fix(): 1 исправлений, 0 отстояно — main.py
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@@ -1,140 +1,116 @@
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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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# main.py – RAG‑агент с ChromaDB и веб‑поиском (Tavily)
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# Используем deepagents, Ollama (LLM и эмбеддинги) и ChromaDB.
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# Всё в одном файле для удобства.
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"""
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import os
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import asyncio
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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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# ────────────────────────────────────── DeepAgents ────────────────────────
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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_ollama import ChatOllama
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from langchain_ollama import OllamaEmbeddings
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage
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# ---------------------------------------------------------------------------
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# Configuration
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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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# ────────────────────────────────────── ChromaDB ────────────────────────
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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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# ────────────────────────────────────── Tavily ────────────────────────
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from langchain_tavily import TavilySearchResults
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# ────────────────────────────────────── Настройки ────────────────────────
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") # не используется, но оставляем для совместимости
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TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
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if not TAVILY_API_KEY:
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raise RuntimeError("Требуется переменная окружения TAVILY_API_KEY")
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# Persist directory for ChromaDB
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# Папки
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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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DOCS_DIR = Path("./documents")
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WORKSPACE_DIR = Path("./workspace")
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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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# ────────────────────────────────────── LLM и Embeddings ────────────────────────
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llm = ChatOllama(model="llama3", temperature=0.0)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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# ────────────────────────────────────── VectorStore ────────────────────────
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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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embedding_function=embeddings,
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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: 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 file in directory.glob("**/*"):
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if file.suffix.lower() not in {".txt", ".md"}:
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# ────────────────────────────────────── Загрузка документов ────────────────────────
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def load_documents(directory: Path, store: Chroma, chunk_size: int = 1000, chunk_overlap: int = 200):
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"""Читает .txt/.md, разбивает на чанки и добавляет в Chroma."""
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if not directory.exists():
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print(f"Документы не найдены в {directory}")
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return
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splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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for file_path in directory.glob("**/*.*"):
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if file_path.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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vector_store.add_documents(docs)
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vector_store.persist()
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text = file_path.read_text(encoding="utf-8")
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docs = splitter.split_text(text)
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documents = [Document(page_content=chunk, metadata={"source": str(file_path)}) for chunk in docs]
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store.add_documents(documents)
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print("Документы загружены в Chroma.")
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# ---------------------------------------------------------------------------
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# 2. Tools
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# ---------------------------------------------------------------------------
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# Если база пустая – загрузим
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if not CHROMA_DIR.exists() or not any(CHROMA_DIR.iterdir()):
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load_documents(DOCS_DIR, vector_store)
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# ────────────────────────────────────── Инструменты ────────────────────────
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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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"""Семантический поиск в локальной базе знаний."""
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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 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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return "No results in local knowledge base."
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return "\n---\n".join(f"{d.metadata.get('source')}\n{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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"""Perform a web search using Tavily."""
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tavily = TavilySearchRun(api_key=TAVILY_API_KEY, max_results=3)
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"""Веб‑поиск через Tavily."""
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tavily = TavilySearchResults(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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api_key=OPENAI_API_KEY,
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temperature=0.0,
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)
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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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LocalShellBackend(workspace_dir=str(WORKSPACE_DIR)),
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FilesystemBackend(),
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])
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system_prompt = (
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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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# ────────────────────────────────────── Агент ────────────────────────
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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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system_prompt="Вы – RAG‑агент. При запросе о локальных документах используйте search_local_kb, для актуальных новостей – web_search. В ответе указывайте источник: chromadb или tavily.",
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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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# ────────────────────────────────────── Чат‑цикл ────────────────────────
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async def chat_loop():
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print("RAG‑агент готов. Введите запрос (exit для выхода).")
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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", "q"}:
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print("Goodbye!")
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user_input = input("Запрос: ")
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if user_input.lower() in {"exit", "quit"}:
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print("Выход.")
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break
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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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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 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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# Предполагаем, что агент возвращает сообщение с полем source в metadata
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response = result["messages"][-1].content
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print("\nОтвет:\n", response)
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print("\n---\n")
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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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asyncio.run(chat_loop())
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