PDFGenie is a Retrieval-Augmented Generation (RAG) chatbot that can answer questions from your PDF documents using vector embeddings and AI chat models. Powered by Gemini API for embeddings and GROQ API for knowledge retrieval.

• Upload PDFs and generate vector embeddings for fast semantic search.
• Ask questions and get answers from PDF content using AI-powered chat.
• Seamlessly integrates Pinecone vector database for efficient search.
• Easy configuration using environment variables.

• Node.js / Express.js – Backend server
• Gemini API – Generate vector embeddings
• GROQ API – Fetch content for chat
• Pinecone – Vector database for fast search
• dotenv – Manage environment variables

1. Clone the repository:

   git clone https://github.com/ankitkumarbarik/PDFGenie.git
   cd PDFGenie

2. Install dependencies:

   npm install

3. Create .env file with the following variables:

   PORT=5000
   GROQ_API_KEY=your_groq_api_key
   DEBUG=false
   GEMINI_API_KEY=your_gemini_api_key
   PINECONE_API_KEY=your_pinecone_api_key
   PINECONE_ENVIRONMENT=your_pinecone_env
   PINECONE_INDEX_NAME=your_index_name

4. Run the server:

   npm start

5. Access your chatbot: Open your browser and go to: http://localhost:5000

1. Upload your PDF(s) through the provided interface or API.
2. PDFGenie will create embeddings and store them in Pinecone.
3. Ask your questions — PDFGenie will fetch relevant content and generate AI responses.

Contributions are welcome! Feel free to submit issues or pull requests.