feat: solution for 'Практическое задание: Агент с RAG-памятью'

README.md

@@ -1,93 +1,26 @@
-```markdown
-# RAG Agent with Qdrant and Ollama
+# Практическое задание: Агент с RAG-памятью
 
-This project implements an AI agent that can search and add documents to a local knowledge base using **Qdrant** for vector storage and **Ollama** for embeddings and LLM inference. The agent is built with **LangChain** and exposes two tools:
+Главная
+Мои задания
+Агент с RAG-памятью
+5Д
+EN
+Агент с RAG-памятью
 
-- `search_knowledge_base(query, max_results)` – semantic search in the knowledge base.
-- `add_to_knowledge_base(content, title)` – add a new document to the knowledge base.
+Практическое задание: Агент с RAG-памятью
+Цель
 
-## Features
+Построить AI-агента с локальным RAG-хранилищем знаний на базе Qdrant и Ollama. Агент должен уметь искать и сохранять информацию в векторной базе.
 
-- **Vector store**: Qdrant with Ollama embeddings (`nomic-embed-text`).
-- **Chunking**: Recursive character splitter with overlap.
-- **Agent**: Zero-shot React agent that uses the two tools.
-- **CLI**: Interactive command line interface to add documents and query the agent.
-- **Batch loading**: Script to load all text files from a directory into the knowledge base.
+Стек
+Python 3.10+
+Qdrant — векторная база данных
+Ollama — локальные LLM и эмбеддинги (llama3, nomic-embed-text)
+LangChain — фреймворк для агентов и RAG
+Установка
+# Ollama
+ollama pull llama3
+ollama pull nomic-embed-text
 
-## Prerequisites
-
-- Python 3.10+
-- Docker (for Qdrant) or a running Qdrant instance.
-- Ollama installed locally with the following models:
-  ```bash
-  ollama pull llama3
-  ollama pull nomic-embed-text
-  ```
-
-## Setup
-
-```bash
-# Clone the repository
-git clone https://github.com/your-username/rag-agent.git
-cd rag-agent
-
-# Create a virtual environment
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Start Qdrant (Docker example)
-docker run -p 6333:6333 qdrant/qdrant
-```
-
-## Usage
-
-### 1. Load documents into the knowledge base
-
-```bash
-python src/main.py /path/to/documents
-```
-
-Supported file types: `.txt`, `.md`. (PDF support can be added with an additional parser.)
-
-### 2. Start the interactive CLI
-
-```bash
-python src/cli.py
-```
-
-Commands:
-
-- `/add <file_path>` – Add a single document.
-- `/search <query>` – Query the agent.
-- `/quit` – Exit.
-
-### 3. Example
-
-```bash
-> /add example.txt
-Document 'example' added to knowledge base with 3 chunks.
-> /search What is the capital of France?
-1. The capital of France is Paris. (Title: example)
-```
-
-## Project Structure
-
-```
-rag-agent/
-├── src/
-│   ├── agent.py
-│   ├── cli.py
-│   ├── main.py
-│   ├── tools.py
-│   └── vector_store.py
-├── requirements.txt
-└── README.md
-```
-
-## License
-
-MIT License
-```
+# Python пакеты
+pi