# RAG Agent with Qdrant and Ollama

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.