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

Clone the repository

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

Install dependencies
```
npm install
```

Configure environment variables

Create a .env file in the project root:

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

Run the agent
```
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!

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