NotebookLite: RAG Application with Next.js

NotebookLite is a lightweight Retrieval-Augmented Generation (RAG) application built with Next.js. It allows users to interact with their data through a chat interface, supporting text, PDF, and URL inputs. The application utilizes Qdrant for vector storage and Google Generative AI embeddings for semantic understanding.

Visit :

https://nextjs-rag-application-note-book-li.vercel.app/

Features

-Multi-input Support: Accepts text, PDF, and URL inputs for data processing. -Semantic Search: Utilizes Google Generative AI embeddings for understanding and retrieving relevant information. -Chat Interface: Engages users in a conversational manner, providing responses based on the provided data. -Persistent Storage: Stores user messages locally for a seamless chat experience.

Technologies Used

• Frontend: Next.js, React, Tailwind CSS
• Backend: Node.js, Qdrant, Google Generative AI
• Libraries: Langchain, Axios

Installation

1. Clone the repository:

   git clone https://github.com/bhushan-ai/nextjs-Rag-application-NoteBookLite.git
   cd nextjs-Rag-application-NoteBookLite
   

2. Install dependencies:

   npm install

3. Set up environment variables:

   Create a .env.local file in the root directory and add the following:

   GEMINI_API_KEY=your_google_genai_api_key
   QDRANT_CLUSTER_URL=your_qdrant_cluster_url
   CLUSTER_API_KEY=your_qdrant_api_key
   
   

4. Run the development server:

   npm run dev
   

Usage

• Text Input: Enter your data directly into the text area and submit.
• PDF Input: Upload a PDF file containing your data.
• URL Input: Provide a URL pointing to the data you wish to use.

The application will process the input and provide relevant responses based on the provided data.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.