# RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs web search to ingest documents. The agent answers user questions by retrieving relevant passages from the stored documents and generating responses with OpenAI’s GPT models.

## Features

- **ChromaDB** vector store (no Qdrant usage)
- Web content ingestion via HTTP fetch
- OpenAI embeddings for vector representation
- GPT-4o-mini for answer generation
- Simple CLI usage

## Prerequisites

- Node.js 20+ (ESM support)
- Docker (optional, for running ChromaDB locally)
- OpenAI API key

## Setup

1. **Clone the repository**

   ```bash
   git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
   cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
   ```

2. **Install dependencies**

   ```bash
   npm install
   ```

3. **Configure environment variables**

   Create a `.env` file in the project root:

   ```dotenv
   CHROMA_HOST=localhost
   CHROMA_PORT=8000
   OPENAI_API_KEY=YOUR_OPENAI_API_KEY
   ```

4. **Run ChromaDB**

   The simplest way is to use Docker:

   ```bash
   docker run -d --name chromadb -p 8000:8000 chromadb/chroma
   ```

   Or install ChromaDB locally following the official docs.

5. **Run the agent**

   ```bash
   npm start
   ```

   The script will ingest a sample document from GitHub and answer a question about the OpenAI Node.js library.

## Project Structure

```
src/
├── agent.js        # RAG agent logic
├── index.js        # Entry point
├── vectorStore.js  # ChromaDB wrapper
└── webSearch.js    # Simple web fetch helper
```

## Notes

- The project **does not** use Qdrant. All references to Qdrant have been removed.
- Only ChromaDB is used for vector storage.
- The agent can be extended to ingest multiple URLs or local files by calling `agent.ingestFromUrl(url)`.

## License

MIT License
--- 
Feel free to contribute or open issues for enhancements.