100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
import os
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import asyncio
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from pathlib import Path
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from typing import List
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from langchain_ollama import OllamaEmbeddings
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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_tavily import TavilySearchResults
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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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# --------------------------- LLM ---------------------------
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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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# --------------------------- 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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# --------------------------- Vectorstore ---------------------------
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PERSIST_DIR = Path("./chroma_db")
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PERSIST_DIR.mkdir(parents=True, exist_ok=True)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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vectorstore = Chroma(persist_directory=str(PERSIST_DIR), embedding_function=embeddings)
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# Load documents from ./documents if not already loaded
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DOCS_DIR = Path("./documents")
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if DOCS_DIR.exists():
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for file in DOCS_DIR.glob("**/*.*"):
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if file.suffix.lower() in {".txt", ".md"}:
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text = file.read_text(encoding="utf-8")
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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docs = splitter.split_text(text)
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vectorstore.add_texts(docs, metadatas=[{"source": str(file)} for _ in docs])
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vectorstore.persist()
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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 ChromaDB knowledge base."""
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retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
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docs = retriever.invoke(query)
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if not docs:
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return "No local knowledge found."
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return "\n---\n".join([f"{d.page_content[:500]}..." 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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tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
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results = tavily.invoke(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']}: {r['content'][:500]}..." for r in results])
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# --------------------------- Agent ---------------------------
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SYSTEM_PROMPT = (
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"You are an AI assistant that can answer questions using either a local knowledge base or the web. "
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"If the answer can be found in the local documents, use the `search_local_kb` tool and prefix the response with `[Local KB]`. "
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"If the answer requires up‑to‑date information, use the `web_search` tool and prefix the response with `[Web Search]`. "
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"Always indicate the source in the response."
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)
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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=SYSTEM_PROMPT,
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)
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# --------------------------- CLI ---------------------------
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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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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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# The last message is the assistant's reply
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reply = result["messages"][-1].content
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print(f"\n{reply}")
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if __name__ == "__main__":
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asyncio.run(main())
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