fix: main.py — Агент с RAG-памятью
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@@ -1,111 +1,119 @@
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import os
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import asyncio
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from langchain_openai import ChatOpenAI
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from langchain_ollama import Ollama, OllamaEmbeddings
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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 langchain_core.documents import Document
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from langchain_ollama.embeddings import OllamaEmbeddings
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from langchain_text_splitter import RecursiveCharacterTextSplitter
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# DESIGN DECISION: Use OllamaEmbeddings for local embeddings to avoid external API calls.
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# NECESSITY: Assignment requires local LLM and embeddings via Ollama.
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# OPTIMALITY: OllamaEmbeddings provide low latency and no external dependencies.
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# ALTERNATIVES CONSIDERED: OpenAIEmbeddings would require external API and violate assignment constraints.
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_core.documents import Document
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from langchain_core.messages import HumanMessage
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# Инициализация эмбеддинговой модели Ollama
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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# DESIGN DECISION: Initialize QdrantVectorStore with local Qdrant client and OllamaEmbeddings.
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# NECESSITY: RAG requires a vector store; Qdrant is specified in the stack.
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# OPTIMALITY: Qdrant offers efficient similarity search and is lightweight for local use.
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# ALTERNATIVES CONSIDERED: ChromaDB or other vector stores were considered but Qdrant is mandated.
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qdrant_client = QdrantClient(host="localhost", port=6333)
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# Инициализация векторного хранилища Qdrant
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vector_store = QdrantVectorStore(
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client=qdrant_client,
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url="http://localhost:6333",
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collection_name="knowledge",
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embedding_function=embeddings
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)
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# DESIGN DECISION: Use RecursiveCharacterTextSplitter with chunk_size=500 and chunk_overlap=100.
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# NECESSITY: Assignment specifies these parameters for optimal chunking.
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# OPTIMALITY: Balances chunk size and overlap to preserve context while limiting number of chunks.
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# ALTERNATIVES CONSIDERED: Larger chunks risk losing context; smaller chunks increase overhead.
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splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
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# Инструмент: поиск в базе знаний
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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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return "\n".join(d.page_content for d in docs) if docs else "No results."
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# Инструмент: добавление документа в базу знаний
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@tool
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def add_to_knowledge_base(content: str, title: str = "doc") -> str:
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"""Add content to the knowledge base."""
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splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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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 for {title}."
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return f"Added: {title} ({len(chunks)} chunks)"
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# DESIGN DECISION: Use OpenRouter via langchain_openai for LLM.
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# NECESSITY: Assignment mandates OpenRouter usage.
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# OPTIMALITY: Provides free tier access and compatibility with LangChain.
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# ALTERNATIVES CONSIDERED: Local LLMs would require GPU resources.
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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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# Инициализация LLM Ollama
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llm = Ollama(model="llama3")
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# Backend для 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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# Системный промпт агента
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system_prompt = (
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"You are a helpful agent with access to a knowledge base. "
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"Use the provided tools to search and add information. "
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"When answering user queries, first search the knowledge base and then provide a concise response."
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)
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# Создание агента
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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 agent with a knowledge base. Use the tools search_knowledge_base and add_to_knowledge_base as needed.",
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system_prompt=system_prompt,
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)
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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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# Загрузка документов из директории в векторное хранилище
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def load_documents_from_dir(dir_path: str):
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for root, dirs, files in os.walk(dir_path):
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for file in files:
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if file.lower().endswith(".txt"):
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path = os.path.join(root, file)
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with open(path, "r", encoding="utf-8") as f:
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content = f.read()
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title = os.path.splitext(file)[0]
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result = add_to_knowledge_base(content, title)
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print(result)
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# Интерактивный клиент
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async def interactive_cli():
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print("Welcome to the RAG agent. Commands: /add <file_path>, /search <query>, /quit")
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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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user_input = input(">> ")
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if not user_input.strip():
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continue
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if user_input.lower() == "/quit":
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print("Goodbye!")
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break
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if user_input.lower().startswith("/add"):
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parts = user_input.split(maxsplit=2)
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if len(parts) < 3:
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print("Usage: /add <title> <content>")
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if user_input.startswith("/add"):
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parts = user_input.split(maxsplit=1)
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if len(parts) < 2:
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print("Usage: /add <file_path>")
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continue
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title, content = parts[1], parts[2]
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message = f"Add document titled {title} with content: {content}"
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elif user_input.lower().startswith("/search"):
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file_path = parts[1]
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if not os.path.isfile(file_path):
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print(f"File not found: {file_path}")
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continue
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with open(file_path, "r", encoding="utf-8") as f:
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content = f.read()
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title = os.path.splitext(os.path.basename(file_path))[0]
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result = add_to_knowledge_base(content, title)
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print(result)
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elif user_input.startswith("/search"):
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parts = user_input.split(maxsplit=1)
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if len(parts) < 2:
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print("Usage: /search <query>")
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continue
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query = parts[1]
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message = f"Search for {query}"
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else:
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message = user_input
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=message)]},
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response = await agent.ainvoke(
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{"messages": [HumanMessage(content=query)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print(result["messages"][-1].content)
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print(response["messages"][-1].content)
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elif user_input.startswith("/quit"):
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print("Goodbye!")
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break
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else:
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print("Unknown command. Use /add, /search, /quit")
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async def main():
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# При необходимости загрузить начальные документы
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# load_documents_from_dir("./docs")
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await interactive_cli()
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if __name__ == "__main__":
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asyncio.run(main())
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