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# Агент с RAG‑памятью # RAG Agent with Qdrant and Ollama
## Описание This repository contains a minimal but fullyfunctional example of an AI agent that uses **Qdrant** as a local vector store, **Ollama** for embeddings and a local LLM, and **LangChain** for the agent logic.
Простой пример AI‑агента, использующего локальный RAG‑хранилище на базе **Qdrant** и **Ollama**. Агент умеет: ## Features
1. Добавлять документы в векторную базу. * **Semantic search** `search_knowledge_base` tool queries the vector store.
2. Выполнять семантический поиск по базе. * **Document ingestion** `add_to_knowledge_base` tool splits text into chunks and stores them.
3. Собирать ответы из найденных документов. * **Interactive CLI** simple command line interface for adding documents, searching and chatting with the agent.
* **Modular design** vector store, tools and agent logic are separated into distinct modules.
## Стек ## Setup
* Python 3.10+
* Qdrant – векторная база данных
* Ollama – локальные LLM и эмбеддинги (`llama3`, `nomic-embed-text`)
* LangChain 1.x – фреймворк для агентов и RAG
## Установка
```bash ```bash
# Ollama # 1. Install Ollama models
ollama pull llama3 ollama pull llama3
ollama pull nomic-embed-text ollama pull nomic-embed-text
# Python пакеты # 2. Install Python dependencies
pip install -r requirements.txt pip install -r requirements.txt
# 3. Start Qdrant (Docker recommended)
# docker run -p 6333:6333 qdrant/qdrant
``` ```
## Запуск ## Usage
```bash ```bash
python agent.py "Какой последний прогресс в области ИИ?" python -m workspace.task-6a02e23da6fe2e4ac16acf65.cli
``` ```
## Структура проекта The CLI accepts the following commands:
* `/add <title>` add a new document. After the title you will be prompted to paste the content; finish with a line containing only `END`.
* `/search <query>` perform a semantic search.
* `/quit` exit.
Anything else is forwarded to the agent.
## Project structure
``` ```
├── agent.py # основной скрипт агента workspace/
├── qdrant_store.py # обёртка над QdrantVectorStore ├── task-6a02e23da6fe2e4ac16acf65/
├── chunker.py # разбиение документов на чанки │ ├── agent.py # Agent and tool definitions
├── tools.py # инструменты для агента │ ├── cli.py # Interactive command line interface
├── requirements.txt │ ├── vector_store.py # Qdrant + Ollama wrapper
└── README.md │ ├── requirements.txt
│ └── README.md
``` ```