111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
import os
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_chroma import Chroma
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_core.documents import Document
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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# Load environment variables
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load_dotenv()
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# ---------- LLM and Embeddings ----------
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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# ---------- Vector Store ----------
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CHROMA_DIR = "./chroma_db"
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vector_store = Chroma(
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collection_name="knowledge",
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embedding_function=embeddings,
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persist_directory=CHROMA_DIR,
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)
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# ---------- Document Loader ----------
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def load_documents(directory: str):
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"""Read .txt/.md files, split into chunks and add to Chroma."""
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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docs = []
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for root, _, files in os.walk(directory):
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for file in files:
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if file.lower().endswith(('.txt', '.md')):
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path = os.path.join(root, file)
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with open(path, 'r', encoding='utf-8') as f:
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text = f.read()
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chunks = splitter.split_text(text)
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docs.extend([Document(page_content=c, metadata={"source": path}) for c in chunks])
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if docs:
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vector_store.add_documents(docs)
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vector_store.persist()
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# Load documents once at startup
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if not os.path.exists(CHROMA_DIR) or not os.listdir(CHROMA_DIR):
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load_documents("./documents")
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# ---------- Tools ----------
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@tool
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def search_local_kb(query: str, top_k: int = 3) -> str:
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"""Semantic search in the local knowledge base."""
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docs = vector_store.similarity_search(query, k=top_k)
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if not docs:
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return "No relevant information found in local knowledge base."
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return "\n---\n".join(f"{d.metadata.get('source', 'unknown')}\n{d.page_content}" for d in docs)
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@tool
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def web_search(query: str) -> str:
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"""Web search using Tavily."""
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from langchain_tavily import TavilySearchResults
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tavily = TavilySearchResults(max_results=3)
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results = tavily.run(query)
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if not results:
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return "No web results found."
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return "\n---\n".join(f"{r['title']}\n{r['content']}" for r in results)
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# ---------- Backend ----------
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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# ---------- Agent ----------
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agent = create_deep_agent(
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model=llm,
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tools=[search_local_kb, web_search],
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backend=backend,
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system_prompt="You are a helpful assistant. For questions about local documents use the local knowledge base. For up‑to‑date facts use web search. Always state the source (chromadb or tavily) in your answer.",
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)
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# ---------- Main Loop ----------
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async def main():
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print("RAG Agent ready. Type 'exit' to quit.")
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while True:
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user_input = input("\nЗапрос: ")
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if user_input.lower() in {"exit", "quit"}:
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print("Goodbye!")
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break
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# Invoke agent
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_input)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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# Extract last message content
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answer = result["messages"][-1].content
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print(f"\nОтвет:\n{answer}")
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if __name__ == "__main__":
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asyncio.run(main())
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