Former-commit-id: 9efcbff901a2e954643c4d475ecb1514652e60edclean-history
Kye
1 year ago
parent
f6c230e6cc
commit
8903f356d0
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# from swarms.workers.worker import Worker
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import os
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import random
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from typing import Dict, Union
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import faiss
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from langchain.chains.qa_with_sources.loading import (
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load_qa_with_sources_chain,
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)
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from langchain.docstore import InMemoryDocstore
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.tools import ReadFileTool, WriteFileTool
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from langchain.tools.human.tool import HumanInputRun
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from langchain.vectorstores import FAISS
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from langchain_experimental.autonomous_agents import AutoGPT
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from swarms.agents.message import Message
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from swarms.tools.autogpt import (
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WebpageQATool,
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process_csv,
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)
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from swarms.utils.decorators import error_decorator, timing_decorator
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# cache
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ROOT_DIR = "./data/"
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class Worker:
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"""
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Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks,
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it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on
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Parameters:
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- `model_name` (str): The name of the language model to be used (default: "gpt-4").
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- `openai_api_key` (str): The OpenvAI API key (optional).
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- `ai_name` (str): The name of the AI worker.
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- `ai_role` (str): The role of the AI worker.
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- `external_tools` (list): List of external tools (optional).
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- `human_in_the_loop` (bool): Enable human-in-the-loop interaction (default: False).
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- `temperature` (float): The temperature parameter for response generation (default: 0.5).
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- `llm` (ChatOpenAI): Pre-initialized ChatOpenAI model instance (optional).
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- `openai` (bool): If True, use the OpenAI language model; otherwise, use `llm` (default: True).
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Usage
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```
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from swarms import Worker
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node = Worker(
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ai_name="Optimus Prime",
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)
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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."
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response = node.run(task)
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print(response)
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```
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llm + tools + memory
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"""
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def __init__(
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self,
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ai_name: str = "Autobot Swarm Worker",
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ai_role: str = "Worker in a swarm",
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external_tools=None,
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human_in_the_loop=False,
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temperature: float = 0.5,
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llm=None,
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openai_api_key: str = None,
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):
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self.temperature = temperature
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self.human_in_the_loop = human_in_the_loop
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self.llm = llm
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self.openai_api_key = openai_api_key
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self.ai_name = ai_name
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self.ai_role = ai_role
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self.coordinates = (
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random.randint(0, 100),
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random.randint(0, 100),
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) # example coordinates for proximity
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self.setup_tools(external_tools)
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self.setup_memory()
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self.setup_agent()
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def reset(self):
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"""
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Reset the message history.
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"""
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self.message_history = ["Here is the conversation so far"]
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@property
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def name(self):
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"""Name of the agent"""
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return self.ai_name
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def receieve(self, name: str, message: str) -> None:
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"""
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Receive a message and update the message history.
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Parameters:
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- `name` (str): The name of the sender.
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- `message` (str): The received message.
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"""
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self.message_history.append(f"{name}: {message}")
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def send(self) -> str:
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"""Send message history."""
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self.agent.run(task=self.message_history)
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def add(self, task, priority=0):
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"""Add a task to the task queue."""
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self.task_queue.append((priority, task))
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def setup_tools(self, external_tools):
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"""
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Set up tools for the worker.
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Parameters:
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- `external_tools` (list): List of external tools (optional).
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Example:
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```
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external_tools = [MyTool1(), MyTool2()]
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worker = Worker(model_name="gpt-4",
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openai_api_key="my_key",
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ai_name="My Worker",
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ai_role="Worker",
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external_tools=external_tools,
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human_in_the_loop=False,
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temperature=0.5)
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```
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"""
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query_website_tool = WebpageQATool(
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qa_chain=load_qa_with_sources_chain(self.llm)
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)
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self.tools = [
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WriteFileTool(root_dir=ROOT_DIR),
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ReadFileTool(root_dir=ROOT_DIR),
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process_csv,
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query_website_tool,
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HumanInputRun(),
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# compile,
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# VQAinference,
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]
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if external_tools is not None:
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self.tools.extend(external_tools)
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def setup_memory(self):
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"""
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Set up memory for the worker.
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"""
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openai_api_key = os.getenv("OPENAI_API_KEY") or self.openai_api_key
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try:
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embeddings_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
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embedding_size = 1536
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index = faiss.IndexFlatL2(embedding_size)
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self.vectorstore = FAISS(
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embeddings_model.embed_query, index, InMemoryDocstore({}), {}
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)
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except Exception as error:
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raise RuntimeError(
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"Error setting up memory perhaps try try tuning the embedding size:"
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f" {error}"
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)
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def setup_agent(self):
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"""
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Set up the autonomous agent.
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"""
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try:
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self.agent = AutoGPT.from_llm_and_tools(
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ai_name=self.ai_name,
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ai_role=self.ai_role,
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tools=self.tools,
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llm=self.llm,
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memory=self.vectorstore.as_retriever(search_kwargs={"k": 8}),
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human_in_the_loop=self.human_in_the_loop,
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)
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except Exception as error:
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raise RuntimeError(f"Error setting up agent: {error}")
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# @log_decorator
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@error_decorator
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@timing_decorator
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def run(self, task: str = None):
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"""
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Run the autonomous agent on a given task.
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Parameters:
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- `task`: The task to be processed.
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Returns:
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- `result`: The result of the agent's processing.
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"""
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try:
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result = self.agent.run([task])
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return result
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except Exception as error:
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raise RuntimeError(f"Error while running agent: {error}")
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# @log_decorator
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@error_decorator
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@timing_decorator
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def __call__(self, task: str = None):
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"""
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Make the worker callable to run the agent on a given task.
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Parameters:
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- `task`: The task to be processed.
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Returns:
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- `results`: The results of the agent's processing.
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"""
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try:
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results = self.agent.run([task])
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return results
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except Exception as error:
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raise RuntimeError(f"Error while running agent: {error}")
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def health_check(self):
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pass
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# @log_decorator
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@error_decorator
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@timing_decorator
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def chat(self, msg: str = None, streaming: bool = False):
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"""
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Run chat
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Args:
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msg (str, optional): Message to send to the agent. Defaults to None.
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language (str, optional): Language to use. Defaults to None.
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streaming (bool, optional): Whether to stream the response. Defaults to False.
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Returns:
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str: Response from the agent
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Usage:
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--------------
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agent = MultiModalAgent()
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agent.chat("Hello")
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"""
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# add users message to the history
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self.history.append(Message("User", msg))
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# process msg
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try:
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response = self.agent.run(msg)
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# add agent's response to the history
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self.history.append(Message("Agent", response))
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# if streaming is = True
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if streaming:
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return self._stream_response(response)
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else:
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response
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except Exception as error:
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error_message = f"Error processing message: {str(error)}"
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# add error to history
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self.history.append(Message("Agent", error_message))
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return error_message
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def _stream_response(self, response: str = None):
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"""
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Yield the response token by token (word by word)
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Usage:
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--------------
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for token in _stream_response(response):
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print(token)
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"""
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for token in response.split():
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yield token
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@staticmethod
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def _message_to_dict(message: Union[Dict, str]):
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"""Convert a message"""
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if isinstance(message, str):
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return {"content": message}
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else:
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return message
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def is_within_proximity(self, other_worker):
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"""Using Euclidean distance for proximity check"""
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distance = (
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(self.coordinates[0] - other_worker.coordinates[0]) ** 2
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+ (self.coordinates[1] - other_worker.coordinates[1]) ** 2
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) ** 0.5
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return distance < 10 # threshold for proximity
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