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

npm install

Usage

node src/index.js "Your query here"

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

Running Tests

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