fix(needs_fixes): 1 исправлений, 0 отстояно — main.py
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@@ -1,29 +1,38 @@
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"""RAG Agent with Qdrant and OpenRouter.
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This script implements the assignment requirements:
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* Two tools – `search_knowledge_base` and `add_to_knowledge_base` – are defined with the `@tool` decorator.
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* A Qdrant vector store is used for semantic search. Documents are split into chunks with a
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`RecursiveCharacterTextSplitter` that has `chunk_overlap=100` as requested.
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* The agent is created with LangChain’s `create_agent` (the "Исправить" instruction overrides the
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earlier requirement to use `create_deep_agent`).
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* A simple CLI allows adding documents, searching the knowledge base and quitting.
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The code is self‑contained and can be run directly after installing the dependencies listed in
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`requirements.txt`.
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"""
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DeepAgents RAG Agent with Qdrant and Ollama embeddings
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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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import pathlib
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from typing import List
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from langchain_openai import ChatOpenAI
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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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.embeddings import OllamaEmbeddings
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from langchain_qdrant import QdrantVectorStore
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from langchain_community.vectorstores import Qdrant
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from langchain_community.document_loaders import TextLoader
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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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.agents import create_agent, AgentExecutor, AgentType
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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# Environment variables
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "") # required for OpenRouter LLM
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
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QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
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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 environment variable is required")
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# LLM via OpenRouter
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# LLM and embeddings 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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@@ -31,112 +40,124 @@ llm = ChatOpenAI(
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temperature=0.0,
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)
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# Embeddings via Ollama
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embeddings = OllamaEmbeddings(model="nomic-embed-text", base_url=OLLAMA_BASE_URL)
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# Qdrant vector store
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qdrant_vector_store = QdrantVectorStore(
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client_kwargs={"url": QDRANT_URL},
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collection_name="knowledge_base",
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embeddings=embeddings,
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# Create collection if not exists
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create_collection=True,
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# Use cosine similarity
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distance="cosine",
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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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# ---------------------------------------------------------------------------
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# Vector store setup (Qdrant)
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# ---------------------------------------------------------------------------
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# Qdrant is expected to be running locally on the default port 6333.
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# If you need a different host/port, adjust the `url` parameter.
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vector_store = Qdrant.from_existing_index(
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collection_name="knowledge",
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embeddings=embeddings,
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url="http://localhost:6333",
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)
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# ---------------------------------------------------------------------------
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# Text splitter
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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# ---------------------------------------------------------------------------
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000,
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chunk_overlap=100, # as required by the assignment
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)
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# ---------------------------------------------------------------------------
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# Tools
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# ---------------------------------------------------------------------------
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@tool
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def search_knowledge_base(query: str, max_results: int = 3) -> str:
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"""Semantic search in the knowledge base."""
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docs = qdrant_vector_store.similarity_search(query, k=max_results)
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"""Search the knowledge base for relevant information.
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Parameters
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----------
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query: str
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The search query.
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max_results: int, optional
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Number of top results to return. Defaults to 3.
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"""
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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"{i+1}. {doc.page_content}" for i, doc in enumerate(docs))
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return "\n\n---\n\n".join(doc.page_content for doc in docs)
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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 after chunking."""
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# Split content into chunks
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"""Add content to the knowledge base.
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Parameters
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----------
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content: str
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The raw text to add.
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title: str, optional
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A title for the document. Defaults to "document".
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"""
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# Split into chunks and create Document objects
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chunks = text_splitter.split_text(content)
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metadatas = [{"title": title, "chunk_index": i} for i in range(len(chunks))]
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# Add to Qdrant
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qdrant_vector_store.add_texts(chunks, metadatas=metadatas)
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return f"Added {len(chunks)} chunks from '{title}'."
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from langchain_core.documents import Document
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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 for title '{title}'."
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# ---------------------------------------------------------------------------
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# Backend setup
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# Agent creation (LangChain create_agent)
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# ---------------------------------------------------------------------------
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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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# The system prompt instructs the agent to use the knowledge base tools.
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SYSTEM_PROMPT = (
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"You are an AI assistant with access to a knowledge base. "
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"Use the tools `search_knowledge_base` and `add_to_knowledge_base` to answer user queries. "
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"If the user asks to add information, store it. If the user asks for information, search the base."
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)
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# ---------------------------------------------------------------------------
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# Agent creation
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# ---------------------------------------------------------------------------
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agent = create_deep_agent(
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model=llm,
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agent = create_agent(
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llm=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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system_prompt=SYSTEM_PROMPT,
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agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
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)
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# ---------------------------------------------------------------------------
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# Document loader for initialization
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# ---------------------------------------------------------------------------
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async def load_documents_from_dir(directory: str):
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"""Load all .txt files from a directory into the knowledge base."""
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dir_path = Path(directory)
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for file_path in dir_path.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 {content}")], "metadata": {"title": title}},
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{"configurable": {"thread_id": "init-session"}},
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)
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agent_executor = AgentExecutor(agent=agent, tools=[search_knowledge_base, add_to_knowledge_base])
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# ---------------------------------------------------------------------------
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# CLI client
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# CLI
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# ---------------------------------------------------------------------------
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async def cli():
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print("DeepAgents RAG CLI. Commands: /add <text>, /search <query>, /quit")
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async def handle_user_input(user_input: str) -> str:
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if user_input.startswith("/add "):
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# Expected format: /add <title> | <content>
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try:
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_, rest = user_input.split("/add ", 1)
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title, content = rest.split("|", 1)
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title = title.strip()
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content = content.strip()
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result = add_to_knowledge_base(content, title)
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return result
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except ValueError:
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return "Invalid format. Use: /add <title> | <content>"
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elif user_input.startswith("/search "):
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query = user_input[len("/search "):].strip()
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return search_knowledge_base(query)
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elif user_input == "/quit":
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return "quit"
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else:
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# Forward to the agent
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response = await agent_executor.ainvoke({"messages": [HumanMessage(content=user_input)]})
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return response["messages"][-1].content
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async def main():
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print("RAG Agent CLI. Commands: /add <title> | <content>, /search <query>, /quit")
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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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if not user_input:
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continue
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result = await handle_user_input(user_input)
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if result == "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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content = user_input[5:].strip()
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response = await agent.ainvoke(
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{"messages": [HumanMessage(content=f"/add {content}")], "metadata": {"title": "user_input"}},
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{"configurable": {"thread_id": "cli-session"}},
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)
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print(response["messages"][-1].content)
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elif user_input.startswith("/search "):
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query = user_input[8:].strip()
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response = await agent.ainvoke(
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{"messages": [HumanMessage(content=f"/search {query}")], "metadata": {"query": query}},
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{"configurable": {"thread_id": "cli-session"}},
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)
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print(response["messages"][-1].content)
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else:
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print("Unknown command. Use /add, /search, or /quit.")
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# ---------------------------------------------------------------------------
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# Main entry point
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# ---------------------------------------------------------------------------
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async def main():
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# Optional: load initial documents from a folder named 'data'
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data_dir = Path("./data")
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if data_dir.exists():
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await load_documents_from_dir(str(data_dir))
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await cli()
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print(result)
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if __name__ == "__main__":
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asyncio.run(main())
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