112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import os
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import asyncio
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from pathlib import Path
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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_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_tavily import TavilySearchResults
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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 = Path("./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=str(CHROMA_DIR),
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)
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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 documents found in local KB."
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return "\n---\n".join(f"{i+1}. {doc.page_content[:200]}..." for i, doc in enumerate(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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tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
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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"{i+1}. {res['title']}\n{res['content'][:200]}..." for i, res in enumerate(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=(
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"You are a helpful assistant. For any user query, decide whether to use the local knowledge base or perform a web search. "
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"If the query is about recent events, news, or requires up‑to‑date information, use the web_search tool. "
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"Otherwise, use search_local_kb. "
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"Always return the source used in the response (either 'chromadb' or 'tavily')."
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),
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)
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# ---------- Document Loader ----------
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def load_documents(directory: str, vectorstore: Chroma):
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"""Load .txt and .md files from a directory, chunk them, and add to the vector store."""
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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docs = []
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for file_path in Path(directory).glob("**/*"):
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if file_path.suffix.lower() in {".txt", ".md"}:
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text = file_path.read_text(encoding="utf-8")
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chunks = splitter.split_text(text)
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docs.extend([Document(page_content=chunk, metadata={"source": str(file_path)}) for chunk in chunks])
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if docs:
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vectorstore.add_documents(docs)
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vectorstore.persist()
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# ---------- CLI ----------
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async def main():
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# Ensure vector store is loaded
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if not CHROMA_DIR.exists() or not any(CHROMA_DIR.iterdir()):
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print("Loading documents into ChromaDB…")
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load_documents("./documents", vector_store)
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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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break
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result = await agent.ainvoke(
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{"messages": [{"role": "user", "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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content = result["messages"][-1].content
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print(f"\nОтвет:\n{content}")
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if __name__ == "__main__":
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asyncio.run(main())
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