RAG App

This project is a Retrieval-Augmented Generation (RAG) application that utilizes the Google AI Studio API for language model and embedding functionalities. It integrates with a Milvus managed instance for vector storage and uses MongoDB for user notes management.

Features

• User authentication with login and signup functionality.
• Chat interface for querying the RAG system.
• Notes management with the ability to create, view, and paginate notes.
• Semantic search capabilities to enhance user queries using embeddings.

Project Structure

rag-app
├── .env.local
├── .gitignore
├── README.md
├── components
│   ├── Auth
│   ├── Chat
│   ├── Layout
│   ├── Notes
│   └── UI
├── lib
├── models
├── next.config.js
├── package.json
├── pages
│   ├── _app.js
│   ├── _document.js
│   ├── account.js
│   ├── api
│   ├── chat.js
│   ├── index.js
│   ├── login.js
│   ├── notes
│   └── signup.js
├── public
│   └── favicon.ico
└── styles
    └── globals.css

Installation

1. Clone the repository:

   git clone <repository-url>
   cd rag-app
   

2. Install dependencies:

   npm install
   

3. Create a .env.local file in the root directory and add your environment variables:

   GOOGLE_API_KEY=your_google_api_key
   MILVUS_CONNECTION_STRING=your_milvus_connection_string
   MONGODB_URI=your_mongodb_uri
   

4. Run the development server:

   npm run dev
   

5. Open your browser and navigate to http://localhost:3000.

Usage

• Navigate to /signup to create a new account.
• Use /login to access your account.
• The main chat interface is available at /chat.
• View and manage your notes at /notes.

Technologies Used

• Next.js
• MongoDB with Mongoose
• Milvus for vector storage
• Google AI Studio API for embeddings and language model

License

This project is licensed under the MIT License.