62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import logging
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import os
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import yaml
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import asyncio
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from browser_use import Agent
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_community.document_loaders import DirectoryLoader
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_community.vectorstores import FAISS
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from langchain.memory import ConversationBufferMemory
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from dotenv import load_dotenv
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load_dotenv()
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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with open('config.yaml') as f:
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config = yaml.safe_load(f)
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def load_documents():
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loader = DirectoryLoader(config['documents_dir'], glob="**/*.txt")
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docs = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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return text_splitter.split_documents(docs)
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def setup_rag():
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docs = load_documents()
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vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
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return vectorstore.as_retriever()
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memory = ConversationBufferMemory()
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def generate_response(query):
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retriever = setup_rag()
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relevant_docs = retriever.get_relevant_documents(query)
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context = "\n\n".join([doc.page_content for doc in relevant_docs])
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memory.save_context({"input": query}, {"output": ""})
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history = memory.load_memory_variables({})
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llm = ChatOpenAI(model=config['deepseek']['model'], api_key=config['deepseek']['api_key'])
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response = llm.invoke(f"Context: {context}\n\nHistory: {history}\n\nQuestion: {query}")
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return f"{config['bot_disclaimer']}\n\n{response.content}"
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async def process_messages():
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agent = Agent(
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llm=ChatOpenAI(model="gpt-4o"),
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task="Log in to LinkedIn, go to messages, check for new messages, and reply based on the provided knowledge base."
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)
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while True:
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try:
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result = await agent.run()
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logging.info(f"Agent result: {result}")
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time.sleep(60)
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except Exception as e:
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logging.error(f"Error: {str(e)}")
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time.sleep(300)
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if __name__ == "__main__":
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logging.info("Starting bot...")
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asyncio.run(process_messages())
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