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-# task-6a02e23da6fe2e4ac16acf65
+# RAG Agent with Qdrant and Ollama
 
-Агент с RAG-памятью
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+This repository contains a simple RAG (Retrieval‑Augmented Generation) agent built with **LangChain**, **Qdrant** as the vector store, and **Ollama** for embeddings and LLMs.
+
+## Features
+
+* Semantic search in a local vector database.
+* Add new documents to the knowledge base.
+* Recursive text splitting for chunking.
+* Interactive CLI to query the agent.
+
+## Setup
+
+```bash
+# Pull required models
+ollama pull llama3
+ollama pull nomic-embed-text
+
+# Install Python dependencies
+pip install -r requirements.txt
+```
+
+## Usage
+
+```bash
+# Load documents from the knowledge folder (default: ./knowledge)
+python agent.py
+```
+
+You can type any question. The agent will automatically search the knowledge base and answer.
+
+## Adding Documents
+
+Place any `.txt` files in the `knowledge` directory before running the agent, or use the `add_to_knowledge_base` tool via the agent.
+
+## Project Structure
+
+- `agent.py` – Main entry point.
+- `qdrant_store.py` – Wrapper around Qdrant for adding/searching.
+- `rag_tools.py` – LangChain tools for the agent.
+- `requirements.txt` – Python dependencies.
+- `README.md` – Documentation.