# RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector database and the **OpenAI API** to generate responses based on retrieved documents. It also includes a simple web‑search component that fetches content from specified URLs for indexing.

## Features

- **Vector Store**: Uses ChromaDB to store embeddings of text chunks.
- **OpenAI Integration**: Generates answers using GPT‑3.5‑Turbo.
- **Web Search**: Fetches and parses HTML pages, splits them into manageable chunks.
- **Command Line Interface**: Ask questions interactively.

## Prerequisites

- Node.js v18+ (supports native ES modules and `node-fetch` v2).
- An OpenAI API key.

## Setup

1. **Clone the repository** (or copy the files into a directory).

2. **Install dependencies**

   ```bash
   npm install
   ```

3. **Configure environment**

   Create a `.env` file in the project root (or edit the existing one) and add your OpenAI API key:

   ```dotenv
   OPENAI_API_KEY=your_api_key_here
   ```

4. **Run the agent**

   ```bash
   npm start
   ```

   The script will:
   - Fetch and index the example URLs.
   - Prompt you to enter questions.
   - Display answers generated by the RAG agent.

## Customization

- **Adding URLs**: Edit the `urls` array in `src/index.js` to index different web pages.
- **Chunk Size**: Adjust the `size` parameter in `chunkText` inside `src/webSearch.js` if you need larger or smaller chunks.
- **Model Parameters**: Modify temperature, max tokens, or model name in `src/agent.js`.

## Notes

- The implementation strictly uses **ChromaDB** as the vector database; no other vector DBs are used.
- All dependencies are declared in `package.json` and can be installed via `npm install`.
- The OpenAI API key is loaded securely from the `.env` file using `dotenv`.

## License

MIT License
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Enjoy building with RAG!