# RAG Agent with ChromaDB and Web Search

This project demonstrates a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and similarity search, and performs web search using DuckDuckGo.

## Features

- **Vector Store**: Stores embeddings in a local ChromaDB collection.
- **RAG Agent**: Retrieves relevant documents and constructs an answer.
- **Web Search**: Fetches top results from DuckDuckGo.

## Setup

```bash
# Clone the repository
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

# Install dependencies
npm install

# Run the example
npm start
```

## Running Tests

```bash
npm test
```

## Configuration

The project uses a local ChromaDB instance by default. If you need to connect to a remote instance, set the following environment variables in a `.env` file:

```dotenv
CHROMA_HOST=localhost
CHROMA_PORT=8000
```

## Project Structure

```
src/
  index.js        # Entry point
  agent.js        # RAG agent logic
  vectorStore.js  # ChromaDB wrapper
  search.js       # Web search helper
  utils.js        # Embedding helper
tests/
  vectorStore.test.js
  agent.test.js
```

## Notes

- The embedding function in `utils.js` is a deterministic placeholder. Replace it with a real embedding model (e.g., OpenAI embeddings) for production use.
- The agent currently returns concatenated context as the answer. Integrate a language model for richer responses.

## License

MIT License