Updated main.py with Ollama and LangChain create_agent
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@@ -1,50 +1,47 @@
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# 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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# Полностью рабочий пример агента с RAG‑памятью на базе Qdrant и Ollama.
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# Используется LangChain 1.x, create_agent, инструменты @tool, и Ollama‑LLM/embeddings.
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#
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# Запуск:
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# python main.py
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# После запуска можно использовать команды:
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# /add <title> <content> – добавить документ
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# /search <query> <max> – семантический поиск
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# /quit – выйти
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#
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# Для загрузки документов из директории используйте функцию load_documents_from_dir.
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#"""
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import os
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import asyncio
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import sys
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import textwrap
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from pathlib import Path
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from typing import List
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from typing import List, Dict, Any
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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_ollama import ChatOllama, OllamaEmbeddings
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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.agents import create_agent, AgentExecutor, AgentToolkit, Tool
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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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# Конфигурация
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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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QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
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QDRANT_COLLECTION = "knowledge_base"
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EMBEDDING_MODEL = "nomic-embed-text"
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LLM_MODEL = "llama3"
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# ---------------------------------------------------------------------------
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# Qdrant клиент и коллекция
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# Векторное хранилище
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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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# Инициализируем эмбеддер и клиент Qdrant
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embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
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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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url=QDRANT_URL,
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collection_name=QDRANT_COLLECTION,
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embedding=embeddings,
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)
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# ---------------------------------------------------------------------------
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@@ -53,117 +50,131 @@ vector_store = QdrantVectorStore(
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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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# Инструменты
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# ---------------------------------------------------------------------------
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@tool
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@tool("search_knowledge_base", "Semantic search in the knowledge base.")
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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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"""Return top‑k relevant documents for a query.
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The function returns a formatted string with titles and snippets.
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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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title = doc.metadata.get("title", "Untitled")
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snippet = doc.page_content[:200].replace("\n", " ")
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formatted.append(f"{title} (score: {score:.3f}): {snippet}...")
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return "\n".join(formatted)
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@tool
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@tool("add_to_knowledge_base", "Add a document to the knowledge base.")
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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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"""Chunk the content, embed, and store in Qdrant.
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Returns a confirmation message.
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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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docs = []
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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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docs.append(
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{
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"page_content": chunk,
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"metadata": {"title": title, "chunk_index": i},
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}
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)
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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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return f"Added {len(chunks)} chunks of '{title}' to the knowledge base."
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# ---------------------------------------------------------------------------
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# Backend для deepagents
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# Агент
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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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# Создаём LLM
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llm = ChatOllama(model=LLM_MODEL, temperature=0.2)
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# Список инструментов
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tools = [search_knowledge_base, add_to_knowledge_base]
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# Создаём агент
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agent = create_agent(
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llm=llm,
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tools=tools,
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system_message="You are an assistant that can search and add documents to a local knowledge base. Use the provided tools.",
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verbose=True,
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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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# Обёртка для выполнения
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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# ---------------------------------------------------------------------------
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# Инициализация: загрузка документов из директории
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# 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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def load_documents_from_dir(directory: str) -> None:
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"""Load all .txt files from a directory into the knowledge base.
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Each file becomes a separate document with its filename as title.
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"""
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path = Path(directory)
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if not path.is_dir():
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print(f"Directory {directory} does not exist.")
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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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for file in path.glob("*.txt"):
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title = file.stem
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content = file.read_text(encoding="utf-8")
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print(f"Adding {title}...", end=" ")
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result = add_to_knowledge_base(content, title)
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print(result)
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# ---------------------------------------------------------------------------
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# Интерактивный клиент
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# CLI
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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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def main():
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# Если пользователь передал путь к директории, загрузим документы
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if len(sys.argv) > 1:
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load_documents_from_dir(sys.argv[1])
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print("\n--- RAG Agent CLI ---")
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print("Commands:")
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print(" /add <title> <content> – add a document")
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print(" /search <query> <max> – search knowledge base")
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print(" /quit – exit")
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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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try:
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user_input = input("\n> ")
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except (EOFError, KeyboardInterrupt):
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print("\nExiting.")
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break
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if not user_input.strip():
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continue
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if user_input.startswith("/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 user_input.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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continue
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title, content = parts[1], parts[2]
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print(add_to_knowledge_base(content, title))
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continue
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if user_input.startswith("/search"):
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parts = user_input.split(maxsplit=2)
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if len(parts) < 2:
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print("Usage: /search <query> [max_results]")
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continue
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query = parts[1]
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max_results = int(parts[2]) if len(parts) > 2 else 5
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print(search_knowledge_base(query, max_results))
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continue
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# Любой другой ввод – передаём агенту
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response = agent_executor.invoke({"input": user_input})
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print(response.get("output", ""))
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if __name__ == "__main__":
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asyncio.run(main())
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main()
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