} seconds") + logging.info(f"{func.__name__} executed in {end_time - start_time} seconds") return result return wrapper def retry_decorator(max_retries=5): - def decorator(func): - @functools.wraps(func) def wrapper(*args, **kwargs): for _ in range(max_retries): @@ -83,20 +77,16 @@ def synchronized_decorator(func): def deprecated_decorator(func): - @functools.wraps(func) def wrapper(*args, **kwargs): - warnings.warn(f"{func.__name__} is deprecated", - category=DeprecationWarning) + warnings.warn(f"{func.__name__} is deprecated", category=DeprecationWarning) return func(*args, **kwargs) return wrapper def validate_inputs_decorator(validator): - def decorator(func): - @functools.wraps(func) def wrapper(*args, **kwargs): if not validator(*args, **kwargs): diff --git a/swarms/utils/futures.py b/swarms/utils/futures.py index 5c2dfdcd..55a4e5d5 100644 --- a/swarms/utils/futures.py +++ b/swarms/utils/futures.py @@ -5,8 +5,6 @@ T = TypeVar("T") def execute_futures_dict(fs_dict: dict[str, futures.Future[T]]) -> dict[str, T]: - futures.wait(fs_dict.values(), - timeout=None, - return_when=futures.ALL_COMPLETED) + futures.wait(fs_dict.values(), timeout=None, return_when=futures.ALL_COMPLETED) return {key: future.result() for key, future in fs_dict.items()} diff --git a/swarms/utils/hash.py b/swarms/utils/hash.py index 458fc147..725cc6ba 100644 --- a/swarms/utils/hash.py +++ b/swarms/utils/hash.py @@ -4,7 +4,8 @@ import hashlib def dataframe_to_hash(dataframe: pd.DataFrame) -> str: return hashlib.sha256( - pd.util.hash_pandas_object(dataframe, index=True).values).hexdigest() + pd.util.hash_pandas_object(dataframe, index=True).values + ).hexdigest() def str_to_hash(text: str, hash_algorithm: str = "sha256") -> str: diff --git a/swarms/utils/main.py b/swarms/utils/main.py index 9d5eefdf..63cb0e4a 100644 --- a/swarms/utils/main.py +++ b/swarms/utils/main.py @@ -51,16 +51,16 @@ def get_new_image_name(org_img_name, func_name="update"): if len(name_split) == 1: most_org_file_name = name_split[0] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.png".format(this_new_uuid, func_name, - recent_prev_file_name, - most_org_file_name) + new_file_name = "{}_{}_{}_{}.png".format( + this_new_uuid, func_name, recent_prev_file_name, most_org_file_name + ) else: assert len(name_split) == 4 most_org_file_name = name_split[3] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.png".format(this_new_uuid, func_name, - recent_prev_file_name, - most_org_file_name) + new_file_name = "{}_{}_{}_{}.png".format( + this_new_uuid, func_name, recent_prev_file_name, most_org_file_name + ) return os.path.join(head, new_file_name) @@ -73,16 +73,16 @@ def get_new_dataframe_name(org_img_name, func_name="update"): if len(name_split) == 1: most_org_file_name = name_split[0] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.csv".format(this_new_uuid, func_name, - recent_prev_file_name, - most_org_file_name) + new_file_name = "{}_{}_{}_{}.csv".format( + this_new_uuid, func_name, recent_prev_file_name, most_org_file_name + ) else: assert len(name_split) == 4 most_org_file_name = name_split[3] recent_prev_file_name = name_split[0] - new_file_name = "{}_{}_{}_{}.csv".format(this_new_uuid, func_name, - recent_prev_file_name, - most_org_file_name) + new_file_name = "{}_{}_{}_{}.csv".format( + this_new_uuid, func_name, recent_prev_file_name, most_org_file_name + ) return os.path.join(head, new_file_name) @@ -92,7 +92,6 @@ def get_new_dataframe_name(org_img_name, func_name="update"): class Code: - def __init__(self, value: int): self.value = value @@ -101,7 +100,6 @@ class Code: class Color(Code): - def bg(self) -> "Color": self.value += 10 return self @@ -148,7 +146,6 @@ class Color(Code): class Style(Code): - @staticmethod def reset() -> "Style": return Style(0) @@ -205,8 +202,7 @@ def dim_multiline(message: str) -> str: lines = message.split("\n") if len(lines) <= 1: return lines[0] - return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to( - Color.black().bright()) + return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to(Color.black().bright()) # +=============================> ANSI Ending @@ -217,7 +213,6 @@ STATIC_DIR = "static" class AbstractUploader(ABC): - @abstractmethod def upload(self, filepath: str) -> str: pass @@ -233,9 +228,7 @@ class AbstractUploader(ABC): class S3Uploader(AbstractUploader): - - def __init__(self, accessKey: str, secretKey: str, region: str, - bucket: str): + def __init__(self, accessKey: str, secretKey: str, region: str, bucket: str): self.accessKey = accessKey self.secretKey = secretKey self.region = region @@ -270,7 +263,6 @@ class S3Uploader(AbstractUploader): class StaticUploader(AbstractUploader): - def __init__(self, server: str, path: Path, endpoint: str): self.server = server self.path = path @@ -338,19 +330,16 @@ class FileType(Enum): class BaseHandler: - def handle(self, filename: str) -> str: raise NotImplementedError class FileHandler: - def __init__(self, handlers: Dict[FileType, BaseHandler], path: Path): self.handlers = handlers self.path = path - def register(self, filetype: FileType, - handler: BaseHandler) -> "FileHandler": + def register(self, filetype: FileType, handler: BaseHandler) -> "FileHandler": self.handlers[filetype] = handler return self @@ -358,8 +347,8 @@ class FileHandler: filetype = FileType.from_url(url) data = requests.get(url).content local_filename = os.path.join( - "file", - str(uuid.uuid4())[0:8] + filetype.to_extension()) + "file", str(uuid.uuid4())[0:8] + filetype.to_extension() + ) os.makedirs(os.path.dirname(local_filename), exist_ok=True) with open(local_filename, "wb") as f: size = f.write(data) @@ -368,15 +357,17 @@ class FileHandler: def handle(self, url: str) -> str: try: - if url.startswith(os.environ.get("SERVER", - "http://localhost:8000")): + if url.startswith(os.environ.get("SERVER", "http://localhost:8000")): local_filepath = url[ - len(os.environ.get("SERVER", "http://localhost:8000")) + 1:] + len(os.environ.get("SERVER", "http://localhost:8000")) + 1 : + ] local_filename = Path("file") / local_filepath.split("/")[-1] src = self.path / local_filepath - dst = (self.path / - os.environ.get("PLAYGROUND_DIR", "./playground") / - local_filename) + dst = ( + self.path + / os.environ.get("PLAYGROUND_DIR", "./playground") + / local_filename + ) os.makedirs(os.path.dirname(dst), exist_ok=True) shutil.copy(src, dst) else: @@ -386,7 +377,8 @@ class FileHandler: if FileType.from_url(url) == FileType.IMAGE: raise Exception( f"No handler for {FileType.from_url(url)}. " - "Please set USE_GPU to True in env/settings.py") + "Please set USE_GPU to True in env/settings.py" + ) else: raise Exception(f"No handler for {FileType.from_url(url)}") return handler.handle(local_filename) @@ -400,17 +392,17 @@ class FileHandler: class CsvToDataframe(BaseHandler): - def handle(self, filename: str): df = pd.read_csv(filename) description = ( f"Dataframe with {len(df)} rows and {len(df.columns)} columns. " "Columns are: " - f"{', '.join(df.columns)}") + f"{', '.join(df.columns)}" + ) print( f"\nProcessed CsvToDataframe, Input CSV: {filename}, Output Description:" - f" {description}") + f" {description}" + ) - return DATAFRAME_PROMPT.format(filename=filename, - description=description) + return DATAFRAME_PROMPT.format(filename=filename, description=description) diff --git a/swarms/utils/parse_code.py b/swarms/utils/parse_code.py index 020c9bef..a2f346ea 100644 --- a/swarms/utils/parse_code.py +++ b/swarms/utils/parse_code.py @@ -7,6 +7,5 @@ def extract_code_in_backticks_in_string(message: str) -> str: """ pattern = r"`` ``(.*?)`` " # Non-greedy match between six backticks - match = re.search(pattern, message, - re.DOTALL) # re.DOTALL to match newline chars + match = re.search(pattern, message, re.DOTALL) # re.DOTALL to match newline chars return match.group(1).strip() if match else None diff --git a/swarms/utils/revutils.py b/swarms/utils/revutils.py index 9db1e123..7868ae44 100644 --- a/swarms/utils/revutils.py +++ b/swarms/utils/revutils.py @@ -49,12 +49,16 @@ def get_input( """ Multiline input function. """ - return (session.prompt( - completer=completer, - multiline=True, - auto_suggest=AutoSuggestFromHistory(), - key_bindings=key_bindings, - ) if session else prompt(multiline=True)) + return ( + session.prompt( + completer=completer, + multiline=True, + auto_suggest=AutoSuggestFromHistory(), + key_bindings=key_bindings, + ) + if session + else prompt(multiline=True) + ) async def get_input_async( @@ -64,11 +68,15 @@ async def get_input_async( """ Multiline input function. """ - return (await session.prompt_async( - completer=completer, - multiline=True, - auto_suggest=AutoSuggestFromHistory(), - ) if session else prompt(multiline=True)) + return ( + await session.prompt_async( + completer=completer, + multiline=True, + auto_suggest=AutoSuggestFromHistory(), + ) + if session + else prompt(multiline=True) + ) def get_filtered_keys_from_object(obj: object, *keys: str) -> any: @@ -86,7 +94,9 @@ def get_filtered_keys_from_object(obj: object, *keys: str) -> any: return {key for key in class_keys if key not in keys[1:]} # Check if all passed keys are valid if invalid_keys := set(keys) - class_keys: - raise ValueError(f"Invalid keys: {invalid_keys}",) + raise ValueError( + f"Invalid keys: {invalid_keys}", + ) # Only return specified keys that are in class_keys return {key for key in keys if key in class_keys} @@ -114,8 +124,8 @@ def random_int(min: int, max: int) -> int: if __name__ == "__main__": logging.basicConfig( - format= - "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s - %(message)s",) + format="%(asctime)s - %(name)s - %(levelname)s - %(funcName)s - %(message)s", + ) log = logging.getLogger(__name__) diff --git a/swarms/utils/serializable.py b/swarms/utils/serializable.py index 47cc815f..8f0e5ccf 100644 --- a/swarms/utils/serializable.py +++ b/swarms/utils/serializable.py @@ -106,22 +106,21 @@ class Serializable(BaseModel, ABC): lc_kwargs.update({key: secret_value}) return { - "lc": - 1, - "type": - "constructor", + "lc": 1, + "type": "constructor", "id": [*self.lc_namespace, self.__class__.__name__], - "kwargs": - lc_kwargs if not secrets else _replace_secrets( - lc_kwargs, secrets), + "kwargs": lc_kwargs + if not secrets + else _replace_secrets(lc_kwargs, secrets), } def to_json_not_implemented(self) -> SerializedNotImplemented: return to_json_not_implemented(self) -def _replace_secrets(root: Dict[Any, Any], - secrets_map: Dict[str, str]) -> Dict[Any, Any]: +def _replace_secrets( + root: Dict[Any, Any], secrets_map: Dict[str, str] +) -> Dict[Any, Any]: result = root.copy() for path, secret_id in secrets_map.items(): [*parts, last] = path.split(".") diff --git a/swarms/utils/static.py b/swarms/utils/static.py index 23f13996..3b8a276d 100644 --- a/swarms/utils/static.py +++ b/swarms/utils/static.py @@ -8,7 +8,6 @@ from swarms.utils.main import AbstractUploader class StaticUploader(AbstractUploader): - def __init__(self, server: str, path: Path, endpoint: str): self.server = server self.path = path diff --git a/swarms/workers/worker.py b/swarms/workers/worker.py index bef9682a..9986666a 100644 --- a/swarms/workers/worker.py +++ b/swarms/workers/worker.py @@ -4,7 +4,8 @@ from typing import Dict, Union import faiss from langchain.chains.qa_with_sources.loading import ( - load_qa_with_sources_chain,) + load_qa_with_sources_chain, +) from langchain.docstore import InMemoryDocstore from langchain.embeddings import OpenAIEmbeddings from langchain.tools import ReadFileTool, WriteFileTool @@ -131,7 +132,8 @@ class Worker: ``` """ query_website_tool = WebpageQATool( - qa_chain=load_qa_with_sources_chain(self.llm)) + qa_chain=load_qa_with_sources_chain(self.llm) + ) self.tools = [ WriteFileTool(root_dir=ROOT_DIR), @@ -155,13 +157,15 @@ class Worker: embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) - self.vectorstore = FAISS(embeddings_model.embed_query, index, - InMemoryDocstore({}), {}) + self.vectorstore = FAISS( + embeddings_model.embed_query, index, InMemoryDocstore({}), {} + ) except Exception as error: raise RuntimeError( "Error setting up memory perhaps try try tuning the embedding size:" - f" {error}") + f" {error}" + ) def setup_agent(self): """ @@ -290,6 +294,8 @@ class Worker: def is_within_proximity(self, other_worker): """Using Euclidean distance for proximity check""" - distance = ((self.coordinates[0] - other_worker.coordinates[0])**2 + - (self.coordinates[1] - other_worker.coordinates[1])**2)**0.5 + distance = ( + (self.coordinates[0] - other_worker.coordinates[0]) ** 2 + + (self.coordinates[1] - other_worker.coordinates[1]) ** 2 + ) ** 0.5 return distance < 10 # threshold for proximity