From 3dc8a4f4448e0b30cccfd1a72f9c84d73e050ecc Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 20 Feb 2024 17:01:28 -0800 Subject: [PATCH] [CLEANUP OPERATION] --- README.md | 147 +++++------- docs/applications/discord.md | 2 + docs/examples/bingchat.md | 1 + docs/examples/flow.md | 11 +- docs/examples/omni_agent.md | 2 +- docs/examples/revgpt.md | 1 - docs/examples/stacked_worker.md | 10 +- docs/examples/worker.md | 6 +- docs/swarms/agents/abstractagent.md | 7 +- docs/swarms/agents/message.md | 16 +- docs/swarms/agents/omni_agent.md | 6 +- docs/swarms/agents/toolagent.md | 8 +- docs/swarms/agents/workeragent.md | 12 +- docs/swarms/chunkers/basechunker.md | 4 +- docs/swarms/chunkers/pdf_chunker.md | 4 +- docs/swarms/index.md | 9 +- docs/swarms/memory/pg.md | 39 ++-- docs/swarms/memory/pinecone.md | 11 +- docs/swarms/memory/qdrant.md | 16 +- docs/swarms/memory/short_term_memory.md | 4 +- docs/swarms/memory/weaviate.md | 27 +-- docs/swarms/models/anthropic.md | 4 +- docs/swarms/models/base_multimodal_model.md | 10 +- docs/swarms/models/bingchat.md | 9 +- docs/swarms/models/biogpt.md | 2 - 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playground/memory/qdrant/usage_example.py | 1 + playground/models/anthropic_example.py | 1 - playground/models/bingchat_example.py | 7 +- playground/models/cohere_example.py | 1 - .../models/dalle3_concurrent_example.py | 8 +- .../models/distilled_whiserpx_example.py | 1 + playground/models/gemini_example.py | 2 + playground/models/gpt4_v_example.py | 1 - playground/models/miqu.py | 1 - playground/models/roboflow_example.py | 1 - playground/models/tts_speech_example.py | 4 +- playground/structs/agent_with_longterm.py | 2 +- .../structs/agent_with_tools_example.py | 12 +- playground/structs/autoscaler_example.py | 1 - .../structs/concurrent_workflow_example.py | 4 +- playground/structs/debate_example.py | 6 +- .../structs/dialogue_simulator_example.py | 2 +- playground/structs/groupchat_example.py | 5 +- playground/structs/gui_app_example.py | 1 - playground/structs/majority_voting.py | 2 +- .../structs/multi_agent_debate_example.py | 2 +- .../structs/multi_modal_flow_example.py | 2 +- playground/structs/multi_modal_rag_agent.py | 6 +- playground/structs/orchestrate_example.py | 2 +- playground/structs/orchestrator_example.py | 2 +- playground/structs/recursive_example.py | 4 +- .../structs/sequential_workflow_example.py | 2 +- playground/structs/swarm_network_example.py | 2 +- playground/structs/todo_app_example.py | 1 - playground/structs/workflow_example.py | 3 +- playground/swarms_example.ipynb | 217 +++++++++--------- playground/tools/agent_with_tools_example.py | 4 +- scripts/auto_tests_docs/auto_docs.py | 11 +- scripts/auto_tests_docs/auto_docs_omni.py | 2 +- scripts/auto_tests_docs/auto_tests.py | 9 +- scripts/auto_tests_docs/update_mkdocs.py | 2 +- scripts/get_package_requirements.py | 2 +- scripts/requirementstxt_to_pyproject.py | 4 +- swarms/__init__.py | 16 +- swarms/agents/__init__.py | 2 +- swarms/agents/base.py | 1 - swarms/agents/developer_agents.py | 4 +- swarms/agents/omni_modal_agent.py | 3 +- swarms/agents/simple_agent.py | 7 +- swarms/artifacts/base_artifact.py | 3 +- swarms/artifacts/text_artifact.py | 7 +- swarms/chunkers/__init__.py | 2 +- swarms/chunkers/base_chunker.py | 5 +- swarms/cli/_cli.py | 1 + swarms/cli/run_file.py | 2 +- swarms/loaders/pdf_loader.py | 16 +- swarms/memory/__init__.py | 12 +- swarms/memory/base_db.py | 20 -- swarms/memory/base_vectordb.py | 18 -- swarms/memory/chroma_db.py | 7 +- swarms/memory/cosine_similarity.py | 1 + swarms/memory/dict_shared_memory.py | 6 +- swarms/memory/lanchain_chroma.py | 3 +- swarms/memory/pg.py | 1 + swarms/memory/pinecone.py | 4 +- swarms/memory/short_term_memory.py | 7 +- swarms/memory/sqlite.py | 3 +- swarms/memory/utils.py | 2 +- swarms/memory/visual_memory.py | 2 +- swarms/models/__init__.py | 107 +++++---- swarms/models/anthropic.py | 6 +- swarms/models/base_embedding_model.py | 9 +- swarms/models/base_llm.py | 2 - swarms/models/base_multimodal_model.py | 3 - swarms/models/base_tts.py | 6 +- swarms/models/base_ttv.py | 10 +- swarms/models/biogpt.py | 6 +- swarms/models/chest_agent.py | 2 + swarms/models/cog_agent.py | 3 +- swarms/models/cohere_chat.py | 19 +- swarms/models/embeddings_base.py | 1 + swarms/models/gemini.py | 1 - swarms/models/gpt4_sam.py | 3 +- swarms/models/gpt4_vision_api.py | 5 +- swarms/models/huggingface_pipeline.py | 3 +- swarms/models/idefics.py | 6 +- swarms/models/jina_embeds.py | 3 +- swarms/models/kosmos_two.py | 2 +- swarms/models/layoutlm_document_qa.py | 4 +- swarms/models/llama_function_caller.py | 5 +- swarms/models/llava.py | 6 +- swarms/models/mistral.py | 2 +- swarms/models/mixtral.py | 2 + swarms/models/model_registry.py | 2 +- swarms/models/mpt.py | 3 +- swarms/models/nougat.py | 2 + swarms/models/odin.py | 6 +- swarms/models/open_dalle.py | 3 +- swarms/models/openai_embeddings.py | 91 ++++---- swarms/models/openai_models.py | 194 ++++++++-------- swarms/models/palm.py | 18 +- swarms/models/petals.py | 2 +- swarms/models/sam.py | 5 +- swarms/models/sam_supervision.py | 9 +- swarms/models/sampling_params.py | 3 +- swarms/models/speecht5.py | 9 +- swarms/models/ssd_1b.py | 2 +- swarms/models/stable_diffusion.py | 7 +- swarms/models/timm.py | 1 + swarms/models/types.py | 3 +- swarms/models/ultralytics_model.py | 6 +- swarms/models/vllm.py | 1 + swarms/models/zephyr.py | 1 + swarms/prompts/__init__.py | 2 +- swarms/prompts/base.py | 10 +- swarms/prompts/chat_prompt.py | 18 +- swarms/structs/SWARMS.md | 2 +- swarms/structs/__init__.py | 39 ++-- swarms/structs/agent.py | 34 +-- swarms/structs/async_workflow.py | 1 + swarms/structs/autoscaler.py | 4 +- swarms/structs/base.py | 12 +- swarms/structs/base_multiagent_structure.py | 4 +- swarms/structs/base_swarm.py | 28 --- swarms/structs/base_workflow.py | 3 +- swarms/structs/blocksdict.py | 6 +- swarms/structs/blockslist.py | 6 +- swarms/structs/company.py | 2 +- swarms/structs/concurrent_workflow.py | 3 +- swarms/structs/conversation.py | 6 +- swarms/structs/debate.py | 10 +- swarms/structs/graph_workflow.py | 5 +- swarms/structs/groupchat.py | 1 + swarms/structs/load_balancer.py | 3 +- swarms/structs/long_swarm.py | 153 ++++++++++++ swarms/structs/model_parallizer.py | 2 +- swarms/structs/multi_agent_collab.py | 2 +- swarms/structs/multi_threaded_workflow.py | 7 +- swarms/structs/nonlinear_workflow.py | 2 +- swarms/structs/recursive_workflow.py | 3 +- swarms/structs/schemas.py | 26 +-- swarms/structs/sequential_workflow.py | 2 +- swarms/structs/stackoverflow_swarm.py | 4 +- swarms/structs/swarming_architectures.py | 3 +- swarms/structs/task.py | 10 +- swarms/structs/task_queue_base.py | 1 + swarms/structs/test_majority_voting.py | 1 + swarms/structs/tool_json_schema.py | 3 +- swarms/structs/utils.py | 5 +- swarms/telemetry/__init__.py | 2 +- swarms/telemetry/auto_upgrade_swarms.py | 1 + swarms/telemetry/bootup.py | 2 +- swarms/telemetry/check_update.py | 6 +- swarms/telemetry/main.py | 5 +- swarms/telemetry/sys_info.py | 2 +- swarms/telemetry/user_utils.py | 7 +- swarms/tokenizers/__init__.py | 16 +- swarms/tokenizers/anthropic_tokenizer.py | 3 +- swarms/tokenizers/base_tokenizer.py | 7 +- swarms/tokenizers/cohere_tokenizer.py | 1 + swarms/tokenizers/openai_tokenizers.py | 10 +- swarms/tokenizers/r_tokenizers.py | 8 +- swarms/tools/__init__.py | 20 +- swarms/tools/code_executor.py | 2 +- swarms/tools/format_tools.py | 9 +- swarms/tools/logits_processor.py | 4 +- swarms/tools/tool.py | 164 ++++++------- swarms/tools/tool_func_doc_scraper.py | 1 + swarms/tools/tool_utils.py | 4 +- swarms/utils/README.md | 21 +- swarms/utils/__init__.py | 45 ++-- swarms/utils/apa.py | 8 +- swarms/utils/check_function_result.py | 18 +- swarms/utils/code_interpreter.py | 2 +- swarms/utils/data_to_text.py | 15 +- swarms/utils/device_checker_cuda.py | 5 +- swarms/utils/disable_logging.py | 4 - swarms/utils/dist_utils.py | 1 - swarms/utils/download_img.py | 7 +- swarms/utils/execute_futures.py | 4 +- swarms/utils/exponential_backoff.py | 3 +- swarms/utils/hash.py | 3 +- swarms/utils/json_output_parser.py | 1 + swarms/utils/jsonl_utils.py | 5 +- swarms/utils/loggers.py | 4 +- swarms/utils/prep_torch_model_inference.py | 1 + swarms/utils/remove_json_whitespace.py | 1 + swarms/utils/save_logs.py | 2 +- swarms/utils/serializable.py | 4 +- swarms/utils/video_to_frames.py | 42 ++++ swarms/utils/yaml_output_parser.py | 3 +- tests/README.md | 26 ++- tests/memory/test_dictinternalmemory.py | 4 +- tests/memory/test_dictsharedmemory.py | 2 + .../test_langchainchromavectormemory.py | 4 +- tests/memory/test_pinecone.py | 1 + tests/memory/test_qdrant.py | 3 +- tests/memory/test_short_term_memory.py | 3 +- tests/memory/test_sqlite.py | 4 +- tests/memory/test_weaviate.py | 4 +- tests/models/test_biogpt.py | 34 +-- tests/models/test_cohere.py | 1 + tests/models/test_elevenlab.py | 10 +- tests/models/test_gemini.py | 4 +- tests/models/test_gpt4_vision_api.py | 3 +- tests/models/test_hf.py | 8 +- tests/models/test_idefics.py | 6 +- tests/models/test_jina_embeds.py | 4 +- tests/models/test_llama_function_caller.py | 1 + tests/models/test_mistral.py | 1 + tests/models/test_mixtral.py | 4 +- tests/models/test_open_dalle.py | 1 + tests/models/test_openaitts.py | 4 +- tests/models/test_qwen.py | 1 + tests/models/test_speech_t5.py | 4 +- tests/models/test_ssd_1b.py | 3 +- tests/models/test_timm.py | 4 +- tests/models/test_timm_model.py | 4 +- tests/models/test_togther.py | 8 +- tests/models/test_ultralytics.py | 1 + tests/models/test_vilt.py | 6 +- tests/models/test_yi_200k.py | 1 + tests/structs/test_agent.py | 2 +- tests/structs/test_autoscaler.py | 3 +- tests/structs/test_base.py | 12 +- tests/structs/test_base_workflow.py | 7 +- tests/structs/test_company.py | 3 +- tests/structs/test_concurrent_workflow.py | 5 +- tests/structs/test_conversation.py | 1 + tests/structs/test_json.py | 4 +- tests/structs/test_majority_voting.py | 1 + tests/structs/test_model_parallizer.py | 5 +- tests/structs/test_multi_agent_collab.py | 12 +- tests/structs/test_nonlinear_workflow.py | 3 +- tests/structs/test_recursive_workflow.py | 4 +- tests/structs/test_task.py | 4 +- tests/structs/test_taskqueuebase.py | 4 +- tests/structs/test_tests_graph_workflow.py | 1 + tests/test_upload_tests_to_issues.py | 1 + tests/tokenizers/test_anthropictokenizer.py | 1 + tests/tokenizers/test_basetokenizer.py | 1 + tests/tokenizers/test_coheretokenizer.py | 4 +- tests/tokenizers/test_huggingfacetokenizer.py | 4 +- tests/tokenizers/test_openaitokenizer.py | 1 + tests/tokenizers/test_tokenizer.py | 3 +- tests/utils/test_check_device.py | 4 +- tests/utils/test_class_args_wrapper.py | 10 +- tests/utils/test_device.py | 4 +- tests/utils/test_display_markdown_message.py | 6 +- .../utils/test_extract_code_from_markdown.py | 1 + tests/utils/test_find_image_path.py | 4 +- tests/utils/test_limit_tokens_from_string.py | 1 + tests/utils/test_load_model_torch.py | 1 + tests/utils/test_load_models_torch.py | 4 +- tests/utils/test_metrics_decorator.py | 5 +- tests/utils/test_pdf_to_text.py | 2 +- tests/utils/test_prep_torch_inference.py | 4 +- .../utils/test_prep_torch_model_inference.py | 4 +- tests/utils/test_print_class_parameters.py | 1 + .../utils/test_subprocess_code_interpreter.py | 10 +- 358 files changed, 1802 insertions(+), 1494 deletions(-) create mode 100644 playground/demos/security_team/IMG_1625.MOV create mode 100644 swarms/structs/long_swarm.py create mode 100644 swarms/utils/video_to_frames.py diff --git a/README.md b/README.md index 3259d55b..4361355f 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ import os from dotenv import load_dotenv # Import the OpenAIChat model and the Agent struct -from swarms import OpenAIChat, Agent +from swarms import Agent, OpenAIChat # Load the environment variables load_dotenv() @@ -54,10 +54,7 @@ api_key = os.environ.get("OPENAI_API_KEY") # Initialize the language model llm = OpenAIChat( - temperature=0.5, - model_name="gpt-4", - openai_api_key=api_key, - max_tokens=4000 + temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000 ) @@ -66,9 +63,6 @@ agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True) # Run the workflow on a task agent.run("Generate a 10,000 word blog on health and wellness.") - - - ``` @@ -79,6 +73,7 @@ ToolAgent is an agent that outputs JSON using any model from huggingface. It tak ```python # Import necessary libraries from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Load the pre-trained model and tokenizer @@ -107,8 +102,6 @@ generated_data = agent.run(task) # Print the generated data print(generated_data) - - ``` @@ -124,8 +117,10 @@ The `Worker` is a simple all-in-one agent equipped with an LLM, tools, and RAG f ```python # Importing necessary modules import os + from dotenv import load_dotenv -from swarms import Worker, OpenAIChat, tool + +from swarms import OpenAIChat, Worker, tool # Loading environment variables from .env file load_dotenv() @@ -151,14 +146,10 @@ worker = Worker( ) # Running the worker with a prompt -out = worker.run( - "Hello, how are you? Create an image of how your are doing!" -) +out = worker.run("Hello, how are you? Create an image of how your are doing!") # Printing the output print(out) - - ``` ------ @@ -174,10 +165,12 @@ Sequential Workflow enables you to sequentially execute tasks with `Agent` and t ✅ Utilizes Agent class ```python -import os -from swarms import OpenAIChat, Agent, SequentialWorkflow +import os + from dotenv import load_dotenv +from swarms import Agent, OpenAIChat, SequentialWorkflow + load_dotenv() # Load the environment variables @@ -186,10 +179,7 @@ api_key = os.getenv("OPENAI_API_KEY") # Initialize the language agent llm = OpenAIChat( - temperature=0.5, - model_name="gpt-4", - openai_api_key=api_key, - max_tokens=4000 + temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000 ) @@ -207,12 +197,14 @@ workflow = SequentialWorkflow(max_loops=1) # Add tasks to the workflow workflow.add( - agent1, "Generate a 10,000 word blog on health and wellness.", + agent1, + "Generate a 10,000 word blog on health and wellness.", ) # Suppose the next task takes the output of the first task as input workflow.add( - agent2, "Summarize the generated blog", + agent2, + "Summarize the generated blog", ) # Run the workflow @@ -231,8 +223,10 @@ for task in workflow.tasks: ```python import os + from dotenv import load_dotenv -from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent + +from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task # Load environment variables from .env file load_dotenv() @@ -254,16 +248,17 @@ workflow.add(tasks=[task1, task2, task3]) # Run the workflow workflow.run() - ``` ### `RecursiveWorkflow` `RecursiveWorkflow` will keep executing the tasks until a specific token like is located inside the text! ```python -import os -from dotenv import load_dotenv -from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent +import os + +from dotenv import load_dotenv + +from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task # Load environment variables from .env file load_dotenv() @@ -287,8 +282,6 @@ workflow.add(task3) # Run the workflow workflow.run() - - ``` @@ -304,7 +297,7 @@ import os from dotenv import load_dotenv -from swarms import Anthropic, Gemini, Mixtral, OpenAIChat, ModelParallelizer +from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat load_dotenv() @@ -346,10 +339,7 @@ import os from dotenv import load_dotenv -from swarms import ( - OpenAIChat, - Conversation, -) +from swarms import Conversation, OpenAIChat conv = Conversation( time_enabled=True, @@ -364,6 +354,7 @@ api_key = os.environ.get("OPENAI_API_KEY") # Initialize the language model llm = OpenAIChat(openai_api_key=api_key, model_name="gpt-4") + # Run the language model in a loop def interactive_conversation(llm): conv = Conversation() @@ -372,9 +363,7 @@ def interactive_conversation(llm): conv.add("user", user_input) if user_input.lower() == "quit": break - task = ( - conv.return_history_as_string() - ) # Get the conversation history + task = conv.return_history_as_string() # Get the conversation history out = llm(task) conv.add("assistant", out) print( @@ -386,7 +375,6 @@ def interactive_conversation(llm): # Replace with your LLM instance interactive_conversation(llm) - ``` @@ -405,7 +393,7 @@ import os from dotenv import load_dotenv # Import the OpenAIChat model and the Agent struct -from swarms import OpenAIChat, Agent, SwarmNetwork +from swarms import Agent, OpenAIChat, SwarmNetwork # Load the environment variables load_dotenv() @@ -442,11 +430,8 @@ print(out) # Run all the agents in the swarm network on a task -out = swarmnet.run_many_agents( - "Generate a 10,000 word blog on health and wellness." -) +out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellness.") print(out) - ``` @@ -513,8 +498,6 @@ else: # Output the result of the task print(f"Task result: {task.result}") - - ``` --- @@ -535,14 +518,7 @@ from dotenv import load_dotenv from transformers import AutoModelForCausalLM, AutoTokenizer # Import the models, structs, and telemetry modules -from swarms import ( - Gemini, - GPT4VisionAPI, - Mixtral, - OpenAI, - ToolAgent, - BlocksList, -) +from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent # Load the environment variables load_dotenv() @@ -552,9 +528,7 @@ openai_api_key = os.getenv("OPENAI_API_KEY") gemini_api_key = os.getenv("GEMINI_API_KEY") # Tool Agent -model = AutoModelForCausalLM.from_pretrained( - "databricks/dolly-v2-12b" -) +model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b") tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b") json_schema = { "type": "object", @@ -565,9 +539,7 @@ json_schema = { "courses": {"type": "array", "items": {"type": "string"}}, }, } -toolagent = ToolAgent( - model=model, tokenizer=tokenizer, json_schema=json_schema -) +toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema) # Blocks List which enables you to build custom swarms by adding classes or functions swarm = BlocksList( @@ -619,9 +591,7 @@ blocks_by_parent_name = swarm.get_by_parent_name(swarm.name) blocks_by_parent_type = swarm.get_by_parent_type(type(swarm).__name__) # Get blocks by parent description -blocks_by_parent_description = swarm.get_by_parent_description( - swarm.description -) +blocks_by_parent_description = swarm.get_by_parent_description(swarm.description) # Run the block in the swarm inference = swarm.run_block(toolagent, "Hello World") @@ -636,25 +606,27 @@ Here's a production grade swarm ready for real-world deployment in a factory and ```python -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.logistics import ( + Efficiency_Agent_Prompt, Health_Security_Agent_Prompt, - Quality_Control_Agent_Prompt, Productivity_Agent_Prompt, + Quality_Control_Agent_Prompt, Safety_Agent_Prompt, Security_Agent_Prompt, Sustainability_Agent_Prompt, - Efficiency_Agent_Prompt, ) +from swarms.structs import Agent # Load ENV load_dotenv() api_key = os.getenv("OPENAI_API_KEY") -# GPT4VisionAPI +# GPT4VisionAPI llm = GPT4VisionAPI(openai_api_key=api_key) # Image for analysis @@ -686,9 +658,7 @@ productivity_agent = Agent( ) # Initiailize safety agent -safety_agent = Agent( - llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True -) +safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True) # Init the security agent security_agent = Agent( @@ -748,7 +718,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma ```python # Description: This is an example of how to use the Agent class to run a multi-modal workflow import os + from dotenv import load_dotenv + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.structs import Agent @@ -775,17 +747,11 @@ img = "assembly_line.jpg" ## Initialize the workflow agent = Agent( - llm=llm, - max_loops="auto", - autosave=True, - dashboard=True, - multi_modal=True + llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True ) # Run the workflow on a task agent.run(task=task, img=img) - - ``` --- @@ -857,14 +823,10 @@ model = QwenVLMultiModal( ) # Run the model -response = model( - "Hello, how are you?", "https://example.com/image.jpg" -) +response = model("Hello, how are you?", "https://example.com/image.jpg") # Print the response print(response) - - ``` @@ -882,7 +844,6 @@ out = model.run("Analyze the reciepts in this image", "docs.jpg") # Print the output print(out) - ``` @@ -923,8 +884,6 @@ model.set_max_length(200) # Clear the chat history of the model model.clear_chat_history() - - ``` ## Radically Simple AI Model APIs @@ -941,9 +900,7 @@ We provide a vast array of language and multi-modal model APIs for you to genera from swarms.models import Anthropic # Initialize an instance of the Anthropic class -model = Anthropic( - anthropic_api_key="" -) +model = Anthropic(anthropic_api_key="") # Using the run method completion_1 = model.run("What is the capital of France?") @@ -952,7 +909,6 @@ print(completion_1) # Using the __call__ method completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"]) print(completion_2) - ``` @@ -964,12 +920,16 @@ from swarms.models import HuggingfaceLLM custom_config = { "quantize": True, "quantization_config": {"load_in_4bit": True}, - "verbose": True + "verbose": True, } -inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config) +inference = HuggingfaceLLM( + model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config +) # Generate text based on a prompt -prompt_text = "Create a list of known biggest risks of structural collapse with references" +prompt_text = ( + "Create a list of known biggest risks of structural collapse with references" +) generated_text = inference(prompt_text) print(generated_text) ``` @@ -1027,7 +987,6 @@ task = "A person is walking on the street." # Generate the video! video_path = zeroscope(task) print(video_path) - ``` diff --git a/docs/applications/discord.md b/docs/applications/discord.md index cae3c8c1..dd7de16c 100644 --- a/docs/applications/discord.md +++ b/docs/applications/discord.md @@ -64,6 +64,7 @@ Initialize the `llm` (Language Learning Model) with your OpenAI API key: ```python from swarms.models import OpenAIChat + llm = OpenAIChat( openai_api_key="Your_OpenAI_API_Key", temperature=0.5, @@ -74,6 +75,7 @@ Initialize the bot with the `llm`: ```python from apps.discord import Bot + bot = Bot(llm=llm) ``` diff --git a/docs/examples/bingchat.md b/docs/examples/bingchat.md index 5ff93c63..3dabbf2e 100644 --- a/docs/examples/bingchat.md +++ b/docs/examples/bingchat.md @@ -46,6 +46,7 @@ You can also specify the conversation style: ```python from bing_chat import ConversationStyle + response = chat("Tell me a joke", style=ConversationStyle.creative) print(response) ``` diff --git a/docs/examples/flow.md b/docs/examples/flow.md index 92842491..2e635111 100644 --- a/docs/examples/flow.md +++ b/docs/examples/flow.md @@ -107,16 +107,17 @@ Now, let's create your first Agent. A Agent represents a chain-like structure th # Import necessary modules ```python -from swarms.models import OpenAIChat # Zephr, Mistral +from swarms.models import OpenAIChat # Zephr, Mistral from swarms.structs import Agent -api_key = ""# Initialize the language model (LLM) -llm = OpenAIChat(openai_api_key=api_key, temperature=0.5, max_tokens=3000)# Initialize the Agent object +api_key = "" # Initialize the language model (LLM) +llm = OpenAIChat( + openai_api_key=api_key, temperature=0.5, max_tokens=3000 +) # Initialize the Agent object -agent = Agent(llm=llm, max_loops=5)# Run the agent +agent = Agent(llm=llm, max_loops=5) # Run the agent out = agent.run("Create an financial analysis on the following metrics") print(out) - ``` ### [3. Initializing the Agent Object](https://github.com/kyegomez/swarms) diff --git a/docs/examples/omni_agent.md b/docs/examples/omni_agent.md index 56a6c996..e4d65498 100644 --- a/docs/examples/omni_agent.md +++ b/docs/examples/omni_agent.md @@ -55,7 +55,7 @@ from swarms.models import OpenAIChat llm = OpenAIChat(openai_api_key="sk-") agent = OmniModalAgent(llm) -response = agent.run("Create an video of a swarm of fish concept art, game art") +response = agent.run("Create an video of a swarm of fish concept art, game art") print(response) ``` diff --git a/docs/examples/revgpt.md b/docs/examples/revgpt.md index 5aa17af4..69107b40 100644 --- a/docs/examples/revgpt.md +++ b/docs/examples/revgpt.md @@ -35,7 +35,6 @@ The abstraction provided in `revgpt.py` is designed to simplify your interaction 1. **Import the Necessary Modules:** ```python -import os from dotenv import load_dotenv from revgpt import AbstractChatGPT ``` diff --git a/docs/examples/stacked_worker.md b/docs/examples/stacked_worker.md index cdcc537c..914d1754 100644 --- a/docs/examples/stacked_worker.md +++ b/docs/examples/stacked_worker.md @@ -28,8 +28,8 @@ The provided code showcases a system built around a worker node that utilizes va The code begins with import statements, bringing in necessary modules and classes. Key imports include the `OpenAIChat` class, which represents a language model, and several custom agents and tools from the `swarms` package. ```python -import os import interpreter # Assuming this is a custom module + from swarms.agents.hf_agents import HFAgent from swarms.agents.omni_modal_agent import OmniModalAgent from swarms.models import OpenAIChat @@ -59,11 +59,7 @@ All defined tools are appended to a list called `tools`. This list is later used ```python # Append tools to a list -tools = [ - hf_agent, - omni_agent, - compile -] +tools = [hf_agent, omni_agent, compile] ``` ### Initializing a Worker Node @@ -263,8 +259,6 @@ response = node.run(task) # Print the response print(response) - - ``` diff --git a/docs/examples/worker.md b/docs/examples/worker.md index 8fe2bf75..cd082aa4 100644 --- a/docs/examples/worker.md +++ b/docs/examples/worker.md @@ -53,11 +53,11 @@ Voila! You’re now ready to summon your Worker. Here’s a simple way to invoke the Worker and give it a task: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -75,8 +75,6 @@ node = Worker( task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times." response = node.run(task) print(response) - - ``` diff --git a/docs/swarms/agents/abstractagent.md b/docs/swarms/agents/abstractagent.md index cdd06715..8833c164 100644 --- a/docs/swarms/agents/abstractagent.md +++ b/docs/swarms/agents/abstractagent.md @@ -37,7 +37,6 @@ class AbstractAgent: def memory(self, memory_store): """init memory""" - pass def reset(self): """(Abstract method) Reset the agent.""" @@ -82,7 +81,7 @@ agent.reset() The `run` method allows the agent to perform a specific task. ```python -agent.run('some_task') +agent.run("some_task") ``` #### 3. `chat` @@ -90,7 +89,7 @@ agent.run('some_task') The `chat` method enables communication with the agent through a series of messages. ```python -messages = [{'id': 1, 'text': 'Hello, agent!'}, {'id': 2, 'text': 'How are you?'}] +messages = [{"id": 1, "text": "Hello, agent!"}, {"id": 2, "text": "How are you?"}] agent.chat(messages) ``` @@ -99,7 +98,7 @@ agent.chat(messages) The `step` method allows the agent to process a single message. ```python -agent.step('Hello, agent!') +agent.step("Hello, agent!") ``` ### Asynchronous Methods diff --git a/docs/swarms/agents/message.md b/docs/swarms/agents/message.md index 87794ebc..413ac016 100644 --- a/docs/swarms/agents/message.md +++ b/docs/swarms/agents/message.md @@ -44,7 +44,7 @@ class Message: def __repr__(self): """ __repr__ represents the string representation of the Message object. - + Returns: (str) A string containing the timestamp, sender, and content of the message. """ @@ -60,10 +60,7 @@ The `Message` class represents a message in the agent system. Upon initializatio Creating a `Message` object and displaying its string representation. ```python -mes = Message( - sender = "Kye", - content = "Hello! How are you?" -) +mes = Message(sender="Kye", content="Hello! How are you?") print(mes) ``` @@ -80,9 +77,7 @@ Creating a `Message` object with metadata. ```python metadata = {"priority": "high", "category": "urgent"} mes_with_metadata = Message( - sender = "Alice", - content = "Important update", - metadata = metadata + sender="Alice", content="Important update", metadata=metadata ) print(mes_with_metadata) @@ -98,10 +93,7 @@ Output: Creating a `Message` object without providing metadata. ```python -mes_no_metadata = Message( - sender = "Bob", - content = "Reminder: Meeting at 2PM" -) +mes_no_metadata = Message(sender="Bob", content="Reminder: Meeting at 2PM") print(mes_no_metadata) ``` diff --git a/docs/swarms/agents/omni_agent.md b/docs/swarms/agents/omni_agent.md index a80da0e6..888e824f 100644 --- a/docs/swarms/agents/omni_agent.md +++ b/docs/swarms/agents/omni_agent.md @@ -39,10 +39,12 @@ For streaming mode, this function yields the response token by token, ensuring a ## Examples & Use Cases Initialize the `OmniModalAgent` and communicate with it: ```python +import os + +from dotenv import load_dotenv + from swarms.agents.omni_modal_agent import OmniModalAgent, OpenAIChat from swarms.models import OpenAIChat -from dotenv import load_dotenv -import os # Load the environment variables load_dotenv() diff --git a/docs/swarms/agents/toolagent.md b/docs/swarms/agents/toolagent.md index ebb00623..35a31f99 100644 --- a/docs/swarms/agents/toolagent.md +++ b/docs/swarms/agents/toolagent.md @@ -69,6 +69,7 @@ The `ToolAgent` class takes the following arguments: ```python from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Creating a model and tokenizer @@ -82,11 +83,8 @@ json_schema = { "name": {"type": "string"}, "age": {"type": "number"}, "is_student": {"type": "boolean"}, - "courses": { - "type": "array", - "items": {"type": "string"} - } - } + "courses": {"type": "array", "items": {"type": "string"}}, + }, } # Defining a task diff --git a/docs/swarms/agents/workeragent.md b/docs/swarms/agents/workeragent.md index e46ec1af..aaa71653 100644 --- a/docs/swarms/agents/workeragent.md +++ b/docs/swarms/agents/workeragent.md @@ -38,7 +38,7 @@ worker = Worker( human_in_the_loop=False, temperature=0.5, llm=some_language_model, - openai_api_key="my_key" + openai_api_key="my_key", ) worker.run("What's the weather in Miami?") ``` @@ -56,11 +56,11 @@ worker.send() ```python external_tools = [MyTool1(), MyTool2()] worker = Worker( -name="My Worker", -role="Worker", -external_tools=external_tools, -human_in_the_loop=False, -temperature=0.5, + name="My Worker", + role="Worker", + external_tools=external_tools, + human_in_the_loop=False, + temperature=0.5, ) ``` diff --git a/docs/swarms/chunkers/basechunker.md b/docs/swarms/chunkers/basechunker.md index 33b03312..11c32dc5 100644 --- a/docs/swarms/chunkers/basechunker.md +++ b/docs/swarms/chunkers/basechunker.md @@ -69,7 +69,9 @@ from basechunker import BaseChunker, ChunkSeparator chunker = BaseChunker() # Text to be chunked -input_text = "This is a long text that needs to be split into smaller chunks for processing." +input_text = ( + "This is a long text that needs to be split into smaller chunks for processing." +) # Chunk the text chunks = chunker.chunk(input_text) diff --git a/docs/swarms/chunkers/pdf_chunker.md b/docs/swarms/chunkers/pdf_chunker.md index 8c92060d..d3eb02a9 100644 --- a/docs/swarms/chunkers/pdf_chunker.md +++ b/docs/swarms/chunkers/pdf_chunker.md @@ -62,8 +62,8 @@ Let's explore how to use the `PdfChunker` class with different scenarios and app #### Example 1: Basic Chunking ```python -from swarms.chunkers.pdf_chunker import PdfChunker from swarms.chunkers.chunk_seperator import ChunkSeparator +from swarms.chunkers.pdf_chunker import PdfChunker # Initialize the PdfChunker pdf_chunker = PdfChunker() @@ -82,8 +82,8 @@ for idx, chunk in enumerate(chunks, start=1): #### Example 2: Custom Separators ```python -from swarms.chunkers.pdf_chunker import PdfChunker from swarms.chunkers.chunk_seperator import ChunkSeparator +from swarms.chunkers.pdf_chunker import PdfChunker # Define custom separators for PDF chunking custom_separators = [ChunkSeparator("\n\n"), ChunkSeparator(". ")] diff --git a/docs/swarms/index.md b/docs/swarms/index.md index 98c6b7a3..b052b6ce 100644 --- a/docs/swarms/index.md +++ b/docs/swarms/index.md @@ -28,7 +28,6 @@ We have a small gallery of examples to run here, [for more check out the docs to - Enterprise Grade + Production Grade: `Agent` is designed and optimized for automating real-world tasks at scale! ```python - from swarms.models import OpenAIChat from swarms.structs import Agent @@ -64,9 +63,6 @@ out = agent.run("Generate a 10,000 word blog on health and wellness.") # out = agent.print_history_and_memory() # # out = agent.save_state("flow_state.json") # print(out) - - - ``` ------ @@ -82,9 +78,7 @@ from swarms.structs import Agent 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 agent llm = OpenAIChat( @@ -118,7 +112,6 @@ workflow.run() # Output the results for task in workflow.tasks: print(f"Task: {task.description}, Result: {task.result}") - ``` --- diff --git a/docs/swarms/memory/pg.md b/docs/swarms/memory/pg.md index 84878a76..3695e11c 100644 --- a/docs/swarms/memory/pg.md +++ b/docs/swarms/memory/pg.md @@ -110,7 +110,9 @@ def setup( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Set up the database with default settings vector_store.setup() @@ -120,10 +122,14 @@ vector_store.setup() ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Set up the database with customized settings -vector_store.setup(create_schema=False, install_uuid_extension=True, install_vector_extension=True) +vector_store.setup( + create_schema=False, install_uuid_extension=True, install_vector_extension=True +) ``` ### 4.2 Upserting Vectors @@ -137,7 +143,7 @@ def upsert_vector( vector_id: Optional[str] = None, namespace: Optional[str] = None, meta: Optional[dict] = None, - **kwargs + **kwargs, ) -> str: """ Inserts or updates a vector in the collection. @@ -158,7 +164,9 @@ def upsert_vector( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Define a vector and upsert it vector = [0.1, 0.2, 0.3, 0.4] @@ -167,10 +175,7 @@ namespace = "your-namespace" meta = {"key1": "value1", "key2": "value2"} vector_store.upsert_vector( - vector=vector, - vector_id=vector_id, - namespace=namespace, - meta=meta + vector=vector, vector_id=vector_id, namespace=namespace, meta=meta ) ``` @@ -222,9 +227,7 @@ else: The `load_entries` method allows you to load all vector entries from the collection, optionally filtering by namespace. ```python -def load_entries( - self, namespace: Optional[str] = None -) -> list[BaseVectorStore.Entry]: +def load_entries(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. @@ -240,7 +243,9 @@ def load_entries( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Load all vector entries in the specified namespace entries = vector_store.load_entries(namespace="your-namespace") @@ -266,7 +271,7 @@ def query( namespace: Optional[str] = None, include_vectors: bool = False, distance_metric: str = "cosine_distance", - **kwargs + **kwargs, ) -> list[BaseVectorStore.QueryResult]: """ Performs a search on the collection to find vectors similar to the provided input vector, @@ -290,7 +295,9 @@ def query( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Perform a vector query query_string = "your-query-string" @@ -304,7 +311,7 @@ results = vector_store.query( count=count, namespace=namespace, include_vectors=include_vectors, - distance_metric=distance_metric + distance_metric=distance_metric, ) # Process the query results diff --git a/docs/swarms/memory/pinecone.md b/docs/swarms/memory/pinecone.md index 830d10fe..f8ca0f2e 100644 --- a/docs/swarms/memory/pinecone.md +++ b/docs/swarms/memory/pinecone.md @@ -174,7 +174,7 @@ pv = PineconeVector( api_key="your-api-key", index_name="your-index-name", environment="us-west1-gcp", - project_name="your-project-name" + project_name="your-project-name", ) ``` @@ -198,12 +198,7 @@ vector_id = "unique-vector-id" namespace = "your-namespace" meta = {"key1": "value1", "key2": "value2"} -pv.upsert_vector( - vector=vector, - vector_id=vector_id, - namespace=namespace, - meta=meta -) +pv.upsert_vector(vector=vector, vector_id=vector_id, namespace=namespace, meta=meta) ``` ### 4.4 Querying the Index @@ -222,7 +217,7 @@ results = pv.query( count=count, namespace=namespace, include_vectors=include_vectors, - include_metadata=include_metadata + include_metadata=include_metadata, ) # Process the query results diff --git a/docs/swarms/memory/qdrant.md b/docs/swarms/memory/qdrant.md index 3717d94f..e234e7be 100644 --- a/docs/swarms/memory/qdrant.md +++ b/docs/swarms/memory/qdrant.md @@ -14,8 +14,15 @@ pip install qdrant-client sentence-transformers httpx ```python class Qdrant: - def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True): - ... + def __init__( + self, + api_key: str, + host: str, + port: int = 6333, + collection_name: str = "qdrant", + model_name: str = "BAAI/bge-small-en-v1.5", + https: bool = True, + ): ... ``` ### Constructor Parameters @@ -60,10 +67,7 @@ qdrant_client = Qdrant(api_key="your_api_key", host="localhost", port=6333) ### Example 2: Adding Vectors to a Collection ```python -documents = [ - {"page_content": "Sample text 1"}, - {"page_content": "Sample text 2"} -] +documents = [{"page_content": "Sample text 1"}, {"page_content": "Sample text 2"}] operation_info = qdrant_client.add_vectors(documents) print(operation_info) diff --git a/docs/swarms/memory/short_term_memory.md b/docs/swarms/memory/short_term_memory.md index 2aabbd5c..9ee3a738 100644 --- a/docs/swarms/memory/short_term_memory.md +++ b/docs/swarms/memory/short_term_memory.md @@ -125,7 +125,9 @@ def update_short_term(self, index, role: str, message: str, *args, **kwargs): ##### Example: Updating a Message in Short-Term Memory ```python -memory.update_short_term(index=0, role="Updated Role", message="Updated message content.") +memory.update_short_term( + index=0, role="Updated Role", message="Updated message content." +) ``` #### 7. `clear` diff --git a/docs/swarms/memory/weaviate.md b/docs/swarms/memory/weaviate.md index b23baedf..dc264653 100644 --- a/docs/swarms/memory/weaviate.md +++ b/docs/swarms/memory/weaviate.md @@ -82,7 +82,7 @@ weaviate_client.create_collection( {"name": "property1", "dataType": ["string"]}, {"name": "property2", "dataType": ["int"]}, ], - vectorizer_config=None # Optional vectorizer configuration + vectorizer_config=None, # Optional vectorizer configuration ) ``` @@ -99,8 +99,7 @@ The `add` method allows you to add an object to a specified collection in Weavia ```python weaviate_client.add( - collection_name="my_collection", - properties={"property1": "value1", "property2": 42} + collection_name="my_collection", properties={"property1": "value1", "property2": 42} ) ``` @@ -142,7 +141,7 @@ The `update` method allows you to update an object in a specified collection in weaviate_client.update( collection_name="my_collection", object_id="object123", - properties={"property1": "new_value", "property2": 99} + properties={"property1": "new_value", "property2": 99}, ) ``` @@ -158,10 +157,7 @@ The `delete` method allows you to delete an object from a specified collection i #### Usage ```python -weaviate_client.delete( - collection_name="my_collection", - object_id="object123" -) +weaviate_client.delete(collection_name="my_collection", object_id="object123") ``` ## Examples @@ -175,28 +171,21 @@ weaviate_client.create_collection( name="people", properties=[ {"name": "name", "dataType": ["string"]}, - {"name": "age", "dataType": ["int"]} - ] + {"name": "age", "dataType": ["int"]}, + ], ) ``` ### Example 2: Adding an Object ```python -weaviate_client.add( - collection_name="people", - properties={"name": "John", "age": 30} -) +weaviate_client.add(collection_name="people", properties={"name": "John", "age": 30}) ``` ### Example 3: Querying Objects ```python -results = weaviate_client.query( - collection_name="people", - query="name:John", - limit=5 -) +results = weaviate_client.query(collection_name="people", query="name:John", limit=5) ``` These examples cover the basic operations of creating collections, adding objects, and querying objects using the Weaviate API Client. diff --git a/docs/swarms/models/anthropic.md b/docs/swarms/models/anthropic.md index 85e7a428..438adfbe 100644 --- a/docs/swarms/models/anthropic.md +++ b/docs/swarms/models/anthropic.md @@ -72,9 +72,7 @@ class Anthropic: from swarms.models import Anthropic # Initialize an instance of the Anthropic class -model = Anthropic( - anthropic_api_key="" -) +model = Anthropic(anthropic_api_key="") # Using the run method completion_1 = model.run("What is the capital of France?") diff --git a/docs/swarms/models/base_multimodal_model.md b/docs/swarms/models/base_multimodal_model.md index 13efa11c..c1a8373d 100644 --- a/docs/swarms/models/base_multimodal_model.md +++ b/docs/swarms/models/base_multimodal_model.md @@ -149,7 +149,9 @@ model = BaseMultiModalModel( ) # Run the model with a text task and an image URL -response = model.run("Generate a summary of this text", "https://www.example.com/image.jpg") +response = model.run( + "Generate a summary of this text", "https://www.example.com/image.jpg" +) print(response) ``` @@ -209,6 +211,7 @@ for response in responses: ```python from swarms.models import BaseMultiModalModel + class CustomMultiModalModel(BaseMultiModalModel): def __init__(self, model_name, custom_parameter, *args, **kwargs): # Call the parent class constructor @@ -226,6 +229,7 @@ class CustomMultiModalModel(BaseMultiModalModel): # You can use self.custom_parameter and other inherited attributes pass + # Create an instance of your custom multimodal model custom_model = CustomMultiModalModel( model_name="your_custom_model_name", @@ -236,7 +240,9 @@ custom_model = CustomMultiModalModel( ) # Run your custom model -response = custom_model.run("Generate a summary of this text", "https://www.example.com/image.jpg") +response = custom_model.run( + "Generate a summary of this text", "https://www.example.com/image.jpg" +) print(response) # Generate a summary using your custom model diff --git a/docs/swarms/models/bingchat.md b/docs/swarms/models/bingchat.md index 70c184d3..a98aa8be 100644 --- a/docs/swarms/models/bingchat.md +++ b/docs/swarms/models/bingchat.md @@ -39,7 +39,6 @@ print(response) ```python from swarms.models.bing_chat import BingChat - edgegpt = BingChat(cookies_path="./path/to/cookies.json") response = edgegpt("Hello, my name is ChatGPT") print(response) @@ -48,7 +47,9 @@ print(response) 3. Generate an image based on a text prompt: ```python -image_path = edgegpt.create_img("Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie") +image_path = edgegpt.create_img( + "Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie" +) print(f"Generated image saved at {image_path}") ``` @@ -59,7 +60,9 @@ from swarms.models.bing_chat import BingChat edgegpt = BingChat(cookies_path="./path/to/cookies.json") -image_path = edgegpt.create_img("Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie") +image_path = edgegpt.create_img( + "Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie" +) print(f"Generated image saved at {image_path}") ``` diff --git a/docs/swarms/models/biogpt.md b/docs/swarms/models/biogpt.md index c43557b6..bb4e8914 100644 --- a/docs/swarms/models/biogpt.md +++ b/docs/swarms/models/biogpt.md @@ -83,7 +83,6 @@ print(generated_text) ```python from swarms.models import BioGPT - # Initialize the BioGPT model biogpt = BioGPT() @@ -99,7 +98,6 @@ print(features) ```python from swarms.models import BioGPT - # Initialize the BioGPT model biogpt = BioGPT() diff --git a/docs/swarms/models/distilled_whisperx.md b/docs/swarms/models/distilled_whisperx.md index e9339c1e..79c8c2ea 100644 --- a/docs/swarms/models/distilled_whisperx.md +++ b/docs/swarms/models/distilled_whisperx.md @@ -29,7 +29,7 @@ from swarms.models import DistilWhisperModel model_wrapper = DistilWhisperModel() # Initialize with a specific model ID -model_wrapper = DistilWhisperModel(model_id='distil-whisper/distil-large-v2') +model_wrapper = DistilWhisperModel(model_id="distil-whisper/distil-large-v2") ``` ## Attributes @@ -62,7 +62,7 @@ Transcribes audio input synchronously. ```python # Synchronous transcription -transcription = model_wrapper.transcribe('path/to/audio.mp3') +transcription = model_wrapper.transcribe("path/to/audio.mp3") print(transcription) ``` @@ -84,7 +84,7 @@ Transcribes audio input asynchronously. import asyncio # Asynchronous transcription -transcription = asyncio.run(model_wrapper.async_transcribe('path/to/audio.mp3')) +transcription = asyncio.run(model_wrapper.async_transcribe("path/to/audio.mp3")) print(transcription) ``` @@ -103,7 +103,7 @@ Simulates real-time transcription of an audio file. ```python # Real-time transcription simulation -model_wrapper.real_time_transcribe('path/to/audio.mp3', chunk_duration=5) +model_wrapper.real_time_transcribe("path/to/audio.mp3", chunk_duration=5) ``` ## Error Handling diff --git a/docs/swarms/models/gpt4v.md b/docs/swarms/models/gpt4v.md index 69968623..5ad80cd9 100644 --- a/docs/swarms/models/gpt4v.md +++ b/docs/swarms/models/gpt4v.md @@ -107,16 +107,16 @@ The `__call__` method is a convenient way to run the GPT-4 Vision model. It has ```python def __call__(task: str, img: str) -> str: """ - Run the GPT-4 Vision model (callable). + Run the GPT-4 Vision model (callable). - Parameters: - - task (str): The task or question related to the image. - - img + Parameters: + - task (str): The task or question related to the image. + - img - (str): URL of the image to analyze. + (str): URL of the image to analyze. - Returns: - str: The model's response. + Returns: + str: The model's response. """ ``` diff --git a/docs/swarms/models/huggingface.md b/docs/swarms/models/huggingface.md index 8606d8f2..50aaa2a1 100644 --- a/docs/swarms/models/huggingface.md +++ b/docs/swarms/models/huggingface.md @@ -114,9 +114,11 @@ from swarms.models import HuggingfaceLLM custom_config = { "quantize": True, "quantization_config": {"load_in_4bit": True}, - "verbose": True + "verbose": True, } -inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config) +inference = HuggingfaceLLM( + model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config +) # Generate text based on a prompt prompt_text = "Tell me a joke" diff --git a/docs/swarms/models/idefics.md b/docs/swarms/models/idefics.md index 04822fdd..57125038 100644 --- a/docs/swarms/models/idefics.md +++ b/docs/swarms/models/idefics.md @@ -36,7 +36,9 @@ model = Idefics() 2. Generate text based on prompts: ```python -prompts = ["User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"] +prompts = [ + "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG" +] response = model(prompts) print(response) ``` @@ -47,7 +49,9 @@ print(response) from swarms.models import Idefics model = Idefics() -prompts = ["User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"] +prompts = [ + "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG" +] response = model(prompts) print(response) ``` diff --git a/docs/swarms/models/index.md b/docs/swarms/models/index.md index 93883779..9e001eea 100644 --- a/docs/swarms/models/index.md +++ b/docs/swarms/models/index.md @@ -42,9 +42,10 @@ OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None **Usage Example:** ```python -from swarms import OpenAI import asyncio +from swarms import OpenAI + chat = OpenAI(api_key="YOUR_OPENAI_API_KEY") response = chat.generate("Hello, how can I assist you?") @@ -126,7 +127,10 @@ GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None from swarms import GooglePalm google_palm = GooglePalm() -messages = [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}] +messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Tell me a joke"}, +] response = google_palm.generate(messages) print(response["choices"][0]["text"]) diff --git a/docs/swarms/models/kosmos.md b/docs/swarms/models/kosmos.md index 1735e153..a19ea791 100644 --- a/docs/swarms/models/kosmos.md +++ b/docs/swarms/models/kosmos.md @@ -30,7 +30,9 @@ kosmos = Kosmos() 2. Perform Multimodal Grounding: ```python -kosmos.multimodal_grounding("Find the red apple in the image.", "https://example.com/apple.jpg") +kosmos.multimodal_grounding( + "Find the red apple in the image.", "https://example.com/apple.jpg" +) ``` ### Example 1 - Multimodal Grounding @@ -40,13 +42,17 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.multimodal_grounding("Find the red apple in the image.", "https://example.com/apple.jpg") +kosmos.multimodal_grounding( + "Find the red apple in the image.", "https://example.com/apple.jpg" +) ``` 3. Perform Referring Expression Comprehension: ```python -kosmos.referring_expression_comprehension("Show me the green bottle.", "https://example.com/bottle.jpg") +kosmos.referring_expression_comprehension( + "Show me the green bottle.", "https://example.com/bottle.jpg" +) ``` ### Example 2 - Referring Expression Comprehension @@ -56,13 +62,17 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.referring_expression_comprehension("Show me the green bottle.", "https://example.com/bottle.jpg") +kosmos.referring_expression_comprehension( + "Show me the green bottle.", "https://example.com/bottle.jpg" +) ``` 4. Generate Referring Expressions: ```python -kosmos.referring_expression_generation("It is on the table.", "https://example.com/table.jpg") +kosmos.referring_expression_generation( + "It is on the table.", "https://example.com/table.jpg" +) ``` ### Example 3 - Referring Expression Generation @@ -72,7 +82,9 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.referring_expression_generation("It is on the table.", "https://example.com/table.jpg") +kosmos.referring_expression_generation( + "It is on the table.", "https://example.com/table.jpg" +) ``` 5. Perform Grounded Visual Question Answering (VQA): @@ -127,7 +139,10 @@ kosmos.grounded_image_captioning_detailed("https://example.com/beach.jpg") ```python image = kosmos.get_image("https://example.com/image.jpg") -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] kosmos.draw_entity_boxes_on_image(image, entities, show=True) ``` @@ -139,24 +154,38 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() image = kosmos.get_image("https://example.com/image.jpg") -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] kosmos.draw_entity_boxes_on_image(image, entities, show=True) ``` 9. Generate Boxes for Entities: ```python -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] -image = kosmos.generate_boxes("Find the apple and the banana in the image.", "https://example.com/image.jpg") +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] +image = kosmos.generate_boxes( + "Find the apple and the banana in the image.", "https://example.com/image.jpg" +) ``` ### Example 8 - Generating Boxes for Entities ```python from swarms.models.kosmos_two import Kosmos + kosmos = Kosmos() -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] -image = kosmos.generate_boxes("Find the apple and the banana in the image.", "https://example.com/image.jpg") +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] +image = kosmos.generate_boxes( + "Find the apple and the banana in the image.", "https://example.com/image.jpg" +) ``` ## How Kosmos Works diff --git a/docs/swarms/models/mistral.md b/docs/swarms/models/mistral.md index c8dc179c..6c38cd97 100644 --- a/docs/swarms/models/mistral.md +++ b/docs/swarms/models/mistral.md @@ -150,7 +150,6 @@ Example: ```python from swarms.models import Mistral - model = Mistral() task = "Translate the following English text to French: 'Hello, how are you?'" result = model.run(task) diff --git a/docs/swarms/models/mpt.md b/docs/swarms/models/mpt.md index 41f3ec74..0592284b 100644 --- a/docs/swarms/models/mpt.md +++ b/docs/swarms/models/mpt.md @@ -52,10 +52,10 @@ class MPT7B: from swarms.models import MPT7B # Initialize the MPT7B class -mpt = MPT7B('mosaicml/mpt-7b-storywriter', 'EleutherAI/gpt-neox-20b', max_tokens=150) +mpt = MPT7B("mosaicml/mpt-7b-storywriter", "EleutherAI/gpt-neox-20b", max_tokens=150) # Generate text -output = mpt.run('generate', 'Once upon a time in a land far, far away...') +output = mpt.run("generate", "Once upon a time in a land far, far away...") print(output) ``` @@ -77,13 +77,16 @@ print(outputs) ```python import asyncio + from swarms.models import MPT7B # Initialize the MPT7B class -mpt = MPT7B('mosaicml/mpt-7b-storywriter', 'EleutherAI/gpt-neox-20b', max_tokens=150) +mpt = MPT7B("mosaicml/mpt-7b-storywriter", "EleutherAI/gpt-neox-20b", max_tokens=150) # Generate text asynchronously -output = asyncio.run(mpt.run_async('generate', 'Once upon a time in a land far, far away...')) +output = asyncio.run( + mpt.run_async("generate", "Once upon a time in a land far, far away...") +) print(output) ``` diff --git a/docs/swarms/models/openai.md b/docs/swarms/models/openai.md index f173619d..ae547631 100644 --- a/docs/swarms/models/openai.md +++ b/docs/swarms/models/openai.md @@ -168,7 +168,11 @@ prompt = "Translate the following English text to French: 'Hello, how are you?'" generated_text = openai.generate(prompt, max_tokens=50) # Generate text from multiple prompts -prompts = ["Translate this: 'Good morning' to Spanish.", "Summarize the following article:", article_text] +prompts = [ + "Translate this: 'Good morning' to Spanish.", + "Summarize the following article:", + article_text, +] generated_texts = openai.generate(prompts, max_tokens=100) # Generate text asynchronously @@ -188,7 +192,7 @@ custom_options = { "max_tokens": 100, "top_p": 0.9, "frequency_penalty": 0.2, - "presence_penalty": 0.4 + "presence_penalty": 0.4, } generated_text = openai.generate(prompt, **custom_options) ``` diff --git a/docs/swarms/models/openai_chat.md b/docs/swarms/models/openai_chat.md index a2ef9811..d7d9b2eb 100644 --- a/docs/swarms/models/openai_chat.md +++ b/docs/swarms/models/openai_chat.md @@ -150,7 +150,9 @@ user_message = "User: Tell me another joke." response = openai_chat.generate([user_message]) # Print the generated response -print(response[0][0].text) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!" +print( + response[0][0].text +) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!" ``` ### Example 3: Asynchronous Generation @@ -158,12 +160,14 @@ print(response[0][0].text) # Output: "Assistant: Why don't scientists trust ato ```python import asyncio + # Define an asynchronous function for generating responses async def generate_responses(): user_message = "User: Tell me a fun fact." async for chunk in openai_chat.stream([user_message]): print(chunk.text) + # Run the asynchronous generation function asyncio.run(generate_responses()) ``` diff --git a/docs/swarms/models/vilt.md b/docs/swarms/models/vilt.md index e2c6f325..2cb56b22 100644 --- a/docs/swarms/models/vilt.md +++ b/docs/swarms/models/vilt.md @@ -26,20 +26,26 @@ To use the Vilt model, follow these steps: ```python from swarms.models import Vilt + model = Vilt() ``` 2. Call the model with a text question and an image URL: ```python -output = model("What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg" +) ``` ### Example 1 - Image Questioning ```python model = Vilt() -output = model("What are the objects in this image?", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "What are the objects in this image?", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` @@ -47,7 +53,10 @@ print(output) ```python model = Vilt() -output = model("Describe the scene in this image.", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "Describe the scene in this image.", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` @@ -55,7 +64,10 @@ print(output) ```python model = Vilt() -output = model("Tell me more about the landmark in this image.", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "Tell me more about the landmark in this image.", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` diff --git a/docs/swarms/models/vllm.md b/docs/swarms/models/vllm.md index 06a7c8f8..778c1b2b 100644 --- a/docs/swarms/models/vllm.md +++ b/docs/swarms/models/vllm.md @@ -63,7 +63,7 @@ custom_vllm = vLLM( trust_remote_code=True, revision="abc123", temperature=0.7, - top_p=0.8 + top_p=0.8, ) ``` @@ -108,7 +108,7 @@ custom_vllm = vLLM( trust_remote_code=True, revision="abc123", temperature=0.7, - top_p=0.8 + top_p=0.8, ) # Generate text with custom configuration @@ -128,7 +128,7 @@ vllm = vLLM() tasks = [ "Translate the following sentence to French: 'Hello, world!'", "Write a short story set in a futuristic world.", - "Summarize the main points of a news article about climate change." + "Summarize the main points of a news article about climate change.", ] for task in tasks: diff --git a/docs/swarms/models/zephyr.md b/docs/swarms/models/zephyr.md index d9522711..76782ead 100644 --- a/docs/swarms/models/zephyr.md +++ b/docs/swarms/models/zephyr.md @@ -45,6 +45,7 @@ To use the Zephyr model, follow these steps: ```python from swarms.models import Zephyr + model = Zephyr(max_new_tokens=300, temperature=0.7, top_k=50, top_p=0.95) ``` diff --git a/docs/swarms/structs/abstractswarm.md b/docs/swarms/structs/abstractswarm.md index 78e28493..6bdf736b 100644 --- a/docs/swarms/structs/abstractswarm.md +++ b/docs/swarms/structs/abstractswarm.md @@ -47,9 +47,11 @@ The `AbstractSwarm` class is an abstract base class that serves as the foundatio ```python from abc import ABC, abstractmethod -from typing import Optional, List, Dict, Any +from typing import List + from swarms.swarms.base import AbstractWorker + class AbstractSwarm(ABC): """ Abstract class for swarm simulation architectures @@ -58,12 +60,12 @@ class AbstractSwarm(ABC): --------- ... """ + # The class definition and constructor are provided here. @abstractmethod def __init__(self, workers: List["AbstractWorker"]): """Initialize the swarm with workers""" - pass # Other abstract methods are listed here. ``` diff --git a/docs/swarms/structs/agent.md b/docs/swarms/structs/agent.md index f04bba02..8408b0b8 100644 --- a/docs/swarms/structs/agent.md +++ b/docs/swarms/structs/agent.md @@ -68,7 +68,9 @@ final_response = agent.run(initial_task) You can collect feedback during the conversation using the `provide_feedback` method: ```python -agent.provide_feedback("Generate an SOP for new sales employees on the best cold sales practices") +agent.provide_feedback( + "Generate an SOP for new sales employees on the best cold sales practices" +) ``` ### Stopping Condition @@ -78,9 +80,11 @@ You can define a custom stopping condition using a function. For example, you ca ```python from swarms.structs import Agent + def stop_when_repeats(response: str) -> bool: return "Stop" in response.lower() + agent = Agent(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats) ``` @@ -107,9 +111,9 @@ Here are three usage examples: ### Example 1: Simple Conversation ```python -from swarms.structs import Agent # Select any Language model from the models folder from swarms.models import Mistral, OpenAIChat +from swarms.structs import Agent llm = Mistral() # llm = OpenAIChat() @@ -128,9 +132,11 @@ final_response = agent.run(initial_task) ```python from swarms.structs import Agent + def stop_when_repeats(response: str) -> bool: return "Stop" in response.lower() + agent = Agent(llm=llm, max_loops=5, stopping_condition=stop_when_repeats) ``` diff --git a/docs/swarms/structs/artifact.md b/docs/swarms/structs/artifact.md index 52444d9f..9e00f083 100644 --- a/docs/swarms/structs/artifact.md +++ b/docs/swarms/structs/artifact.md @@ -41,9 +41,7 @@ class Artifact(BaseModel): ) relative_path: Optional[str] = Field( None, - description=( - "Relative path of the artifact in the agent's workspace" - ), + description=("Relative path of the artifact in the agent's workspace"), example="python/code/", ) ``` @@ -64,7 +62,7 @@ from swarms.structs import Artifact artifact_instance = Artifact( artifact_id="b225e278-8b4c-4f99-a696-8facf19f0e56", file_name="main.py", - relative_path="python/code/" + relative_path="python/code/", ) ``` @@ -85,8 +83,7 @@ If the `relative_path` attribute is not provided during artifact creation, it wi ```python artifact_instance_no_path = Artifact( - artifact_id="c280s347-9b7d-3c68-m337-7abvf50j23k", - file_name="script.js" + artifact_id="c280s347-9b7d-3c68-m337-7abvf50j23k", file_name="script.js" ) print(artifact_instance_no_path.relative_path) diff --git a/docs/swarms/structs/artifactupload.md b/docs/swarms/structs/artifactupload.md index 533f7d53..90b30f58 100644 --- a/docs/swarms/structs/artifactupload.md +++ b/docs/swarms/structs/artifactupload.md @@ -15,9 +15,7 @@ class ArtifactUpload(BaseModel): file: bytes = Field(..., description="File to upload") relative_path: Optional[str] = Field( None, - description=( - "Relative path of the artifact in the agent's workspace" - ), + description=("Relative path of the artifact in the agent's workspace"), example="python/code/", ) ``` @@ -32,10 +30,12 @@ The `ArtifactUpload` class is used to create an instance of an artifact upload. from swarms.structs import ArtifactUpload # Uploading a file with no relative path -upload_no_path = ArtifactUpload(file=b'example_file_contents') +upload_no_path = ArtifactUpload(file=b"example_file_contents") # Uploading a file with a relative path -upload_with_path = ArtifactUpload(file=b'example_file_contents', relative_path="python/code/") +upload_with_path = ArtifactUpload( + file=b"example_file_contents", relative_path="python/code/" +) ``` In the above example, `upload_no_path` is an instance of `ArtifactUpload` with no specified `relative_path`, whereas `upload_with_path` is an instance of `ArtifactUpload` with the `relative_path` set to "python/code/". diff --git a/docs/swarms/structs/autoscaler.md b/docs/swarms/structs/autoscaler.md index 703ae860..6d07d3b1 100644 --- a/docs/swarms/structs/autoscaler.md +++ b/docs/swarms/structs/autoscaler.md @@ -36,7 +36,9 @@ Initializes the `AutoScaler` with a predefined number of agents and sets up conf ```python from swarms import AutoScaler -scaler = AutoScaler(initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8) +scaler = AutoScaler( + initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8 +) ``` --- @@ -140,7 +142,9 @@ scaler.start() from swarms import AutoScaler # Initialize the scaler -auto_scaler = AutoScaler(initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7) +auto_scaler = AutoScaler( + initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7 +) # Start the monitoring and task processing auto_scaler.start() @@ -161,7 +165,6 @@ auto_scaler.start() for i in range(100): # Adding tasks auto_scaler.add_task(f"Task {i}") - ``` diff --git a/docs/swarms/structs/baseworkflow.md b/docs/swarms/structs/baseworkflow.md index ee048af8..2cb4b5eb 100644 --- a/docs/swarms/structs/baseworkflow.md +++ b/docs/swarms/structs/baseworkflow.md @@ -13,19 +13,19 @@ Base class for workflows. Source Code: ```python - class BaseWorkflow(BaseStructure): - """ - Base class for workflows. +class BaseWorkflow(BaseStructure): +""" +Base class for workflows. - Attributes: - task_pool (list): A list to store tasks. + Attributes: + task_pool (list): A list to store tasks. - Methods: - add(task: Task = None, tasks: List[Task] = None, *args, **kwargs): - Adds a task or a list of tasks to the task pool. - run(): - Abstract method to run the workflow. - """ + Methods: + add(task: Task = None, tasks: List[Task] = None, *args, **kwargs): + Adds a task or a list of tasks to the task pool. + run(): + Abstract method to run the workflow. +""" ``` For the usage examples and additional in-depth documentation please visit [BaseWorkflow](https://github.com/swarms-modules/structs/blob/main/baseworkflow.md#swarms-structs) diff --git a/docs/swarms/structs/groupchatmanager.md b/docs/swarms/structs/groupchatmanager.md index 623a1fbf..cb2ce035 100644 --- a/docs/swarms/structs/groupchatmanager.md +++ b/docs/swarms/structs/groupchatmanager.md @@ -24,7 +24,7 @@ agents = Agent() manager = GroupChatManager(groupchat, selector) # Call the group chat manager passing a specific chat task -result = manager('Discuss the agenda for the upcoming meeting') +result = manager("Discuss the agenda for the upcoming meeting") ``` Explanation: @@ -67,9 +67,7 @@ class GroupChatManager: Returns: str: The response from the group chat. """ - 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 diff --git a/docs/swarms/structs/json.md b/docs/swarms/structs/json.md index 2a5040e5..a495a0ce 100644 --- a/docs/swarms/structs/json.md +++ b/docs/swarms/structs/json.md @@ -78,16 +78,17 @@ Suppose we have a JSON Schema in `config_schema.json` for application configurat Now we'll create a subclass `AppConfig` that uses this schema. ```python -import json from swarms.structs import JSON + class AppConfig(JSON): def __init__(self, schema_path): super().__init__(schema_path) def validate(self, config_data): # Here we'll use a JSON Schema validation library like jsonschema - from jsonschema import validate, ValidationError + from jsonschema import ValidationError, validate + try: validate(instance=config_data, schema=self.schema) except ValidationError as e: @@ -95,15 +96,13 @@ class AppConfig(JSON): return False return True + # Main Example Usage if __name__ == "__main__": - config = { - "debug": True, - "window_size": [800, 600] - } + config = {"debug": True, "window_size": [800, 600]} - app_config = AppConfig('config_schema.json') + app_config = AppConfig("config_schema.json") if app_config.validate(config): print("Config is valid!") diff --git a/docs/swarms/structs/majorityvoting.md b/docs/swarms/structs/majorityvoting.md index 97d28847..4c7f7612 100644 --- a/docs/swarms/structs/majorityvoting.md +++ b/docs/swarms/structs/majorityvoting.md @@ -82,9 +82,11 @@ Executes the given task by all participating agents and aggregates the results t from swarms.structs.agent import Agent from swarms.structs.majority_voting import MajorityVoting + def create_agent(name): return Agent(name) + agents = [create_agent(name) for name in ["GPT-3", "Codex", "Tabnine"]] majority_voting = MajorityVoting(agents) result = majority_voting.run("What is the capital of France?") diff --git a/docs/swarms/structs/nonlinearworkflow.md b/docs/swarms/structs/nonlinearworkflow.md index 1974de24..09d00074 100644 --- a/docs/swarms/structs/nonlinearworkflow.md +++ b/docs/swarms/structs/nonlinearworkflow.md @@ -60,7 +60,7 @@ task3 = Task(llm, "Find a hotel in Paris") # Initialize the NonlinearWorkflow workflow = NonlinearWorkflow() # Add tasks to the workflow with dependencies -workflow.add(task1, task2.name) +workflow.add(task1, task2.name) workflow.add(task2, task3.name) workflow.add(task3, "OpenAIChat Initialization") # Execute the workflow @@ -82,7 +82,7 @@ task3 = Task(llm, "Find a hotel in Paris") # Initialize the NonlinearWorkflow workflow = NonlinearWorkflow() # Add tasks to the workflow with dependencies -workflow.add(task1) +workflow.add(task1) workflow.add(task2, task1.name) workflow.add(task3, task1.name, task2.name) # Execute the workflow diff --git a/docs/swarms/structs/sequential_workflow.md b/docs/swarms/structs/sequential_workflow.md index 6f6d8954..74ee1acb 100644 --- a/docs/swarms/structs/sequential_workflow.md +++ b/docs/swarms/structs/sequential_workflow.md @@ -310,9 +310,7 @@ from swarms.structs import Agent 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 agent llm = OpenAIChat( @@ -350,9 +348,7 @@ from swarms.structs import Agent 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 agent llm = OpenAIChat( @@ -393,9 +389,7 @@ from swarms.structs import Agent 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 agent llm = OpenAIChat( @@ -436,9 +430,7 @@ from swarms.structs import Agent 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 agent llm = OpenAIChat( diff --git a/docs/swarms/structs/stackoverflowswarm.md b/docs/swarms/structs/stackoverflowswarm.md index 5f5630df..c3e83b47 100644 --- a/docs/swarms/structs/stackoverflowswarm.md +++ b/docs/swarms/structs/stackoverflowswarm.md @@ -58,11 +58,13 @@ The design of the `StackOverflowSwarm` class is intended to allow easy tracking from swarms.structs.agent import Agent from swarms.structs.stack_overflow_swarm import StackOverflowSwarm + # Define custom Agents with some logic (placeholder for actual Agent implementation) class CustomAgent(Agent): def run(self, conversation, *args, **kwargs): return "This is a response from CustomAgent." + # Initialize agents agent1 = CustomAgent(ai_name="Agent1") agent2 = CustomAgent(ai_name="Agent2") diff --git a/docs/swarms/structs/stepinput.md b/docs/swarms/structs/stepinput.md index ad4be016..2230ccdf 100644 --- a/docs/swarms/structs/stepinput.md +++ b/docs/swarms/structs/stepinput.md @@ -12,10 +12,7 @@ The `StepInput` class is defined as follows: class StepInput(BaseModel): __root__: Any = Field( ..., - description=( - "Input parameters for the task step. Any value is" - " allowed." - ), + description=("Input parameters for the task step. Any value is" " allowed."), example='{\n"file_to_refactor": "models.py"\n}', ) ``` @@ -29,10 +26,7 @@ The `StepInput` class is designed to accept any input value, providing flexibili ```python from swarms.structs import StepInput -input_params = { - "file_to_refactor": "models.py", - "refactor_method": "code" -} +input_params = {"file_to_refactor": "models.py", "refactor_method": "code"} step_input = StepInput(__root__=input_params) ``` @@ -42,10 +36,7 @@ In this example, we import the `StepInput` class from the `swarms.structs` libra ```python from swarms.structs import StepInput -input_params = { - "input_path": "data.csv", - "output_path": "result.csv" -} +input_params = {"input_path": "data.csv", "output_path": "result.csv"} step_input = StepInput(__root__=input_params) ``` @@ -56,7 +47,7 @@ In this example, we again create an instance of `StepInput` by passing a diction from swarms.structs import StepInput file_path = "config.json" -with open(file_path, 'r') as f: +with open(file_path) as f: input_data = json.load(f) step_input = StepInput(__root__=input_data) diff --git a/docs/swarms/structs/swarmnetwork.md b/docs/swarms/structs/swarmnetwork.md index 2c2f74e5..efbdf279 100644 --- a/docs/swarms/structs/swarmnetwork.md +++ b/docs/swarms/structs/swarmnetwork.md @@ -28,6 +28,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.add_task("task") @@ -41,6 +42,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) await swarm.async_add_task("task") @@ -57,6 +59,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.run_single_agent(agent_id, "task") @@ -72,6 +75,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.run_many_agents("task") @@ -85,6 +89,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.list_agents() @@ -98,6 +103,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork() swarm.add_agent(agent) @@ -111,6 +117,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.remove_agent(agent_id) @@ -124,6 +131,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + swarm = SwarmNetwork() swarm.scale_up(num_agents=5) ``` @@ -136,6 +144,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + swarm = SwarmNetwork(agents=[agent1, agent2, agent3, agent4, agent5]) swarm.scale_down(num_agents=2) ``` @@ -146,6 +155,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.create_apis_for_agents() diff --git a/docs/swarms/structs/task.md b/docs/swarms/structs/task.md index 7e829b66..f6a72aef 100644 --- a/docs/swarms/structs/task.md +++ b/docs/swarms/structs/task.md @@ -8,8 +8,9 @@ ```python # Example 1: Creating and executing a Task -from swarms.structs import Task, Agent from swarms.models import OpenAIChat +from swarms.structs import Agent, Task + agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False) task = Task(agent=agent) task.execute("What's the weather in miami") diff --git a/docs/swarms/structs/taskinput.md b/docs/swarms/structs/taskinput.md index e7a0be68..8e9ed33f 100644 --- a/docs/swarms/structs/taskinput.md +++ b/docs/swarms/structs/taskinput.md @@ -20,11 +20,14 @@ The `TaskInput` class encapsulates the input parameters in a structured format. #### Usage Example 1: Using TaskInput for Debugging ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class DebugInput(TaskInput): debug: bool + # Creating an instance of DebugInput debug_params = DebugInput(__root__={"debug": True}) @@ -35,11 +38,14 @@ print(debug_params.debug) # Output: True #### Usage Example 2: Using TaskInput for Task Modes ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class ModeInput(TaskInput): mode: str + # Creating an instance of ModeInput mode_params = ModeInput(__root__={"mode": "benchmarks"}) @@ -50,12 +56,15 @@ print(mode_params.mode) # Output: benchmarks #### Usage Example 3: Using TaskInput with Arbitrary Parameters ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class ArbitraryInput(TaskInput): message: str quantity: int + # Creating an instance of ArbitraryInput arbitrary_params = ArbitraryInput(__root__={"message": "Hello, world!", "quantity": 5}) diff --git a/docs/swarms/structs/taskqueuebase.md b/docs/swarms/structs/taskqueuebase.md index a8d4c599..0feee648 100644 --- a/docs/swarms/structs/taskqueuebase.md +++ b/docs/swarms/structs/taskqueuebase.md @@ -7,13 +7,14 @@ The `swarms.structs` library is a key component of a multi-agent system's task m ## TaskQueueBase Class ```python -from abc import ABC, abstractmethod import threading +from abc import ABC, abstractmethod # Include any additional imports that are relevant to decorators and other classes such as Task and Agent if needed # Definition of the synchronized_queue decorator (if necessary) + class TaskQueueBase(ABC): def __init__(self): self.lock = threading.Lock() @@ -27,12 +28,12 @@ class TaskQueueBase(ABC): @abstractmethod def get_task(self, agent: Agent) -> Task: pass - + @synchronized_queue @abstractmethod def complete_task(self, task_id: str): pass - + @synchronized_queue @abstractmethod def reset_task(self, task_id: str): @@ -65,11 +66,12 @@ Below are three examples of how the `TaskQueueBase` class can be implemented and ```python # file: basic_queue.py -import threading -from swarms.structs import TaskQueueBase, Task, Agent # Assume synchronized_queue decorator is defined elsewhere -from decorators import synchronized_queue +from decorators import synchronized_queue + +from swarms.structs import Agent, Task, TaskQueueBase + class BasicTaskQueue(TaskQueueBase): def __init__(self): @@ -95,6 +97,7 @@ class BasicTaskQueue(TaskQueueBase): # Logic to reset the task pass + # Usage queue = BasicTaskQueue() # Add task, assuming Task object is created diff --git a/docs/swarms/tokenizers/basetokenizer.md b/docs/swarms/tokenizers/basetokenizer.md index 16db07fc..8047f301 100644 --- a/docs/swarms/tokenizers/basetokenizer.md +++ b/docs/swarms/tokenizers/basetokenizer.md @@ -25,6 +25,7 @@ The `BaseTokenizer` class provides the structure for creating tokenizers. It inc ```python from swarms.tokenizers import BaseTokenizer + class SimpleTokenizer(BaseTokenizer): def count_tokens(self, text: Union[str, List[dict]]) -> int: @@ -33,10 +34,11 @@ class SimpleTokenizer(BaseTokenizer): return len(text.split()) elif isinstance(text, list): # Assume list of dictionaries with 'token' key - return sum(len(item['token'].split()) for item in text) + return sum(len(item["token"].split()) for item in text) else: raise TypeError("Unsupported type for text") + # Usage example tokenizer = SimpleTokenizer(max_tokens=100) text = "This is an example sentence to tokenize." diff --git a/docs/swarms/tokenizers/coheretokenizer.md b/docs/swarms/tokenizers/coheretokenizer.md index 5a11b8ce..197346a3 100644 --- a/docs/swarms/tokenizers/coheretokenizer.md +++ b/docs/swarms/tokenizers/coheretokenizer.md @@ -57,7 +57,7 @@ def count_tokens(self, text: str | list) -> int: Args: text (str | list): The input text to tokenize. - + Returns: int: The number of tokens in the text. @@ -82,13 +82,14 @@ First, the Cohere client must be initialized and passed in to create an instance ```python from cohere import Client + from swarms.tokenizers import CohereTokenizer # Initialize Cohere client with your API key -cohere_client = Client('your-api-key') +cohere_client = Client("your-api-key") # Instantiate the tokenizer -tokenizer = CohereTokenizer(model='your-model-name', client=cohere_client) +tokenizer = CohereTokenizer(model="your-model-name", client=cohere_client) ``` ### Count Tokens Example 1 diff --git a/docs/swarms/tokenizers/huggingfacetokenizer.md b/docs/swarms/tokenizers/huggingfacetokenizer.md index 8cf7fd77..9330b3be 100644 --- a/docs/swarms/tokenizers/huggingfacetokenizer.md +++ b/docs/swarms/tokenizers/huggingfacetokenizer.md @@ -76,7 +76,7 @@ Tokenizes given text when the object is called like a function. from swarms.tokenizers import HuggingFaceTokenizer # Initialize the tokenizer with the path to your tokenizer model. -tokenizer = HuggingFaceTokenizer('/path/to/your/model_dir') +tokenizer = HuggingFaceTokenizer("/path/to/your/model_dir") ``` ### 2. Encoding Text diff --git a/docs/swarms/tokenizers/openaitokenizer.md b/docs/swarms/tokenizers/openaitokenizer.md index 9e051c32..94918261 100644 --- a/docs/swarms/tokenizers/openaitokenizer.md +++ b/docs/swarms/tokenizers/openaitokenizer.md @@ -50,7 +50,7 @@ Given the extensive nature of this class, several examples are provided for each ```python from swarms.tokenizers import OpenAITokenizer -tokenizer = OpenAITokenizer(model='gpt-4') +tokenizer = OpenAITokenizer(model="gpt-4") ``` This example creates a new instance of `OpenAITokenizer` set to work with the GPT-4 model. @@ -61,7 +61,7 @@ This example creates a new instance of `OpenAITokenizer` set to work with the GP text = "Hello, this is an example text to tokenize." # Initialize the tokenizer -tokenizer = OpenAITokenizer(model='gpt-4') +tokenizer = OpenAITokenizer(model="gpt-4") # Count tokens num_tokens = tokenizer.count_tokens(text) @@ -78,7 +78,7 @@ messages = [ {"name": "Bob", "message": "I'm good! Just working on some code."}, ] -tokenizer = OpenAITokenizer(model='gpt-3.5-turbo') +tokenizer = OpenAITokenizer(model="gpt-3.5-turbo") # Count tokens for a list of messages num_tokens = tokenizer.len(messages, model="gpt-3.5-turbo-0613") diff --git a/docs/swarms/tokenizers/sentencepiecetokenizer.md b/docs/swarms/tokenizers/sentencepiecetokenizer.md index 390dbb07..ac6f2a2f 100644 --- a/docs/swarms/tokenizers/sentencepiecetokenizer.md +++ b/docs/swarms/tokenizers/sentencepiecetokenizer.md @@ -14,7 +14,7 @@ In `SentencePieceTokenizer`, the tokenization process is language-agnostic and e class SentencePieceTokenizer: """ Tokenizer of sentencepiece. - + Args: model_file (str): the path of the tokenizer model """ @@ -45,7 +45,7 @@ Parameter | Type | Description ```python from swarms.tokenizers import SentencePieceTokenizer -tokenizer = SentencePieceTokenizer(model_file='your_model.model') +tokenizer = SentencePieceTokenizer(model_file="your_model.model") ``` ### Properties: Vocabulary Information diff --git a/docs/swarms/utils/check_device.md b/docs/swarms/utils/check_device.md index bdb8c780..a944dc1f 100644 --- a/docs/swarms/utils/check_device.md +++ b/docs/swarms/utils/check_device.md @@ -36,16 +36,18 @@ This function does not take any mandatory argument. However, it supports optiona ### Example 1: Basic Usage ```python -import torch import logging + +import torch + from swarms.utils import check_device # Basic usage device = check_device( - log_level=logging.INFO, - memory_threshold=0.8, - capability_threshold=3.5, - return_type="list" + log_level=logging.INFO, + memory_threshold=0.8, + capability_threshold=3.5, + return_type="list", ) ``` @@ -53,24 +55,24 @@ device = check_device( ```python import torch -import logging + from swarms.utils import check_device # When CUDA is not available device = check_device() -print(device) # If CUDA is not available it should return torch.device('cpu') +print(device) # If CUDA is not available it should return torch.device('cpu') ``` ### Example 3: Multiple GPU Available ```python import torch -import logging + from swarms.utils import check_device # When multiple GPUs are available device = check_device() -print(device) # Should return a list of available GPU devices +print(device) # Should return a list of available GPU devices ``` ## Tips and Additional Information diff --git a/docs/swarms/utils/extract_code_from_markdown.md b/docs/swarms/utils/extract_code_from_markdown.md index f6f76835..fdef5018 100644 --- a/docs/swarms/utils/extract_code_from_markdown.md +++ b/docs/swarms/utils/extract_code_from_markdown.md @@ -57,14 +57,13 @@ Below are three examples of how you might use this function: Extracting code blocks from a simple markdown string. ```python -import re from swarms.utils import extract_code_from_markdown -markdown_string = '''# Example +markdown_string = """# Example This is an example of a code block: ```python print("Hello World!") -``` ''' +``` """ print(extract_code_from_markdown(markdown_string)) ``` @@ -75,13 +74,15 @@ Extracting code blocks from a markdown file. ```python import re + def extract_code_from_markdown(markdown_content: str) -> str: pattern = r"```(?:\w+\n)?(.*?)```" matches = re.findall(pattern, markdown_content, re.DOTALL) return "\n".join(code.strip() for code in matches) + # Assume that 'example.md' contains multiple code blocks -with open('example.md', 'r') as file: +with open("example.md") as file: markdown_content = file.read() print(extract_code_from_markdown(markdown_content)) ``` @@ -93,17 +94,20 @@ Using the function in a pipeline to extract and then analyze code blocks. ```python import re + def extract_code_from_markdown(markdown_content: str) -> str: pattern = r"```(?:\w+\n)?(.*?)```" matches = re.findall(pattern, markdown_content, re.DOTALL) return "\n".join(code.strip() for code in matches) + def analyze_code_blocks(code: str): # Add your analysis logic here - pass + pass + # Assume that 'example.md' contains multiple code blocks -with open('example.md', 'r') as file: +with open("example.md") as file: markdown_content = file.read() code_blocks = extract_code_from_markdown(markdown_content) analyze_code_blocks(code_blocks) diff --git a/docs/swarms/utils/find_image_path.md b/docs/swarms/utils/find_image_path.md index 59c9c127..844cbe78 100644 --- a/docs/swarms/utils/find_image_path.md +++ b/docs/swarms/utils/find_image_path.md @@ -40,15 +40,9 @@ The function `find_image_path` performs text parsing and pattern recognition to ```python def find_image_path(text): pattern = r"([A-Za-z]:\\[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))|(/[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))" - matches = [ - match.group() - for match in re.finditer(pattern, text) - if match.group() - ] + matches = [match.group() for match in re.finditer(pattern, text) if match.group()] matches += [match.replace("\\", "") for match in matches if match] - existing_paths = [ - match for match in matches if os.path.exists(match) - ] + existing_paths = [match for match in matches if os.path.exists(match)] return max(existing_paths, key=len) if existing_paths else None ``` @@ -75,7 +69,9 @@ Consider the case where the text has multiple image paths. from swarms.utils import find_image_path text = "Here is an image path: /home/user/image1.png. Here is another one: C:\\Users\\User\\Documents\\image2.jpeg" -print(find_image_path(text)) # Outputs: the longest image path (depends on your file system and existing files) +print( + find_image_path(text) +) # Outputs: the longest image path (depends on your file system and existing files) ``` **Example 3:** diff --git a/docs/swarms/utils/limit_tokens_from_string.md b/docs/swarms/utils/limit_tokens_from_string.md index b096ebad..bc2cf8cf 100644 --- a/docs/swarms/utils/limit_tokens_from_string.md +++ b/docs/swarms/utils/limit_tokens_from_string.md @@ -73,7 +73,7 @@ from swarms.utils import limit_tokens_from_string string = "In case the method does not find the specified model, it will fall back to gpt2 model." # model -model = "gpt-4" +model = "gpt-4" output = limit_tokens_from_string(string, model=model) ``` diff --git a/docs/swarms/utils/load_model_torch.md b/docs/swarms/utils/load_model_torch.md index ddcd7ee6..7effabb6 100644 --- a/docs/swarms/utils/load_model_torch.md +++ b/docs/swarms/utils/load_model_torch.md @@ -50,12 +50,14 @@ This function can be used directly inside your code as shown in the following ex Loading a model without specifying a device results in the function choosing the most optimal available device automatically. ```python -from swarms.utils import load_model_torch import torch.nn as nn +from swarms.utils import load_model_torch + # Assume `mymodel.pth` is in the current directory model_path = "./mymodel.pth" + # Define your model architecture if the model file only contains state dict class MyModel(nn.Module): def __init__(self): @@ -65,6 +67,7 @@ class MyModel(nn.Module): def forward(self, x): return self.linear(x) + model = MyModel() # Load the model diff --git a/docs/swarms/utils/math_eval.md b/docs/swarms/utils/math_eval.md index 691089f8..19eb9517 100644 --- a/docs/swarms/utils/math_eval.md +++ b/docs/swarms/utils/math_eval.md @@ -15,6 +15,8 @@ Let's say you have two functions: `ground_truth` and `generated_func`, that have @math_eval(ground_truth, generated_func) def test_func(x): return x + + result1, result2 = test_func(5) print(f"Result from ground_truth: {result1}") print(f"Result from generated_func: {result2}") @@ -46,6 +48,7 @@ Here's how to implement the `math_eval` decorator: import functools import logging + def math_eval(func1, func2): """Math evaluation decorator.""" @@ -65,9 +68,7 @@ def math_eval(func1, func2): result2 = None if result1 != result2: - logging.warning( - f"Outputs do not match: {result1} != {result2}" - ) + logging.warning(f"Outputs do not match: {result1} != {result2}") return result1, result2 diff --git a/docs/swarms/utils/metrics_decorator.md b/docs/swarms/utils/metrics_decorator.md index aeafe151..17850ba1 100644 --- a/docs/swarms/utils/metrics_decorator.md +++ b/docs/swarms/utils/metrics_decorator.md @@ -70,6 +70,7 @@ def text_generator(self, text: str): # language generation implementation goes here return tokens + # Instantiate the class and call the decorated function obj = ClassName() obj.text_generator("Hello, world!") diff --git a/docs/swarms/utils/pdf_to_text.md b/docs/swarms/utils/pdf_to_text.md index e5b8dce0..3ec73039 100644 --- a/docs/swarms/utils/pdf_to_text.md +++ b/docs/swarms/utils/pdf_to_text.md @@ -54,7 +54,7 @@ Here is an example of how to use `pdf_to_text`: ```python # Define the path to the pdf file -pdf_path = 'sample.pdf' +pdf_path = "sample.pdf" # Use the function to extract text text = pdf_to_text(pdf_path) diff --git a/docs/swarms/utils/prep_torch_inference.md b/docs/swarms/utils/prep_torch_inference.md index 68598fa8..0fde2503 100644 --- a/docs/swarms/utils/prep_torch_inference.md +++ b/docs/swarms/utils/prep_torch_inference.md @@ -56,7 +56,8 @@ Here are some examples of how you can use the `prep_torch_inference` method. Bef ```python import torch -from swarms.utils import prep_torch_inference, load_model_torch + +from swarms.utils import load_model_torch, prep_torch_inference ``` ### Example 1: Load a model for inference on CPU diff --git a/docs/swarms/utils/print_class_parameters.md b/docs/swarms/utils/print_class_parameters.md index 3c09578f..84e0104f 100644 --- a/docs/swarms/utils/print_class_parameters.md +++ b/docs/swarms/utils/print_class_parameters.md @@ -94,16 +94,16 @@ def print_class_parameters(cls, api_format: bool = False): if api_format: param_dict = {} for name, param in params.items(): - if name == "self": - continue - param_dict[name] = str(param.annotation) + if name == "self": + continue + param_dict[name] = str(param.annotation) return param_dict # Print the parameters for name, param in params.items(): - if name == "self": - continue - print(f"Parameter: {name}, Type: {param.annotation}") + if name == "self": + continue + print(f"Parameter: {name}, Type: {param.annotation}") except Exception as e: print(f"An error occurred while inspecting the class: {e}") diff --git a/docs/swarms/workers/base.md b/docs/swarms/workers/base.md index 8991210b..9da45ba3 100644 --- a/docs/swarms/workers/base.md +++ b/docs/swarms/workers/base.md @@ -37,43 +37,46 @@ class AbstractWorker: Args: name (str): Name of the worker. """ - + @property def name(self): """Get the name of the worker.""" - pass def run(self, task: str): """Run the worker agent once.""" - pass - def send(self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None): + def send( + self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None + ): """Send a message to another worker.""" - pass - async def a_send(self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None): + async def a_send( + self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None + ): """Send a message to another worker asynchronously.""" - pass - def receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + def receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): """Receive a message from another worker.""" - pass - async def a_receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + async def a_receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): """Receive a message from another worker asynchronously.""" - pass def reset(self): """Reset the worker.""" - pass - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: """Generate a reply based on received messages.""" - pass - async def a_generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + async def a_generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: """Generate a reply based on received messages asynchronously.""" - pass ``` ### 2.2 Attributes @@ -121,6 +124,7 @@ class MyWorker(AbstractWorker): def run(self, task: str): print(f"{self.name} is performing task: {task}") + worker = MyWorker("Worker1") worker.run("Collect data") ``` @@ -155,6 +159,7 @@ The `a_send()` method is an asynchronous version of the `send()` method, allowin ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -162,6 +167,7 @@ async def main(): message = "Hello, Worker2!" await worker1.a_send(message, worker2) + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -208,6 +214,7 @@ The `a_receive()` method is an asynchronous version of the `receive()` method, a ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -218,6 +225,7 @@ async def main(): await worker1.a_receive(message1, worker2) await worker1.a_receive(message2, worker2) + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -233,6 +241,7 @@ class MyWorker(AbstractWorker): def reset(self): print(f"{self.name} has been reset.") + worker = MyWorker("Worker1") worker.reset() ``` @@ -253,13 +262,16 @@ The `generate_reply()` method is a placeholder for generating a reply based on r ```python class MyWorker(AbstractWorker): - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: if messages: # Generate a reply based on received messages return f"Received {len(messages)} messages from {sender.name}." else: return None + worker1 = MyWorker("Worker1") worker2 = MyWorker("Worker2") @@ -284,6 +296,7 @@ The `a_generate_reply()` method is an asynchronous version of the `generate_repl ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -294,6 +307,7 @@ async def main(): if reply: print(f"{worker2.name} generated a reply: {reply}") + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -312,12 +326,16 @@ Start by creating a custom worker class that inherits from `AbstractWorker`. Def class CustomWorker(AbstractWorker): def run(self, task: str): print(f"{self.name} is performing task: {task}") - - def receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + + def receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): if isinstance(message, str): print(f"{self.name} received a text message from {sender.name}: {message}") elif isinstance(message, dict): - print(f"{self.name} received a dictionary message from {sender.name}: {message}") + print( + f"{self.name} received a dictionary message from {sender.name}: {message}" + ) ``` ### Step 2: Create Custom Worker Instances @@ -355,7 +373,9 @@ Customize the `generate_reply()` method to allow your workers to generate replie ```python class CustomWorker(AbstractWorker): - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: if messages: # Generate a reply based on received messages return f"Received {len(messages)} messages from {sender.name}." diff --git a/docs/swarms/workers/index.md b/docs/swarms/workers/index.md index 9cf75e8b..3662fd8a 100644 --- a/docs/swarms/workers/index.md +++ b/docs/swarms/workers/index.md @@ -49,11 +49,11 @@ Makes the Worker class callable. When an instance of the class is called, it wil ### **Example 1**: Basic usage with default parameters: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -195,17 +195,16 @@ response = node.run(task) # Print the response print(response) - ``` ### **Example 3**: Usage with human in the loop: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -223,7 +222,6 @@ node = Worker( task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times." response = node.run(task) print(response) - ``` ## **Mathematical Description**: diff --git a/playground/agents/multi_modal_auto_agent_example.py b/playground/agents/multi_modal_auto_agent_example.py index d32a6221..4204f48c 100644 --- a/playground/agents/multi_modal_auto_agent_example.py +++ b/playground/agents/multi_modal_auto_agent_example.py @@ -1,6 +1,8 @@ # Description: This is an example of how to use the Agent class to run a multi-modal workflow import os + from dotenv import load_dotenv + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.structs import Agent diff --git a/playground/agents/multion_agent.py b/playground/agents/multion_agent.py index 230f098b..a8f5175d 100644 --- a/playground/agents/multion_agent.py +++ b/playground/agents/multion_agent.py @@ -1,7 +1,8 @@ import multion -from swarms.structs.concurrent_workflow import ConcurrentWorkflow + from swarms.models.base_llm import AbstractLLM from swarms.structs.agent import Agent +from swarms.structs.concurrent_workflow import ConcurrentWorkflow from swarms.structs.task import Task diff --git a/playground/agents/perimeter_defense_agent.py b/playground/agents/perimeter_defense_agent.py index 3f5480c6..d235fa22 100644 --- a/playground/agents/perimeter_defense_agent.py +++ b/playground/agents/perimeter_defense_agent.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv + +import swarms.prompts.security_team as stsp from swarms.models import GPT4VisionAPI from swarms.structs import Agent -import swarms.prompts.security_team as stsp # Load environment variables and initialize the Vision API load_dotenv() diff --git a/playground/agents/simple_agent_example.py b/playground/agents/simple_agent_example.py index 5d9d57ed..b79b8f59 100644 --- a/playground/agents/simple_agent_example.py +++ b/playground/agents/simple_agent_example.py @@ -3,12 +3,11 @@ import os from dotenv import load_dotenv from swarms import ( - OpenAIChat, Conversation, + OpenAIChat, detect_markdown, extract_code_from_markdown, ) - from swarms.tools.code_executor import CodeExecutor conv = Conversation( diff --git a/playground/agents/tool_agent.py b/playground/agents/tool_agent.py index 0a95f42c..a6445b39 100644 --- a/playground/agents/tool_agent.py +++ b/playground/agents/tool_agent.py @@ -1,5 +1,6 @@ # Import necessary libraries from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Load the pre-trained model and tokenizer diff --git a/playground/agents/worker_example.py b/playground/agents/worker_example.py index 9e215e83..a2117e46 100644 --- a/playground/agents/worker_example.py +++ b/playground/agents/worker_example.py @@ -1,8 +1,10 @@ # Importing necessary modules import os + from dotenv import load_dotenv -from swarms.agents.worker_agent import Worker + from swarms import OpenAIChat +from swarms.agents.worker_agent import Worker # Loading environment variables from .env file load_dotenv() diff --git a/playground/demos/accountant_team/account_team2_example.py b/playground/demos/accountant_team/account_team2_example.py index 1b9d3659..6ad030a9 100644 --- a/playground/demos/accountant_team/account_team2_example.py +++ b/playground/demos/accountant_team/account_team2_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models import Anthropic, OpenAIChat from swarms.prompts.accountant_swarm_prompts import ( DECISION_MAKING_PROMPT, diff --git a/playground/demos/ad_gen/ad_gen_example.py b/playground/demos/ad_gen/ad_gen_example.py index b665b63a..978ab502 100644 --- a/playground/demos/ad_gen/ad_gen_example.py +++ b/playground/demos/ad_gen/ad_gen_example.py @@ -1,9 +1,11 @@ -import random import os +import random + from dotenv import load_dotenv + from swarms.models import OpenAIChat -from swarms.structs import Agent from swarms.models.stable_diffusion import StableDiffusion +from swarms.structs import Agent load_dotenv() openai_api_key = os.getenv("OPENAI_API_KEY") diff --git a/playground/demos/assembly/assembly_example.py b/playground/demos/assembly/assembly_example.py index 704c80d4..7ac97ab0 100644 --- a/playground/demos/assembly/assembly_example.py +++ b/playground/demos/assembly/assembly_example.py @@ -1,5 +1,5 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/autotemp/autotemp_example.py b/playground/demos/autotemp/autotemp_example.py index baf8f091..f086f112 100644 --- a/playground/demos/autotemp/autotemp_example.py +++ b/playground/demos/autotemp/autotemp_example.py @@ -1,4 +1,5 @@ import re + from swarms.models.openai_models import OpenAIChat diff --git a/playground/demos/autotemp/blog_gen_example.py b/playground/demos/autotemp/blog_gen_example.py index e11a1521..fe2a2317 100644 --- a/playground/demos/autotemp/blog_gen_example.py +++ b/playground/demos/autotemp/blog_gen_example.py @@ -1,7 +1,9 @@ import os + +from autotemp import AutoTemp from termcolor import colored + from swarms.models import OpenAIChat -from autotemp import AutoTemp from swarms.structs import SequentialWorkflow diff --git a/playground/demos/developer_swarm/main_example.py b/playground/demos/developer_swarm/main_example.py index 18c0a346..0a2e2a95 100644 --- a/playground/demos/developer_swarm/main_example.py +++ b/playground/demos/developer_swarm/main_example.py @@ -6,7 +6,7 @@ This is a simple example of how to use the swarms library to create a swarm of d The swarm is composed of two agents: - Documentation agent: writes documentation for a given code snippet. - Tests agent: writes tests for a given code snippet. - + The swarm is initialized with a language model that is used by the agents to generate text. In this example, we use the OpenAI GPT-3 language model. Agent: @@ -14,6 +14,7 @@ Documentation agent -> Tests agent """ + import os from dotenv import load_dotenv diff --git a/playground/demos/education/education_example.py b/playground/demos/education/education_example.py index 266cede9..31c08f0d 100644 --- a/playground/demos/education/education_example.py +++ b/playground/demos/education/education_example.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv -from swarms.models import OpenAIChat -from swarms import Agent, SequentialWorkflow + import swarms.prompts.education as edu_prompts +from swarms import Agent, SequentialWorkflow +from swarms.models import OpenAIChat # Load environment variables load_dotenv() diff --git a/playground/demos/gemini_benchmarking/gemini_chat_example.py b/playground/demos/gemini_benchmarking/gemini_chat_example.py index 6d9dc7ae..2ea6a900 100644 --- a/playground/demos/gemini_benchmarking/gemini_chat_example.py +++ b/playground/demos/gemini_benchmarking/gemini_chat_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models.gemini import Gemini from swarms.prompts.react import react_prompt diff --git a/playground/demos/gemini_benchmarking/gemini_react_example.py b/playground/demos/gemini_benchmarking/gemini_react_example.py index 022405e9..37765baf 100644 --- a/playground/demos/gemini_benchmarking/gemini_react_example.py +++ b/playground/demos/gemini_benchmarking/gemini_react_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models.gemini import Gemini from swarms.prompts.react import react_prompt diff --git a/playground/demos/grupa/app_example.py b/playground/demos/grupa/app_example.py index 3ab52e22..ff5fc27d 100644 --- a/playground/demos/grupa/app_example.py +++ b/playground/demos/grupa/app_example.py @@ -1,12 +1,12 @@ import os from dotenv import load_dotenv +from termcolor import colored from swarms.models import OpenAIChat from swarms.prompts.code_interpreter import CODE_INTERPRETER +from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP from swarms.structs import Agent -from swarms.prompts.programming import TEST_SOP, DOCUMENTATION_SOP -from termcolor import colored load_dotenv() diff --git a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py b/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py index 05cd4fff..cce61fba 100644 --- a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py +++ b/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py @@ -1,9 +1,8 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.prompts.multi_modal_autonomous_instruction_prompt import ( MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1, ) - +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/langchain_example/langchain_example.py b/playground/demos/langchain_example/langchain_example.py index 803e7857..0e47684e 100644 --- a/playground/demos/langchain_example/langchain_example.py +++ b/playground/demos/langchain_example/langchain_example.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv -from swarms import Agent from langchain.llms import OpenAIChat +from swarms import Agent + # Loading environment variables from .env file load_dotenv() diff --git a/playground/demos/logistics/logistics_example.py b/playground/demos/logistics/logistics_example.py index 108ec702..48d8b9ce 100644 --- a/playground/demos/logistics/logistics_example.py +++ b/playground/demos/logistics/logistics_example.py @@ -1,16 +1,18 @@ -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.logistics import ( + Efficiency_Agent_Prompt, Health_Security_Agent_Prompt, - Quality_Control_Agent_Prompt, Productivity_Agent_Prompt, + Quality_Control_Agent_Prompt, Safety_Agent_Prompt, Security_Agent_Prompt, Sustainability_Agent_Prompt, - Efficiency_Agent_Prompt, ) +from swarms.structs import Agent # from swarms.utils.banana_wrapper import banana diff --git a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py b/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py index 74be8d5a..007776ac 100644 --- a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py +++ b/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py @@ -1,6 +1,5 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI - +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/multimodal_tot/main_example.py b/playground/demos/multimodal_tot/main_example.py index 2d5ed653..2a0494dc 100644 --- a/playground/demos/multimodal_tot/main_example.py +++ b/playground/demos/multimodal_tot/main_example.py @@ -2,8 +2,8 @@ Multi Modal tree of thoughts that leverages the GPT-4 language model and the Stable Diffusion model to generate a multimodal output and evaluate the output based a metric from 0.0 to 1.0 and then run a search algorithm using DFS and BFS and return the best output. - - + + task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates the img from 0.0 to 1.0 -> DFS/BFS -> return the best output @@ -16,10 +16,12 @@ task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates """ import os + from dotenv import load_dotenv +from termcolor import colored + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.models.stable_diffusion import StableDiffusion -from termcolor import colored # Load the environment variables load_dotenv() diff --git a/playground/demos/nutrition/nutrition_example.py b/playground/demos/nutrition/nutrition_example.py index 428560e3..b4331db6 100644 --- a/playground/demos/nutrition/nutrition_example.py +++ b/playground/demos/nutrition/nutrition_example.py @@ -1,7 +1,9 @@ -import os import base64 +import os + import requests from dotenv import load_dotenv + from swarms.models import OpenAIChat from swarms.structs import Agent diff --git a/playground/demos/personal_assistant/better_communication_example.py b/playground/demos/personal_assistant/better_communication_example.py index c6e79eb7..e0ff75cc 100644 --- a/playground/demos/personal_assistant/better_communication_example.py +++ b/playground/demos/personal_assistant/better_communication_example.py @@ -1,5 +1,5 @@ -import time import os +import time import pygame import speech_recognition as sr diff --git a/playground/demos/personal_stylist/personal_stylist_example.py b/playground/demos/personal_stylist/personal_stylist_example.py index b8641aa3..dde64cb7 100644 --- a/playground/demos/personal_stylist/personal_stylist_example.py +++ b/playground/demos/personal_stylist/personal_stylist_example.py @@ -1,14 +1,16 @@ -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.personal_stylist import ( - HAIRCUT_STYLIST_AGENT_PROMPT, - MAKEUP_STYLIST_AGENT_PROMPT, + ACCESSORIES_STYLIST_AGENT_PROMPT, BEARD_STYLIST_AGENT_PROMPT, CLOTHING_STYLIST_AGENT_PROMPT, - ACCESSORIES_STYLIST_AGENT_PROMPT, + HAIRCUT_STYLIST_AGENT_PROMPT, + MAKEUP_STYLIST_AGENT_PROMPT, ) +from swarms.structs import Agent # Load environment variables load_dotenv() diff --git a/playground/demos/positive_med/positive_med_example.py b/playground/demos/positive_med/positive_med_example.py index b92b9586..09cbb411 100644 --- a/playground/demos/positive_med/positive_med_example.py +++ b/playground/demos/positive_med/positive_med_example.py @@ -20,6 +20,7 @@ Distribution Agent: - Optimize writer prompt to create longer and more enjoyeable blogs - Use Local Models like Storywriter """ + import os from termcolor import colored diff --git a/playground/demos/security_team/IMG_1625.MOV b/playground/demos/security_team/IMG_1625.MOV new file mode 100644 index 0000000000000000000000000000000000000000..9bbb35741ee3cdd917216e5be7a6530511b24088 GIT binary patch literal 6932435 zcmV);K!(2n000zbba`-bbRZxA0001SbRZxA000PgX=G&pX_S6#WMOn6AOk>X93f8U zHwWVTghUONF_{Sl{>yp9*8Okq|Mou?)YK5?dDJ)%_#g1Jk4N|MD#{}|Av}#j2@yKg zh!R;5LnKk-$RrCM)PbmIvdfA{|Hqyypn({AEg9Yj4b7>`j2n|@J zc&|Q9;c>Fe5wf6-B|I_%Y@~FQIK@u!8ey=}Etihk7k3%4$84=k+$BsFFBZ}!MmUH& zf2qFA_>yc7#ri}InsC`eLIwS?HEtX4-~NC8|Kmw_Cz;_6w_RU>580RXAR+zD|BK-} zo01sbGDfYtzQxv32Bbx3$L`KrKPWk0{h?+80|L-E-UYmNm&AIRG7r%2jFU}&V&5Os zx}h!4RiKOxr(*FQ)C;m9(|DK=tO-Xt~??Er&G5n{9eS7;;P>XrN31-oLui!ei#RXqz`T`L(%bAYr@HjD8&}crbJqJN8sbvTO0=UaWepuSvbcL;9^Vv>bP04S7Cb>h=nkTo)P zP*>f~%xn2&E?_fUto6?{xx@A7lP~QiD?u<27J--&Eg?EcnOLP@QY+-!Z4#25iweS+ zIt@33sHH@`j<)yG7ZvN3ujsn~1-VV$n?+Szajj{lNh=BW%=gzHdeQq_y3D&9X@9*u zE#=WiLL@W1NvoJ;8B(4@HZn^CI@S}UsV?+=bwO=f&b<(X2$H1M5@CJNtZ<_7U!KNn zit^8ZAq)(Eo8SNc{B@xRWoRJ*kK^7gnWdi0&I`4p7Z3_R8Mv!vYO1x<((6c=ah`-> zj0i6O+tUT~r8sz%3r)4f`K+?^rz)&AYt7#VIcaYh=^@6R+oZBJXs2aTNk#C9%5akF zG+lqYv)ABu(>`@ne{j*+y?gVoJIf0FEmM%l5oMPfW7XTIb2?t!We3_p+s^v+*Ob3S zq-s;?VG#@|R*WKc3R@N^gX6MLLlVeMiX@rvF1U$8GzqbqkXHHCRPs8KI;({&zHO{`DiTy6pz7E-R8qC#2=`DiQ0tzw9fOOC4pxzDGI~=w?!3o z)I_=EeakI9VatxH7cSZ;NKKbb8TE3uA2ZAI65U)OWKycmr#;rZq^x(CwMlCP!4A@% zA|q2Z$a4`3P~%~_kH^FK>riM@8W!(@j{2CKLP`JvO5X@Tu+WSY6A2apcSqW9iRGCV zwbkcZ%o3VWxey6766^_(6_SZn8O#+q0_|Z~jl#OGoUCc>rIy*wiQrw4CkAY!BBVr| z;USnGOEkDlIZp1F@Qmj_ZLCMkWYAhLjAz?o{K* zGw_g0`6$Tw+rcPxy$%zXf$=I8ImZ)MHvdju)Nm>=8rO2d>@(wR2z;ygjJQ z`X1f);^{^-^-f`Ip6PM^0%B2;&Ydjni)P}6zz|a?BY+_c6m^;u$DuM*fTzy;Firt; zMBpV+U>Gul&O_A1@GLfrJu@#6&8F`d%+d~ba zaoL2Dl=?mA)OUFi<7r`^R4p|V*Npa*)cw9PT)X`wsVW^i3YTRjDHYk} zyuy{ef=h%R>xD>7=_8pC2=c!c0wqUOXd>~(s*zB$ilr4exC+&A%&=TdqSUn6N-l&| zwiLxdv7ktR3-jMH@&>kPaW#vI3)Wrl#Dc<2CDniUc|Am$KNisW3K2Wd`}*up;$;%- zO`m8Iw-1(Yc^Aa4(qe9h#=2>A=ubiFjCbyHs?gK(D9QS?M1-jb$A;6Pqz3PsQ5KgB zn{80(T(u1IT#-1s>h9=-WCqH9Ak$&;NYHDCr+{0>h9@yd4Y^$|xhGhd|!Ua8bdg zC|yQ_G}8_^NK)9B;f*$GxoMVoV~+jgNTMGNGIgU4r{lMM#Ft5(&A~D>+AnDO4xN_F za^i(gK%5P+6$wn-rl3xSsmT+pgB*5}ChKxi{};-PV<$unZl_{CkjUh^W^%Oir26*p zn5$jAfv=xN0RmH0aS|)$px%u_)^H&V6n&Ko31Yxl$Wag}`F*= z51sR_VJPh+n29X8^T3JDlVy($21MN>pj=bNvWp=ZD6GU`kcS?wgcVdt8Bge%RRJwE z_rZOu2@vqtTdg+LFAF$hl{?QtKg#Ta*MuWEA#%p>N$h~iFmY5~% zkA%ZDp9y?CwiXgu5uCTzhMO?itw+OcdBXT(ZX4RLR7;!>iNoS=AZL+qA77)+fX1Fg&3^rOS|FECU2?HWO$8-M?8G%;MG;}&eDh(Vs~T+_{MCF4L<$pz<#uG&+Z6|V zti_yd+^*FMM`9khGVx}cS4zcYC$OjpQjo;YBSK1e_U*BmSioa2wtal8t)$~G zPj9cX#peHozmVBv^vzKtPZrfDRrbEw{)t=*T2;l*_0FvOzuzpKE;%2w>z+lNr8@s! ziiC30(A`E>6D8dc$cGwz@hGg{Pf*Hh$2`PyHP^ch- z!3v$mQ|qbwcJ0)SM@1AOHYkgiJ_m&jU0dut2N_7yC_sU+0tY@EoVs$*4DnUdDW#oq z#kV9>)2IrJPXlFwqLo|>6~hfheT8yGr;R3nWCCwnPNZ08!ZeTt63*JA1Z-jYEP^m~>Vkj1>3BfJ24V z5@~v>F<=Z{OTT2&5MlXGo4(KQv61DK2@#3GQeMidRda*GhQF$kj>Ov`Uxl}R8H8lb zFqjA^&TRM*yw43ZaEt`h3==QHM{|;v7lN|Pq~(T-jQU(XMiqD}@YdN~hW8fb6K}62 zmaWLH8g1plqm%JKhazFJgwR8%0%@(RId zXM%Hy20vHuxlwFT>kI()SbTC6uU#h!??NjnW^{!6wWgljzGLv&^MDD zh@QCsRtg&MjY0bEUk8C=7`-G?vkn
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