fix: main.py — Агент с RAG-памятью
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@@ -1,135 +1,41 @@
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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_core.documents import Document
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_qdrant import QdrantVectorStore
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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 rag_agent import agent, add_to_knowledge_base, search_knowledge_base
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# Configuration
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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if not OPENAI_API_KEY:
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raise RuntimeError("OPENAI_API_KEY not set in environment")
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# LLM via OpenRouter
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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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# Embeddings via OpenRouter
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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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# Qdrant client and vector store
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from qdrant_client import QdrantClient
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qdrant_client = QdrantClient(url="http://localhost:6333")
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collection_name = "knowledge_base"
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vector_store = QdrantVectorStore(
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client=qdrant_client,
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collection_name=collection_name,
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embedding_function=embeddings,
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)
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# Text splitter
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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# Tool: search knowledge base
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@tool
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def search_knowledge_base(query: str, max_results: int = 3) -> str:
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"""Search the knowledge base for relevant information."""
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docs = vector_store.similarity_search(query, k=max_results)
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if not docs:
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return "No results found."
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return "\n\n".join(f"Title: {doc.metadata.get('title', 'unknown')}\n{doc.page_content}" for doc in docs)
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# Tool: add to knowledge base
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@tool
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def add_to_knowledge_base(content: str, title: str = "document") -> str:
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"""Add content to the knowledge base."""
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chunks = splitter.split_text(content)
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docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
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vector_store.add_documents(docs)
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return f"Added {len(docs)} chunks from '{title}'."
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# Backend for deepagents
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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_knowledge_base, add_to_knowledge_base],
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backend=backend,
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system_prompt="You are a helpful knowledge assistant. Use the provided tools to search and add information.",
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)
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# Helper: load documents from a directory
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def load_documents_from_dir(directory: str):
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dir_path = Path(directory)
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if not dir_path.is_dir():
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raise ValueError(f"Directory {directory} does not exist.")
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for file_path in dir_path.rglob("*"):
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if file_path.is_file() and file_path.suffix.lower() in {".txt", ".md", ".py", ".json"}:
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content = file_path.read_text(encoding="utf-8")
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title = file_path.stem
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add_to_knowledge_base(content, title)
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# Interactive CLI
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async def interactive_loop():
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print("RAG Agent CLI. Commands: /add <file_path>, /search <query>, /quit")
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print("Welcome to the RAG agent. Type /add to add a document, /search to query, /quit to exit.")
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thread_id = "session-1"
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while True:
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user_input = input(">> ").strip()
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if not user_input:
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continue
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if user_input.lower() == "/quit":
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print("Exiting.")
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print("Goodbye!")
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break
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if user_input.startswith("/add "):
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_, file_path = user_input.split(maxsplit=1)
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try:
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content = Path(file_path).read_text(encoding="utf-8")
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title = Path(file_path).stem
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elif user_input.lower() == "/add":
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title = input("Title: ").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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result = add_to_knowledge_base(content, title)
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print(result)
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except Exception as e:
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print(f"Error adding file: {e}")
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continue
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if user_input.startswith("/search "):
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_, query = user_input.split(maxsplit=1)
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result = search_knowledge_base(query, max_results=3)
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elif user_input.lower() == "/search":
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query = input("Query: ").strip()
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result = search_knowledge_base(query)
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print("Search results:")
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print(result)
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continue
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# Treat as normal message to agent
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try:
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else:
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messages = [HumanMessage(content=user_input)]
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response = 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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{"messages": messages},
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{"configurable": {"thread_id": thread_id}},
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)
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print(response["messages"][-1].content)
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except Exception as e:
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print(f"Agent error: {e}")
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def main():
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# Optional: load initial docs from a folder
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init_dir = os.getenv("INIT_DOCS_DIR")
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if init_dir:
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try:
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load_documents_from_dir(init_dir)
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print(f"Loaded documents from {init_dir}")
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except Exception as e:
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print(f"Failed to load initial docs: {e}")
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asyncio.run(interactive_loop())
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if __name__ == "__main__":
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