swarms/swarms/workers/worker.py

import faiss
from langchain.chat_models import ChatOpenAI
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores import FAISS
from langchain_experimental.autonomous_agents import AutoGPT

from swarms.tools.autogpt import (
    ReadFileTool,
    VQAinference,
    WriteFileTool,
    compile,
    process_csv,
    query_website_tool,
)
from swarms.utils.decorators import error_decorator, log_decorator, timing_decorator

ROOT_DIR = "./data/"


class Worker:
    """Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks, it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on"""
    @log_decorator
    @error_decorator
    @timing_decorator
    def __init__(
        self, 
        model_name="gpt-4", 
        openai_api_key=None,
        ai_name="Autobot Swarm Worker",
        ai_role="Worker in a swarm",
        external_tools = None,
        human_in_the_loop=False,
        temperature=0.5,
        llm=None,
        openai: bool = True,
    ):
        self.openai_api_key = openai_api_key
        self.temperature = temperature
        self.human_in_the_loop = human_in_the_loop

        
        if self.openai is True:
            try:
                self.llm = ChatOpenAI(
                    model_name=model_name, 
                    openai_api_key=self.openai_api_key, 
                    temperature=self.temperature
                )
            except Exception as error:
                raise RuntimeError(f"Error Initializing ChatOpenAI: {error}")    
        else:
            self.llm = llm(
                model_name=model_name, 
                temperature=self.temperature
            )
            
        self.ai_name = ai_name
        self.ai_role = ai_role

        # self.embedding_size = embedding_size
        # # self.k = k

        self.setup_tools(external_tools)
        self.setup_memory()
        self.setup_agent()
    
    def reset(self):
        self.message_history = ["Here is the conversation so far"]
    
    def receieve(self, name: str, message: str) -> None:
        self.message_history.append(f"{name}: {message}")

    
    @log_decorator
    @error_decorator
    @timing_decorator
    def setup_tools(self, external_tools):
        """
        external_tools = [MyTool1(), MyTool2()]
        worker = Worker(model_name="gpt-4", 
                openai_api_key="my_key", 
                ai_name="My Worker", 
                ai_role="Worker", 
                external_tools=external_tools, 
                human_in_the_loop=False, 
                temperature=0.5)
        
        """
        self.tools = [
            WriteFileTool(root_dir=ROOT_DIR),
            ReadFileTool(root_dir=ROOT_DIR),
            process_csv,
            query_website_tool,
            HumanInputRun(),
            compile,
            VQAinference
        ]
        if external_tools is not None:
            self.tools.extend(external_tools)

    def setup_memory(self):
        try:
            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({}), {})
        except Exception as error:
            raise RuntimeError(f"Error setting up memory perhaps try try tuning the embedding size: {error}")
        
    
    def setup_agent(self):
        try: 
            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}),
                human_in_the_loop=self.human_in_the_loop
            )
        
        except Exception as error:
            raise RuntimeError(f"Error setting up agent: {error}")
    
    @log_decorator
    @error_decorator
    @timing_decorator
    def run(self, task):
        try:
            result = self.agent.run([task])
            return result
        except Exception as error:
            raise RuntimeError(f"Error while running agent: {error}")
    
    @log_decorator
    @error_decorator
    @timing_decorator
    def __call__(self, task):
        try:
            results = self.agent.run([task])
            return results
        except Exception as error:
            raise RuntimeError(f"Error while running agent: {error}")