# RAG Agent with ChromaDB and Web Search

This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs web search as a fallback. The agent is written in Node.js and uses only the required dependencies.

## Features

- **Vector storage** with ChromaDB (in-memory by default).
- **Simple embedding** function (placeholder) – replace with a real model for production.
- **Web search** using DuckDuckGo’s HTML interface.
- **RAG agent** that retrieves relevant documents or falls back to web search.

## Installation

```bash
npm install
```

## Usage

```bash
node src/index.js "Your query here"
```

If no query is provided, it defaults to `"What is ChromaDB?"`.

## Running Tests

```bash
npm test
```

## Project Structure

```
src/
  index.js        # Entry point
  agent.js        # RAG agent logic
  vectorStore.js  # ChromaDB wrapper
  webSearch.js    # Simple web search helper
  test.js         # Basic test for vector store
```

## Extending

- Replace the `embed` function in `vectorStore.js` with a real embedding model (e.g., OpenAI, HuggingFace).
- Persist the ChromaDB collection by configuring the client with a storage path.
- Add a language model to generate responses from retrieved documents.

## License

MIT