# RAG Agent with Qdrant and Ollama

## Overview
This repository contains a minimal implementation of an AI agent that uses **RAG (Retrieval‑Augmented Generation)** with a local vector store powered by **Qdrant** and embeddings from **Ollama**. The agent can:

1. Add documents to the knowledge base.
2. Search the knowledge base semantically.
3. Answer arbitrary user queries using the stored information.

The project is structured into three main files:

- `main.py` – entry point with an interactive CLI and examples.
- `tools.py` – LangChain tools for adding/searching documents.
- `requirements.txt` – Python dependencies.

## Installation
```bash
# Pull required Ollama models (run once)
ollama pull llama3
ollama pull nomic-embed-text

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

## Usage
Run the interactive client:
```bash
python main.py
```
You can use the following commands:

- `/add` – add a new document.
- `/search <query>` – perform a semantic search.
- `/quit` – exit.
- Any other text is treated as a question for the agent.

## Architecture
The agent uses LangChain’s `create_agent` with two custom tools:

1. **add_to_knowledge_base** – splits input into chunks, embeds them via Ollama, and stores in Qdrant.
2. **search_knowledge_base** – performs a similarity search on the vector store.

The LLM is an Ollama `llama3` model accessed through LangChain’s `ChatOllama`. The embeddings are provided by `OllamaEmbeddings` with the `nomic-embed-text` model.

## Extending
Feel free to add more tools or integrate a persistent Qdrant instance instead of an in‑memory one. The code is intentionally simple for educational purposes.