# RAG Agent with ChromaDB and Web Search

This project demonstrates a Retrieval-Augmented Generation (RAG) agent built with **LangChain 1.x**, **ChromaDB** as the vector store, and **SerpAPI** for web search integration.

## Features

- Stores documents in ChromaDB and generates embeddings using OpenAI.
- Retrieves relevant documents via a vector store tool.
- Performs live web searches with SerpAPI.
- Combines both sources to answer user queries.

## Prerequisites

- Node.js v18+ (ES modules support)
- A running ChromaDB instance (default: `localhost:8000`)
- OpenAI API key
- SerpAPI key

## Setup

1. **Clone the repository**

   ```bash
   git clone https://github.com/your-username/rag-agent-chromadb-websearch.git
   cd rag-agent-chromadb-websearch
   ```

2. **Install dependencies**

   ```bash
   npm install
   ```

3. **Configure environment variables**

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

   ```dotenv
   OPENAI_API_KEY=your_openai_api_key
   CHROMA_HOST=localhost
   CHROMA_PORT=8000
   SERPAPI_KEY=your_serpapi_key
   ```

4. **Run the agent**

   ```bash
   npm start
   ```

   The agent will add sample documents to ChromaDB, then answer a sample query using both the vector store and web search.

## Project Structure

```
src/
├── index.js        # Entry point
├── agent.js        # Agent construction
├── vectorStore.js  # ChromaDB interactions
└── webSearch.js    # SerpAPI web search
```

## Customization

- **Adding Documents**: Use `addDocuments` from `vectorStore.js` to add your own documents.
- **Changing LLM**: Replace `OpenAI` with another LLM provider supported by LangChain.
- **Adjusting Retrieval**: Modify the number of results returned by the vector store or web search.

## License

MIT License
--- 
Happy coding!