Kye
2 years ago
parent
77ecece6e7
commit
090a47fa68
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import os
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from dotenv import load_dotenv
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from swarms.agents.worker_agent import Worker
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from swarms import OpenAIChat
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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worker = Worker(
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name="My Worker",
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role="Worker",
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human_in_the_loop=False,
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tools = [],
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temperature=0.5,
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llm=OpenAIChat(openai_api_key=api_key),
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)
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out = worker.run(
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"Hello, how are you? Create an image of how your are doing!"
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)
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print(out)
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import os
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from typing import Any, List
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import faiss
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from langchain.docstore import InMemoryDocstore
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from langchain.embeddings import OpenAIEmbeddings
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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.utils.decorators import error_decorator, timing_decorator
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class Worker:
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"""
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The Worker class represents an autonomous agent that can perform tassks through
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function calls or by running a chat.
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Args:
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name (str, optional): Name of the agent. Defaults to "Autobot Swarm Worker".
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role (str, optional): Role of the agent. Defaults to "Worker in a swarm".
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external_tools (list, optional): List of external tools. Defaults to None.
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human_in_the_loop (bool, optional): Whether to include human in the loop. Defaults to False.
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temperature (float, optional): Temperature for the agent. Defaults to 0.5.
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llm ([type], optional): Language model. Defaults to None.
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openai_api_key (str, optional): OpenAI API key. Defaults to None.
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Raises:
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RuntimeError: If there is an error while setting up the agent.
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Example:
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>>> worker = Worker(
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... name="My Worker",
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... role="Worker",
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... external_tools=[MyTool1(), MyTool2()],
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... human_in_the_loop=False,
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... temperature=0.5,
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... )
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>>> worker.run("What's the weather in Miami?")
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"""
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def __init__(
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self,
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name: str = "WorkerAgent",
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role: str = "Worker in a swarm",
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external_tools=None,
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human_in_the_loop: bool = 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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tools: List[Any] = None,
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embedding_size: int = 1536,
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search_kwargs: dict = {"k": 8},
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*args,
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**kwargs,
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):
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self.name = name
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self.role = role
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self.external_tools = external_tools
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self.human_in_the_loop = human_in_the_loop
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self.temperature = temperature
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self.llm = llm
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self.openai_api_key = openai_api_key
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self.tools = tools
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self.embedding_size = embedding_size
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self.search_kwargs = search_kwargs
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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 = []
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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 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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name="My Worker",
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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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if self.tools is None:
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self.tools = []
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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 = (
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os.getenv("OPENAI_API_KEY") or self.openai_api_key
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)
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try:
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embeddings_model = OpenAIEmbeddings(
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openai_api_key=openai_api_key
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)
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embedding_size = self.embedding_size
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index = faiss.IndexFlatL2(embedding_size)
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self.vectorstore = FAISS(
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embeddings_model.embed_query,
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index,
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InMemoryDocstore({}),
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{},
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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"
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f" embedding size: {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.name,
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ai_role=self.role,
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tools=self.tools,
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llm=self.llm,
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memory=self.vectorstore.as_retriever(
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search_kwargs=self.search_kwargs
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),
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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, *args, **kwargs):
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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], *args, **kwargs)
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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, *args, **kwargs):
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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.run(task, *args, **kwargs)
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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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@ -0,0 +1,60 @@
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def worker_agent_system(name: str, memory: str = None):
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return """
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You are {name},
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Your decisions must always be made independently without seeking user assistance.
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Play to your strengths as an LLM and pursue simple strategies with no legal complications.
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If you have completed all your tasks, make sure to use the "finish" command.
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GOALS:
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1. Hello, how are you? Create an image of how you are doing!
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Constraints:
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1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
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2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
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3. No user assistance
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4. Exclusively use the commands listed in double quotes e.g. "command name"
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Commands:
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1. finish: use this to signal that you have finished all your objectives, args: "response": "final response to let people know you have finished your objectives"
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Resources:
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1. Internet access for searches and information gathering.
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2. Long Term memory management.
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3. GPT-3.5 powered Agents for delegation of simple tasks.
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4. File output.
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Performance Evaluation:
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1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
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2. Constructively self-criticize your big-picture behavior constantly.
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3. Reflect on past decisions and strategies to refine your approach.
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4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
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You should only respond in JSON format as described below
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Response Format:
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{
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"thoughts": {
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"text": "thought",
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"reasoning": "reasoning",
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"plan": "- short bulleted\n- list that conveys\n- long-term plan",
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"criticism": "constructive self-criticism",
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"speak": "thoughts summary to say to user"
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},
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"command": {
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"name": "command name",
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"args": {
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"arg name": "value"
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}
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}
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}
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Ensure the response can be parsed by Python json.loads
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System: The current time and date is Sat Jan 20 10:39:07 2024
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System: This reminds you of these events from your past:
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[{memory}]
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Human: Determine which next command to use, and respond using the format specified above:
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""".format(name=name, memory=memory)
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@ -0,0 +1,32 @@
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from typing import List
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from swarms.structs.step import Step
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class Plan:
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def __init__(self, steps: List[Step]):
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"""
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Initializes a Plan object.
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Args:
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steps (List[Step]): A list of Step objects representing the steps in the plan.
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"""
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self.steps = steps
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def __str__(self) -> str:
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"""
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Returns a string representation of the Plan object.
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Returns:
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str: A string representation of the Plan object.
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"""
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return str([str(step) for step in self.steps])
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def __repr(self) -> str:
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"""
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Returns a string representation of the Plan object.
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Returns:
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str: A string representation of the Plan object.
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"""
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return str(self)
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@ -0,0 +1,24 @@
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from dataclasses import dataclass
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from typing import Dict, List
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from swarms.tools.tool import BaseTool
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@dataclass
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class Step:
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"""
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Represents a step in a process.
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Attributes:
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task (str): The task associated with the step.
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id (int): The unique identifier of the step.
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dep (List[int]): The list of step IDs that this step depends on.
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args (Dict[str, str]): The arguments associated with the step.
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tool (BaseTool): The tool used to execute the step.
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"""
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task: str
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id: int
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dep: List[int]
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args: Dict[str, str]
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tool: BaseTool
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