# RAG Agent with Local Qdrant and Ollama

This repository implements a simple AI agent that can store, search, and retrieve information from a local vector store using Qdrant and Ollama embeddings. The agent is built with LangChain v1 and supports an interactive CLI with the following commands:

* `/add` – add a new document to the knowledge base.
* `/search` – perform a semantic search in the knowledge base.
* `/quit` – exit the program.

## Features

* **RAG** – Retrieval-Augmented Generation using a local vector store.
* **Qdrant** – Vector similarity search engine.
* **Ollama** – Local LLM (`llama3`) and embeddings (`nomic-embed-text`).
* **LangChain v1** – Modern agent framework.
* **Recursive text splitter** – Chunk documents before embedding.

## Setup

```bash
# Install Ollama models
ollama pull llama3
ollama pull nomic-embed-text

# Install Python dependencies
pip install -r requirements.txt
```

## Usage

```bash
# Load documents from the `docs` folder and start the CLI
python -m src.cli --docs docs
```

You can then interact with the agent using the commands described above.