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RAG Chatbot Starter

Vercel Deploy

This project serves as a starter template for creating Retrieval-Augmented Generation (RAG) chatbots using NeonDB and OpenAI's GPT models. Built with Nuxt 3 and Tailwind CSS, it provides a solid foundation for developing intelligent, context-aware chatbot applications with a modern web interface.

Live Demo: RAG Chatbot Starter alt text

Features

  • RAG Architecture: Implements a Retrieval-Augmented Generation system for more accurate and context-aware responses.
  • Converstation History: Stores chat history for enhanced context and continuity.
  • Neon Postgres Integration: Uses Neon Postgres as a vector database for efficient storage and retrieval of embeddings.
  • OpenAI Integration: Leverages OpenAI's powerful language models for generating responses.
  • Nuxt 3 Framework: Built on Nuxt 3, offering a modern, performant foundation for web applications.
  • Tailwind CSS: Utilizes Tailwind CSS for rapid UI development and easy customization.
  • Modular Design: Structured for easy understanding and extension of core functionalities.
  • Serverless-Ready: Prepared for deployment on serverless platforms like Cloudflare Workers.

Architecture Overview

  1. Frontend: Nuxt 3 application with Tailwind CSS for styling.
  2. Backend: Nuxt server routes for handling API requests.
  3. Vector Database: Neon Postgres with pgvector for storing and querying document embeddings.
  4. Language Model: OpenAI's GPT model for generating responses.
  5. RAG Pipeline:
    • Query embedding
    • Vector similarity search
    • Context retrieval
    • Response generation

Getting Started

Prerequisites

  • Node.js (v18+)
  • pnpm
  • Neon account with a pgvector-enabled database
  • OpenAI account with API access
  • (Optional) Vercel, Netlify, or Cloudflare Workers account for deployment

Setup for Local Development

  1. Clone the repository:

    https://github.com/sreeharsha-rav/ai-apps/tree/main/nuxt-rag-chat
cd nuxt-rag-chat

  2. Install dependencies:

    pnpm install

  3. Set up environment variables: Create a .env file in the project root:

    OPENAI_API_KEY=your_openai_api_key
NEON_CONNECTION_STRING=your_neon_connection_string

  4. Prepare your vector database:

  5. Start the development server:

    pnpm run dev

    Access the application at http://localhost:3000

Customization Guide

  1. Modify the UI: Edit Vue components in the components/ directory.

  2. Adjust RAG Logic: Modify the retrieval and generation process in server/utils/aiSingleton.ts.

  3. Prompt Engineering: Update the prompts and responses in server/utils/prompts.ts.

  4. Change Language Model: Update the OpenAI model or switch to a different provider in server/utils/aiSingleton.ts.

  5. Extend Vector Store: Modify or replace the Neon Postgres integration in server/utils/aiSingleton.ts.

  6. Add New Features: Implement additional server routes in server/api/ and corresponding client-side logic.

Deployment

Build for Production

pnpm run build

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  • Possible Improvements:
    • UI enhancements - Move suggestions to bottom, make it random, etc.
    • Better error handling and edge case client-server interactions
    • Add authentication
    • Enhance the RAG pipeline with better retrieval strategies
    • Better handling of conversation history and context with user sessions
    • Option to upload and store custom documents for retrieval

License

This project is open source and available under the MIT License.