# RAG‑Agent with ChromaDB and Tavily

## What is it?

A small demo of a LangChain agent that can answer questions using either a local knowledge base stored in **ChromaDB** or live web search via **Tavily**. The agent automatically chooses the source based on the query and returns the answer together with a source label.

## Prerequisites

* Python 3.10+
* Ollama (downloaded models below)

```bash
ollama pull llama3
ollama pull nomic-embed-text
```

## Installation

```bash
pip install -r requirements.txt
```

Create a `.env` file in the project root with your Tavily API key:

```
TAVILY_API_KEY=your_key_here
```

## Data

Place any `.txt` or `.md` files you want to index into the `documents/` folder.

## Usage

```bash
python main.py
```

You will see a prompt. Type your question and press Enter. Type `exit` to quit.

The output shows the answer and the source (`chromadb` or `tavily`).