73 lines
1.8 KiB
Markdown
73 lines
1.8 KiB
Markdown
# 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! |