fix(needs_fixes): 1 исправлений, 0 отстояно — main.py
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@@ -1,91 +1,91 @@
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import os
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#!/usr/bin/env python3
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"""RAG‑agent with ChromaDB and Tavily search.
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This implementation follows the course specification and uses the
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`deepagents` framework to create a single agent that can decide whether
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to query the local knowledge base (ChromaDB) or perform a web search
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via Tavily. The agent is backed by OpenRouter for both LLM and
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embeddings, complying with the mandatory technical constraints.
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"""
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import asyncio
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import os
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from pathlib import Path
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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 TavilySearchRun
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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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from langchain_core.messages import HumanMessage
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from tavily import TavilySearchResults
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# ---------------------
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------
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# ---------------------------------------------------------------------------
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# Load environment variables (OpenRouter key, Tavily key)
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
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# ---------------------
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# Vector store utilities
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# ---------------------
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# Persist directory for ChromaDB
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CHROMA_DIR = Path("./chroma_db")
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CHROMA_DIR.mkdir(parents=True, exist_ok=True)
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def create_vectorstore(persist_directory: str = "./chroma_db") -> Chroma:
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"""Create or load a Chroma vector store with OpenAI embeddings."""
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# ---------------------------------------------------------------------------
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# 1. Vector store (ChromaDB + OpenRouter embeddings)
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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=OPENAI_API_KEY,
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)
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return Chroma(
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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=persist_directory,
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persist_directory=str(CHROMA_DIR),
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)
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# Helper to load documents from a directory and add to the store
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def load_documents(directory: str, vectorstore: Chroma) -> None:
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"""Load .txt and .md files from *directory* into *vectorstore* using chunking."""
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def load_documents(directory: Path):
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"""Read .txt/.md files, split into chunks, and store in 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 fname in files:
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if fname.lower().endswith(('.txt', '.md')):
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path = os.path.join(root, fname)
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with open(path, "r", encoding="utf-8") as f:
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text = f.read()
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# Create Document objects
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docs.extend(
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[Document(page_content=chunk, metadata={"source": path}) for chunk in splitter.split_text(text)]
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)
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for file in directory.glob("**/*"):
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if file.suffix.lower() not in {".txt", ".md"}:
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continue
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text = file.read_text(encoding="utf-8")
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chunks = splitter.split_text(text)
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docs.extend([Document(page_content=c, metadata={"source": str(file)}) for c 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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# ---------------------
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# Tools
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# ---------------------
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vectorstore = create_vectorstore()
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# Ensure we have some data loaded – load from ./documents if collection empty
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if len(vectorstore.get_all_documents()) == 0:
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load_documents("./documents", vectorstore)
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vector_store.add_documents(docs)
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vector_store.persist()
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# ---------------------------------------------------------------------------
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# 2. Tools
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# ---------------------------------------------------------------------------
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@tool
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def search_local_kb(query: str, top_k: int = 3) -> str:
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"""Search the local knowledge base for relevant passages."""
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docs = vectorstore.similarity_search(query, k=top_k)
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"""Semantic search in the local ChromaDB 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 "[Local KB] No relevant information found."
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result = "\n\n".join([f"{d.metadata.get('source', 'unknown')}\n{d.page_content}" for d in docs])
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return f"[Local KB]\n{result}"
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return "No relevant information found in the local knowledge base."
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return "\n---\n".join([f"{i+1}. {d.page_content[:200]}…" for i, d in enumerate(docs)])
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@tool
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def web_search(query: str) -> str:
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"""Perform a web search using Tavily and return top results."""
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results = TavilySearchResults(query=query, max_results=3, api_key=TAVILY_API_KEY)
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if not results.results:
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return "[Web Search] No results found."
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snippets = []
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for r in results.results:
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snippets.append(f"{r.get('title', 'No title')}\n{r.get('content', 'No content')}\nURL: {r.get('url', '')}")
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return f"[Web Search]\n\n".join(snippets)
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# ---------------------
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# Agent setup
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# ---------------------
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"""Perform a web search using Tavily."""
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tavily = TavilySearchRun(api_key=TAVILY_API_KEY, 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"{i+1}. {r['title']}\n{r['content'][:200]}…" for i, r in enumerate(results)])
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# ---------------------------------------------------------------------------
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# 3. Agent (deepagents)
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# ---------------------------------------------------------------------------
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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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@@ -99,9 +99,11 @@ backend = CompositeBackend([
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])
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system_prompt = (
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"You are a helpful RAG agent. For questions about local documents, use the tool `search_local_kb`. "
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"For current news or facts that may not be in your local knowledge base, use `web_search`. "
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"Return the answer prefixed with either `[Local KB]` or `[Web Search]` to indicate the source."
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"You are a helpful assistant. For a user query, first decide whether the
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answer can be found in the local knowledge base. If so, use the
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`search_local_kb` tool. If the query requires up‑to‑date information,
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use the `web_search` tool. Respond with the best answer and clearly
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state the source: `chromadb` or `tavily`."
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)
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agent = create_deep_agent(
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@@ -111,25 +113,28 @@ agent = create_deep_agent(
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system_prompt=system_prompt,
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)
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# ---------------------
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# CLI loop
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# ---------------------
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# ---------------------------------------------------------------------------
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# 4. CLI loop
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# ---------------------------------------------------------------------------
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async def main():
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print("RAG Agent ready. Type your question (or 'exit' to quit).")
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thread_id = "session-1"
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while True:
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user_input = input("\nQuery: ")
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user_input = input("\nЗапрос: ")
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if user_input.lower() in {"exit", "quit", "q"}:
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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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response = await agent.ainvoke(
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{"messages": [{"role": "user", "content": user_input}]},
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{"configurable": {"thread_id": thread_id}},
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)
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# The agent returns a list of messages; get the last one
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reply = result["messages"][-1].content
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print(f"\n{reply}")
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# The last message contains the assistant reply
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assistant_msg = response["messages"][-1].content
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print(f"\nОтвет:\n{assistant_msg}")
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if __name__ == "__main__":
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# Load documents once at startup
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docs_dir = Path("./documents")
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if docs_dir.exists():
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load_documents(docs_dir)
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asyncio.run(main())
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