170 lines
6.9 KiB
Python
170 lines
6.9 KiB
Python
# main.py
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# Полностью рабочий пример агента с RAG‑памятью на Qdrant и OpenRouter
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# Использует deepagents, langchain‑openai, langchain‑qdrant, langchain‑core
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# Запуск: python main.py
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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 typing import List
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_core.documents import Document
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from langchain_qdrant import QdrantVectorStore
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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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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# ---------------------------------------------------------------------------
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# Конфигурация LLM и Embeddings
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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=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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# ---------------------------------------------------------------------------
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# Qdrant клиент и коллекция
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# ---------------------------------------------------------------------------
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# Предполагается, что Qdrant запущен локально на порту 6333
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qdrant_url = "http://localhost:6333"
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collection_name = "knowledge_base"
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vector_store = QdrantVectorStore(
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embeddings=embeddings,
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url=qdrant_url,
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collection_name=collection_name,
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# Если коллекция не существует, она будет создана автоматически
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)
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# ---------------------------------------------------------------------------
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# Чанкинг
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# ---------------------------------------------------------------------------
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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# ---------------------------------------------------------------------------
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# Инструменты для агента
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# ---------------------------------------------------------------------------
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@tool
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def search_knowledge_base(query: str, max_results: int = 5) -> str:
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"""Semantic search in the knowledge base.
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Returns a formatted string with the top results.
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"""
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results = vector_store.similarity_search_with_score(query, k=max_results)
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if not results:
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return "No relevant documents found."
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formatted = []
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for doc, score in results:
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formatted.append(f"Title: {doc.metadata.get('title', 'Untitled')}\nScore: {score:.4f}\nContent: {doc.page_content[:200]}...\n")
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return "\n".join(formatted)
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@tool
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def add_to_knowledge_base(content: str, title: str) -> str:
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"""Add a new document to the knowledge base.
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The content is split into chunks, embedded and stored.
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"""
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# Split into chunks
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chunks = text_splitter.split_text(content)
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docs: List[Document] = []
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for i, chunk in enumerate(chunks):
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docs.append(Document(page_content=chunk, metadata={"title": title, "chunk_index": i}))
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# Add to vector store
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vector_store.add_documents(docs)
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return f"Added {len(docs)} chunks for document '{title}'."
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# ---------------------------------------------------------------------------
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# Backend для deepagents
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# ---------------------------------------------------------------------------
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backend = CompositeBackend(
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default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
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routes={},
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)
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# ---------------------------------------------------------------------------
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# Создание агента
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# ---------------------------------------------------------------------------
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agent = create_deep_agent(
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model=llm,
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tools=[search_knowledge_base, add_to_knowledge_base],
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backend=backend,
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system_prompt="You are a helpful assistant with access to a knowledge base. Use the provided tools to search and add information."
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)
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# ---------------------------------------------------------------------------
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# Инициализация: загрузка документов из директории
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# ---------------------------------------------------------------------------
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DOCS_DIR = Path("./docs")
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async def load_documents_from_dir(directory: Path):
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if not directory.exists():
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return
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for file_path in directory.rglob("*.txt"):
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content = file_path.read_text(encoding="utf-8")
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title = file_path.stem
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await agent.ainvoke(
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{"messages": [HumanMessage(content=f"/add {title}")], "content": content},
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{"configurable": {"thread_id": "init-session"}},
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)
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# ---------------------------------------------------------------------------
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# Интерактивный клиент
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# ---------------------------------------------------------------------------
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async def interactive_loop():
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print("Welcome to the RAG agent. Commands: /add <title>, /search <query>, /quit")
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thread_id = "interactive-session"
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while True:
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user_input = input("> ")
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if user_input.strip() == "/quit":
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print("Goodbye!")
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break
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if user_input.startswith("/add "):
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title = user_input[5:].strip()
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print("Enter content (end with a single line containing only 'END'): ")
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lines = []
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while True:
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line = input()
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if line.strip() == "END":
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break
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lines.append(line)
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content = "\n".join(lines)
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await agent.ainvoke(
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{"messages": [HumanMessage(content=f"/add {title}")], "content": content},
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{"configurable": {"thread_id": thread_id}},
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)
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print(f"Document '{title}' added.")
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elif user_input.startswith("/search "):
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query = user_input[8:].strip()
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=f"/search {query}")]},
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{"configurable": {"thread_id": thread_id}},
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)
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print(result["messages"][-1].content)
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else:
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# обычный запрос к LLM
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_input)]},
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{"configurable": {"thread_id": thread_id}},
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)
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print(result["messages"][-1].content)
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# ---------------------------------------------------------------------------
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# Основной запуск
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# ---------------------------------------------------------------------------
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async def main():
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# Загрузим документы из каталога docs при старте
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await load_documents_from_dir(DOCS_DIR)
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await interactive_loop()
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if __name__ == "__main__":
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asyncio.run(main())
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