From 56b5ab3efbc665e70f9274727f8094da005206a3 Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Mon, 21 Aug 2023 20:10:18 -0400
Subject: [PATCH] stable worker autobot node with prototype autoscaler need
task assignment and delegation
Former-commit-id: ae627eb82a3a175924f56061c2c5a824bd6ec9d2
---
chat_history.txt | 1 +
example.py | 36 ++-
playground/autoscaler.py | 9 +
pyproject.toml | 2 +-
requirements.txt | 5 +-
swarms/__init__.py | 12 +-
swarms/agents/__init__.py | 2 +-
swarms/agents/models/huggingface.py | 20 +-
swarms/agents/models/palm.py | 374 ++++++++++++++--------------
swarms/agents/tools/autogpt.py | 5 +-
swarms/orchestrator/autoscaler.py | 62 +++++
swarms/workers/autobot.py | 75 ++++++
swarms/workers/worker_node.py | 35 ++-
13 files changed, 400 insertions(+), 238 deletions(-)
create mode 100644 chat_history.txt
create mode 100644 playground/autoscaler.py
create mode 100644 swarms/orchestrator/autoscaler.py
create mode 100644 swarms/workers/autobot.py
diff --git a/chat_history.txt b/chat_history.txt
new file mode 100644
index 00000000..0637a088
--- /dev/null
+++ b/chat_history.txt
@@ -0,0 +1 @@
+[]
\ No newline at end of file
diff --git a/example.py b/example.py
index 124b5b18..69be9e3d 100644
--- a/example.py
+++ b/example.py
@@ -47,20 +47,34 @@
################
-from swarms import WorkerNode
+# from swarms import WorkerNode
-# Your OpenAI API key
-api_key = "example key"
+# # Your OpenAI API key
+# api_key = ""
+# # Initialize a WorkerNode with your API key
+# node = WorkerNode(api_key)
+# # node.create_worker_node()
-# Initialize a WorkerNode with your API key
-node = WorkerNode(api_key)
-node.create_worker_node()
+# # Define an objective
+# objective = "Please make a web GUI for using HTTP API server..."
+
+# # Run the task
+# task = node.run(objective)
+
+# print(task)
+
+
+
+##########
+from swarms import AutoBot
-# Define an objective
-objective = "Please make a web GUI for using HTTP API server..."
-# Run the task
-task = node.run(objective)
+auto_bot = AutoBot(
+ openai_api_key="",
+ ai_name="Optimus Prime",
+)
-print(task)
+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 = auto_bot.run(task)
+print(response)
\ No newline at end of file
diff --git a/playground/autoscaler.py b/playground/autoscaler.py
new file mode 100644
index 00000000..3c7cc525
--- /dev/null
+++ b/playground/autoscaler.py
@@ -0,0 +1,9 @@
+from swarms import AutoBot, AutoScaler
+
+auto_scaler = AutoScaler()
+auto_scaler.start()
+
+for i in range(100):
+ auto_scaler.add_task(f"Task {i}")
+
+
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index a3ccbeec..4d441009 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ google-generativeai = "*"
torch = "*"
langchain-experimental = "*"
playwright = "*"
-duckduckgo_search = "*"
+duckduckgo-search = "*"
faiss-cpu = "*"
wget = "*"
httpx = "*"
diff --git a/requirements.txt b/requirements.txt
index c433a1bb..87cb9ab8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -121,7 +121,6 @@ google-generativeai
oceandb
langchain-experimental
playwright
-duckduckgo_search
wget==3.2
simpleaichat
httpx
@@ -132,4 +131,6 @@ redis
google-search-results==2.4.2
Pillow
faiss-cpu
-openai
\ No newline at end of file
+openai
+google-generativeai
+duckduckgo-search
\ No newline at end of file
diff --git a/swarms/__init__.py b/swarms/__init__.py
index 2163acf5..3e9e626e 100644
--- a/swarms/__init__.py
+++ b/swarms/__init__.py
@@ -1,14 +1,18 @@
#swarms
+from swarms.orchestrator.autoscaler import AutoScaler
# worker
-# from swarms.workers.worker_node import WorkerNode
+from swarms.workers.worker_node import WorkerNode
+from swarms.workers.autobot import AutoBot
#boss
-# from swarms.boss.boss_node import BossNode
+from swarms.boss.boss_node import BossNode
#models
from swarms.agents.models.anthropic import Anthropic
from swarms.agents.models.huggingface import HuggingFaceLLM
-from swarms.agents.models.palm import GooglePalm
+# from swarms.agents.models.palm import GooglePalm
from swarms.agents.models.petals import Petals
-# from swarms.agents.models.openai import OpenAI
+from swarms.agents.models.openai import OpenAI
+
+
diff --git a/swarms/agents/__init__.py b/swarms/agents/__init__.py
index 3400e7f1..32d65d8e 100644
--- a/swarms/agents/__init__.py
+++ b/swarms/agents/__init__.py
@@ -4,7 +4,7 @@
#models
from swarms.agents.models.anthropic import Anthropic
from swarms.agents.models.huggingface import HuggingFaceLLM
-from swarms.agents.models.palm import GooglePalm
+# from swarms.agents.models.palm import GooglePalm
from swarms.agents.models.petals import Petals
from swarms.agents.models.openai import OpenAI
diff --git a/swarms/agents/models/huggingface.py b/swarms/agents/models/huggingface.py
index f43c76e5..1eb36d79 100644
--- a/swarms/agents/models/huggingface.py
+++ b/swarms/agents/models/huggingface.py
@@ -1,6 +1,6 @@
import torch
import logging
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer #,# BitsAndBytesConfig
class HuggingFaceLLM:
def __init__(self, model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None):
@@ -10,15 +10,15 @@ class HuggingFaceLLM:
self.max_length = max_length
bnb_config = None
- if quantize:
- if not quantization_config:
- quantization_config = {
- 'load_in_4bit': True,
- 'bnb_4bit_use_double_quant': True,
- 'bnb_4bit_quant_type': "nf4",
- 'bnb_4bit_compute_dtype': torch.bfloat16
- }
- bnb_config = BitsAndBytesConfig(**quantization_config)
+ # if quantize:
+ # if not quantization_config:
+ # quantization_config = {
+ # 'load_in_4bit': True,
+ # 'bnb_4bit_use_double_quant': True,
+ # 'bnb_4bit_quant_type': "nf4",
+ # 'bnb_4bit_compute_dtype': torch.bfloat16
+ # }
+ # bnb_config = BitsAndBytesConfig(**quantization_config)
try:
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
diff --git a/swarms/agents/models/palm.py b/swarms/agents/models/palm.py
index 3f4856b0..20eafd61 100644
--- a/swarms/agents/models/palm.py
+++ b/swarms/agents/models/palm.py
@@ -1,187 +1,189 @@
-from __future__ import annotations
-
-import logging
-from swarms.utils.logger import logger
-from typing import Any, Callable, Dict, List, Optional
-
-from pydantic import BaseModel, root_validator
-from tenacity import (
- before_sleep_log,
- retry,
- retry_if_exception_type,
- stop_after_attempt,
- wait_exponential,
-)
-
-import google.generativeai as genai
-
-
-class GooglePalmError(Exception):
- """Error raised when there is an issue with the Google PaLM API."""
-
-def _truncate_at_stop_tokens(
- text: str,
- stop: Optional[List[str]],
-) -> str:
- """Truncates text at the earliest stop token found."""
- if stop is None:
- return text
-
- for stop_token in stop:
- stop_token_idx = text.find(stop_token)
- if stop_token_idx != -1:
- text = text[:stop_token_idx]
- return text
-
-def _response_to_result(response: genai.types.ChatResponse, stop: Optional[List[str]]) -> Dict[str, Any]:
- """Convert a PaLM chat response to a result dictionary."""
- result = {
- "id": response.id,
- "created": response.created,
- "model": response.model,
- "usage": {
- "prompt_tokens": response.usage.prompt_tokens,
- "completion_tokens": response.usage.completion_tokens,
- "total_tokens": response.usage.total_tokens,
- },
- "choices": [],
- }
- for choice in response.choices:
- result["choices"].append({
- "text": _truncate_at_stop_tokens(choice.text, stop),
- "index": choice.index,
- "finish_reason": choice.finish_reason,
- })
- return result
-
-def _messages_to_prompt_dict(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
- """Convert a list of message dictionaries to a prompt dictionary."""
- prompt = {"messages": []}
- for message in messages:
- prompt["messages"].append({
- "role": message["role"],
- "content": message["content"],
- })
- return prompt
-
-def _create_retry_decorator() -> Callable[[Any], Any]:
- """Create a retry decorator with exponential backoff."""
- return retry(
- retry=retry_if_exception_type(GooglePalmError),
- stop=stop_after_attempt(5),
- wait=wait_exponential(multiplier=1, min=2, max=30),
- before_sleep=before_sleep_log(logger, logging.DEBUG),
- reraise=True,
- )
-
-####################### => main class
-class GooglePalm(BaseModel):
- """Wrapper around Google's PaLM Chat API."""
-
- client: Any #: :meta private:
- model_name: str = "models/chat-bison-001"
- google_api_key: Optional[str] = None
- temperature: Optional[float] = None
- top_p: Optional[float] = None
- top_k: Optional[int] = None
- n: int = 1
-
- @root_validator()
- def validate_environment(cls, values: Dict) -> Dict:
- # Same as before
- pass
-
- def chat_with_retry(self, **kwargs: Any) -> Any:
- """Use tenacity to retry the completion call."""
- retry_decorator = _create_retry_decorator()
-
- @retry_decorator
- def _chat_with_retry(**kwargs: Any) -> Any:
- return self.client.chat(**kwargs)
-
- return _chat_with_retry(**kwargs)
-
- async def achat_with_retry(self, **kwargs: Any) -> Any:
- """Use tenacity to retry the async completion call."""
- retry_decorator = _create_retry_decorator()
-
- @retry_decorator
- async def _achat_with_retry(**kwargs: Any) -> Any:
- return await self.client.chat_async(**kwargs)
-
- return await _achat_with_retry(**kwargs)
+# from __future__ import annotations
+
+# import logging
+# from swarms.utils.logger import logger
+# from typing import Any, Callable, Dict, List, Optional
+
+# from pydantic import BaseModel, model_validator
+# from tenacity import (
+# before_sleep_log,
+# retry,
+# retry_if_exception_type,
+# stop_after_attempt,
+# wait_exponential,
+# )
+
+# import google.generativeai as palm
+
+
+# class GooglePalmError(Exception):
+# """Error raised when there is an issue with the Google PaLM API."""
+
+# def _truncate_at_stop_tokens(
+# text: str,
+# stop: Optional[List[str]],
+# ) -> str:
+# """Truncates text at the earliest stop token found."""
+# if stop is None:
+# return text
+
+# for stop_token in stop:
+# stop_token_idx = text.find(stop_token)
+# if stop_token_idx != -1:
+# text = text[:stop_token_idx]
+# return text
+
+
+# def _response_to_result(response: palm.types.ChatResponse, stop: Optional[List[str]]) -> Dict[str, Any]:
+# """Convert a PaLM chat response to a result dictionary."""
+# result = {
+# "id": response.id,
+# "created": response.created,
+# "model": response.model,
+# "usage": {
+# "prompt_tokens": response.usage.prompt_tokens,
+# "completion_tokens": response.usage.completion_tokens,
+# "total_tokens": response.usage.total_tokens,
+# },
+# "choices": [],
+# }
+# for choice in response.choices:
+# result["choices"].append({
+# "text": _truncate_at_stop_tokens(choice.text, stop),
+# "index": choice.index,
+# "finish_reason": choice.finish_reason,
+# })
+# return result
+
+# def _messages_to_prompt_dict(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
+# """Convert a list of message dictionaries to a prompt dictionary."""
+# prompt = {"messages": []}
+# for message in messages:
+# prompt["messages"].append({
+# "role": message["role"],
+# "content": message["content"],
+# })
+# return prompt
+
+
+# def _create_retry_decorator() -> Callable[[Any], Any]:
+# """Create a retry decorator with exponential backoff."""
+# return retry(
+# retry=retry_if_exception_type(GooglePalmError),
+# stop=stop_after_attempt(5),
+# wait=wait_exponential(multiplier=1, min=2, max=30),
+# before_sleep=before_sleep_log(logger, logging.DEBUG),
+# reraise=True,
+# )
+
+
+# ####################### => main class
+# class GooglePalm(BaseModel):
+# """Wrapper around Google's PaLM Chat API."""
+
+# client: Any #: :meta private:
+# model_name: str = "models/chat-bison-001"
+# google_api_key: Optional[str] = None
+# temperature: Optional[float] = None
+# top_p: Optional[float] = None
+# top_k: Optional[int] = None
+# n: int = 1
+
+# @model_validator(mode="pre")
+# def validate_environment(cls, values: Dict) -> Dict:
+# # Same as before
+# pass
+
+# def chat_with_retry(self, **kwargs: Any) -> Any:
+# """Use tenacity to retry the completion call."""
+# retry_decorator = _create_retry_decorator()
+
+# @retry_decorator
+# def _chat_with_retry(**kwargs: Any) -> Any:
+# return self.client.chat(**kwargs)
+
+# return _chat_with_retry(**kwargs)
+
+# async def achat_with_retry(self, **kwargs: Any) -> Any:
+# """Use tenacity to retry the async completion call."""
+# retry_decorator = _create_retry_decorator()
+
+# @retry_decorator
+# async def _achat_with_retry(**kwargs: Any) -> Any:
+# return await self.client.chat_async(**kwargs)
+
+# return await _achat_with_retry(**kwargs)
- def __call__(
- self,
- messages: List[Dict[str, Any]],
- stop: Optional[List[str]] = None,
- **kwargs: Any,
- ) -> Dict[str, Any]:
- prompt = _messages_to_prompt_dict(messages)
-
- response: genai.types.ChatResponse = self.chat_with_retry(
- model=self.model_name,
- prompt=prompt,
- temperature=self.temperature,
- top_p=self.top_p,
- top_k=self.top_k,
- candidate_count=self.n,
- **kwargs,
- )
-
- return _response_to_result(response, stop)
-
- def generate(
- self,
- messages: List[Dict[str, Any]],
- stop: Optional[List[str]] = None,
- **kwargs: Any,
- ) -> Dict[str, Any]:
- prompt = _messages_to_prompt_dict(messages)
-
- response: genai.types.ChatResponse = self.chat_with_retry(
- model=self.model_name,
- prompt=prompt,
- temperature=self.temperature,
- top_p=self.top_p,
- top_k=self.top_k,
- candidate_count=self.n,
- **kwargs,
- )
-
- return _response_to_result(response, stop)
-
- async def _agenerate(
- self,
- messages: List[Dict[str, Any]],
- stop: Optional[List[str]] = None,
- **kwargs: Any,
- ) -> Dict[str, Any]:
- prompt = _messages_to_prompt_dict(messages)
-
- response: genai.types.ChatResponse = await self.achat_with_retry(
- model=self.model_name,
- prompt=prompt,
- temperature=self.temperature,
- top_p=self.top_p,
- top_k=self.top_k,
- candidate_count=self.n,
- )
-
- return _response_to_result(response, stop)
-
-
- @property
- def _identifying_params(self) -> Dict[str, Any]:
- """Get the identifying parameters."""
- return {
- "model_name": self.model_name,
- "temperature": self.temperature,
- "top_p": self.top_p,
- "top_k": self.top_k,
- "n": self.n,
- }
-
- @property
- def _llm_type(self) -> str:
- return "google-palm-chat"
\ No newline at end of file
+# def __call__(
+# self,
+# messages: List[Dict[str, Any]],
+# stop: Optional[List[str]] = None,
+# **kwargs: Any,
+# ) -> Dict[str, Any]:
+# prompt = _messages_to_prompt_dict(messages)
+
+# response: palm.types.ChatResponse = self.chat_with_retry(
+# model=self.model_name,
+# prompt=prompt,
+# temperature=self.temperature,
+# top_p=self.top_p,
+# top_k=self.top_k,
+# candidate_count=self.n,
+# **kwargs,
+# )
+
+# return _response_to_result(response, stop)
+
+# def generate(
+# self,
+# messages: List[Dict[str, Any]],
+# stop: Optional[List[str]] = None,
+# **kwargs: Any,
+# ) -> Dict[str, Any]:
+# prompt = _messages_to_prompt_dict(messages)
+
+# response: palm.types.ChatResponse = self.chat_with_retry(
+# model=self.model_name,
+# prompt=prompt,
+# temperature=self.temperature,
+# top_p=self.top_p,
+# top_k=self.top_k,
+# candidate_count=self.n,
+# **kwargs,
+# )
+
+# return _response_to_result(response, stop)
+
+# async def _agenerate(
+# self,
+# messages: List[Dict[str, Any]],
+# stop: Optional[List[str]] = None,
+# **kwargs: Any,
+# ) -> Dict[str, Any]:
+# prompt = _messages_to_prompt_dict(messages)
+
+# response: palm.types.ChatResponse = await self.achat_with_retry(
+# model=self.model_name,
+# prompt=prompt,
+# temperature=self.temperature,
+# top_p=self.top_p,
+# top_k=self.top_k,
+# candidate_count=self.n,
+# )
+
+# return _response_to_result(response, stop)
+
+# @property
+# def _identifying_params(self) -> Dict[str, Any]:
+# """Get the identifying parameters."""
+# return {
+# "model_name": self.model_name,
+# "temperature": self.temperature,
+# "top_p": self.top_p,
+# "top_k": self.top_k,
+# "n": self.n,
+# }
+
+# @property
+# def _llm_type(self) -> str:
+# return "google-palm-chat"
\ No newline at end of file
diff --git a/swarms/agents/tools/autogpt.py b/swarms/agents/tools/autogpt.py
index c989b655..af471449 100644
--- a/swarms/agents/tools/autogpt.py
+++ b/swarms/agents/tools/autogpt.py
@@ -132,11 +132,12 @@ class WebpageQATool(BaseTool):
query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm))
# !pip install duckduckgo_search
-web_search = DuckDuckGoSearchRun()
+# web_search = DuckDuckGoSearchRun()
# from swarms.agents.tools.code_intepretor import CodeInterpreter
# # @tool
-# code_intepret = CodeInterpreter()
\ No newline at end of file
+# code_intepret = CodeInterpreter()
+
diff --git a/swarms/orchestrator/autoscaler.py b/swarms/orchestrator/autoscaler.py
new file mode 100644
index 00000000..eff3fc37
--- /dev/null
+++ b/swarms/orchestrator/autoscaler.py
@@ -0,0 +1,62 @@
+import threading
+import queue
+from time import sleep
+from swarms.workers.autobot import AutoBot
+
+# TODO Handle task assignment and task delegation
+# TODO: User task => decomposed into very small sub tasks => sub tasks assigned to workers => workers complete and update the swarm, can ask for help from other agents.
+# TODO: Missing, Task Assignment, Task delegation, Task completion, Swarm level communication with vector db
+class AutoScaler:
+ def __init__(self,
+ initial_agents=10,
+ scale_up_factor=1,
+ idle_threshold=0.2,
+ busy_threshold=0.7
+ ):
+ self.agents_pool = [AutoBot() for _ in range(initial_agents)]
+ self.task_queue = queue.Queue()
+ self.scale_up_factor = scale_up_factor
+ self.idle_threshold = idle_threshold
+ self.lock = threading.Lock()
+
+ def add_task(self, task):
+ self.tasks_queue.put(task)
+
+ def scale_up(self):
+ with self.lock:
+ new_agents_counts = len(self.agents_pool) * self.scale_up_factor
+ for _ in range(new_agents_counts):
+ self.agents_pool.append(AutoBot())
+
+ def scale_down(self):
+ with self.lock:
+ if len(self.agents_pool) > 10: #ensure minmum of 10 agents
+ del self.agents_pool[-1] #remove last agent
+
+ def monitor_and_scale(self):
+ while True:
+ sleep(60)#check minute
+ pending_tasks = self.task_queue.qsize()
+ active_agents = sum([1 for agent in self.agents_pool if agent.is_busy()])
+
+ if pending_tasks / len(self.agents_pool) > self.busy_threshold:
+ self.scale_up()
+ elif active_agents / len(self.agents_pool) < self.idle_threshold:
+ self.scale_down()
+
+ def start(self):
+ monitor_thread = threading.Thread(target=self.monitor_and_scale)
+ monitor_thread.start()
+
+ while True:
+ task = self.task_queue.get()
+ if task:
+ available_agent = next((agent for agent in self.agents_pool))
+ if available_agent:
+ available_agent.run(task)
+
+
+
+
+
+
\ No newline at end of file
diff --git a/swarms/workers/autobot.py b/swarms/workers/autobot.py
new file mode 100644
index 00000000..07221130
--- /dev/null
+++ b/swarms/workers/autobot.py
@@ -0,0 +1,75 @@
+import faiss
+from langchain.chat_models import ChatOpenAI
+from langchain.docstore import InMemoryDocstore
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain_experimental.autonomous_agents import AutoGPT
+
+from swarms.agents.tools.autogpt import (
+ DuckDuckGoSearchRun,
+ FileChatMessageHistory,
+ ReadFileTool,
+ WebpageQATool,
+ WriteFileTool,
+ load_qa_with_sources_chain,
+ process_csv,
+ # web_search,
+ query_website_tool
+)
+
+
+ROOT_DIR = "./data/"
+
+
+class AutoBot:
+ def __init__(self,
+ model_name="gpt-4",
+ openai_api_key=None,
+ ai_name="Autobot Swarm Worker",
+ ai_role="Worker in a swarm",
+ # embedding_size=None,
+ # k=None,
+ temperature=0.5):
+ self.openai_api_key = openai_api_key
+ self.temperature = temperature
+ self.llm = ChatOpenAI(model_name=model_name,
+ openai_api_key=self.openai_api_key,
+ temperature=self.temperature)
+
+ self.ai_name = ai_name
+ self.ai_role = ai_role
+
+ # self.embedding_size = embedding_size
+ # # self.k = k
+
+ self.setup_tools()
+ self.setup_memory()
+ self.setup_agent()
+
+ def setup_tools(self):
+ self.tools = [
+ WriteFileTool(root_dir=ROOT_DIR),
+ ReadFileTool(root_dir=ROOT_DIR),
+ process_csv,
+ query_website_tool,
+ ]
+
+ def setup_memory(self):
+ embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+ embedding_size = 1536
+ index = faiss.IndexFlatL2(embedding_size)
+ self.vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
+
+ def setup_agent(self):
+ self.agent = AutoGPT.from_llm_and_tools(
+ ai_name=self.ai_name,
+ ai_role=self.ai_role,
+ tools=self.tools,
+ llm=self.llm,
+ memory=self.vectorstore.as_retriever(search_kwargs={"k": 8}),
+ )
+
+ def run(self, task):
+ result = self.agent.run([task])
+ return result
+
diff --git a/swarms/workers/worker_node.py b/swarms/workers/worker_node.py
index 0860630b..28077552 100644
--- a/swarms/workers/worker_node.py
+++ b/swarms/workers/worker_node.py
@@ -17,17 +17,17 @@ from swarms.agents.tools.autogpt import (
WriteFileTool,
load_qa_with_sources_chain,
process_csv,
- web_search,
-)
-from swarms.agents.tools.developer import (
- code_editor_append,
- code_editor_delete,
- code_editor_patch,
- code_editor_read,
- code_editor_summary,
- code_editor_write,
- terminal_execute,
+ # web_search,
)
+# from swarms.agents.tools.developer import (
+# code_editor_append,
+# code_editor_delete,
+# code_editor_patch,
+# code_editor_read,
+# code_editor_summary,
+# code_editor_write,
+# terminal_execute,
+# )
ROOT_DIR = "./data/"
@@ -182,8 +182,8 @@ class WorkerNode:
def initialize_vectorstore(self):
try:
embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
- embedding_size = self.embedding_size
- index = faiss.IndexFlatL2(embedding_size)
+ # embedding_size = self.embedding_size
+ index = faiss.IndexFlatL2(self.embedding_size)
return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
except Exception as e:
@@ -227,18 +227,11 @@ class WorkerNode:
llm = self.initialize_llm(llm_class, self.temperature)
tools = [
- web_search,
+ # web_search,
WriteFileTool(root_dir=ROOT_DIR),
ReadFileTool(root_dir=ROOT_DIR),
process_csv,
WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
- code_editor_append,
- code_editor_delete,
- code_editor_patch,
- code_editor_read,
- code_editor_summary,
- code_editor_write,
- terminal_execute,
]
if not tools:
logging.error("Tools are not initialized")
@@ -276,7 +269,7 @@ def worker_node(openai_api_key, objective):
try:
worker_node = WorkerNode(openai_api_key)
- worker_node.create_worker_node()
+ # worker_node.create_worker_node()
return worker_node.run(objective)
except Exception as e:
logging.error(f"An error occured in worker_node: {e}")