Updated main.py with Qdrant-based RAG implementation.
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@@ -1,16 +1,23 @@
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# 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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# Весь код написан в одном файле для простоты демонстрации.
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#
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# Требования:
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# - Python 3.10+
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# - Qdrant (работает по умолчанию на localhost:6333)
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# - Ollama (llama3 + nomic-embed-text)
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# - LangChain 1.2.10+ и связанные пакеты
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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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# 1. Убедитесь, что Qdrant и Ollama запущены.
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# 2. pip install -r requirements.txt
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# 3. python main.py
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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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# После запуска появится интерактивный клиент с командами:
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# /add <path_to_file> – добавить документ в базу знаний.
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# /search <query> – выполнить поиск.
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# /quit – выйти.
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# Любой другой ввод – агент обработает как обычный запрос.
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import os
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import sys
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@@ -22,14 +29,14 @@ 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 langchain.agents import create_agent, AgentExecutor, AgentToolkit, Tool
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from langchain.agents import create_agent, AgentExecutor, AgentType
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from langchain_core.messages import HumanMessage
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# ---------------------------------------------------------------------------
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# Конфигурация
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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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QDRANT_URL = "http://localhost:6333"
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COLLECTION_NAME = "knowledge"
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EMBEDDING_MODEL = "nomic-embed-text"
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LLM_MODEL = "llama3"
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@@ -40,141 +47,152 @@ LLM_MODEL = "llama3"
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embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
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vector_store = QdrantVectorStore(
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url=QDRANT_URL,
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collection_name=QDRANT_COLLECTION,
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collection_name=COLLECTION_NAME,
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embedding=embeddings,
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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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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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# ---------------------------------------------------------------------------
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# Инструменты
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# ---------------------------------------------------------------------------
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@tool("search_knowledge_base", "Semantic search in the knowledge base.")
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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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"""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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"""Возвращает топ‑N релевантных фрагментов из Qdrant."""
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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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# Формируем читаемый ответ
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formatted = []
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for doc, score in results:
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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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for i, (doc, score) in enumerate(results, 1):
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formatted.append(f"{i}. (score={score:.3f})\n{doc.page_content[:500]}\n---")
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return "\n".join(formatted)
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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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"""Chunk the content, embed, and store in Qdrant.
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Returns a confirmation message.
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"""
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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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"""Разбивает документ на чанки, эмбеддит и сохраняет в Qdrant."""
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# Разбиваем на чанки
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chunks = text_splitter.split_text(content)
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docs = []
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for i, chunk in enumerate(chunks):
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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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# Создаём Document объекты с метаданными
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from langchain.schema import Document
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docs = [Document(page_content=chunk, metadata={"title": title, "source": title}) for chunk in chunks]
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# Добавляем в хранилище
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vector_store.add_documents(docs)
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return f"Added {len(chunks)} chunks of '{title}' to the knowledge base."
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return f"Added {len(docs)} chunks from '{title}'."
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# ---------------------------------------------------------------------------
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# Агент
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# ---------------------------------------------------------------------------
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# Системный промпт, который подсказывает агенту использовать инструменты
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SYSTEM_PROMPT = textwrap.dedent("""
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You are an intelligent assistant with access to a knowledge base.
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Use the following tools when you need to retrieve or store information:
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- search_knowledge_base: Perform a semantic search in the knowledge base.
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- add_to_knowledge_base: Add new content to the knowledge base.
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When you answer a user query, first decide if you need to search the base.
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If you do, call search_knowledge_base with a relevant query.
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If you need to add new information, call add_to_knowledge_base.
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Otherwise, answer directly using your internal knowledge.
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""")
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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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tools=[search_knowledge_base, add_to_knowledge_base],
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system_message=SYSTEM_PROMPT,
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agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
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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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# Обёртка для удобного вызова
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agent_executor = AgentExecutor(agent=agent, tools=[search_knowledge_base, add_to_knowledge_base], verbose=True)
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# ---------------------------------------------------------------------------
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# Загрузка документов из директории
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# Загрузка документов из директории (инициализация)
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# ---------------------------------------------------------------------------
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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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"""Загружает все .txt и .md файлы из указанной папки в базу."""
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p = Path(directory)
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if not p.is_dir():
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print(f"Directory {directory} does not exist.")
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return
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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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for file_path in p.rglob("*.txt") | p.rglob("*.md"):
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try:
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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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print(f"Loaded {file_path}")
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except Exception as e:
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print(f"Failed to load {file_path}: {e}")
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# ---------------------------------------------------------------------------
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# CLI
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# Интерактивный клиент
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# ---------------------------------------------------------------------------
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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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def interactive_client():
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print("Welcome to the RAG agent. Type /help for commands.")
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while True:
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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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if not user_input:
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continue
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if user_input.startswith("/quit"):
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if user_input.startswith("/"):
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# Команды
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if user_input.startswith("/add "):
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path = user_input[5:].strip()
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if not path:
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print("Usage: /add <path_to_file>")
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continue
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try:
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content = Path(path).read_text(encoding="utf-8")
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title = Path(path).stem
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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 reading file: {e}")
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elif user_input.startswith("/search "):
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query = user_input[8:].strip()
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if not query:
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print("Usage: /search <query>")
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continue
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result = search_knowledge_base(query)
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print(result)
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elif user_input in {"/quit", "/exit"}:
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print("Goodbye!")
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break
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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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elif user_input == "/help":
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print(textwrap.dedent("""
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Commands:
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/add <path> – add a document to the knowledge base
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/search <q> – search the knowledge base
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/quit /exit – exit the program
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/help – show this help
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Any other input is treated as a normal user query for the agent.
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"""))
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else:
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print("Unknown command. Type /help for list of commands.")
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else:
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# Передаём запрос агенту
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try:
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response = agent_executor.invoke({"input": user_input})
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print(response.get("output", ""))
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print(response["output"])
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except Exception as e:
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print(f"Agent error: {e}")
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# ---------------------------------------------------------------------------
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# Точка входа
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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main()
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# Если передан аргумент – загрузить документы из указанной папки
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if len(sys.argv) > 1:
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load_documents_from_dir(sys.argv[1])
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interactive_client()
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