PDF RAG (Retrieval-Augmented Generation) Application

1. Upload PDF files
2. Process and index the contents using embeddings stored in a Qdrant vector database
3. Chat with your PDF content using Google's Gemini AI

Getting Started

1. Clone the repository

git clone <repository-url>
cd pdf_rag

2. Install dependencies

npm install

3. Set up environment variables

Update the .env file with your actual Gemini API key:

GEMINI_API_KEY=YOUR_GEMINI_API_KEY
QDRANT_URL=http://localhost:6333
PORT=3000

4. Start Qdrant database with Docker

docker-compose -f docker-compose.db.yml up -d

5. Run the development server

npm run dev

API Endpoints

Upload PDF

Upload a PDF file to be processed and indexed in the vector database.

• Endpoint: POST /api/pdf/upload
• Content-Type: multipart/form-data
• Request Body:
  • pdf: PDF file (max size: 10MB)
• Response:

  {
    "message": "PDF processed successfully",
    "collectionName": "filename.pdf",
    "content": [...]
  }

Chat with PDF

Send a query to chat with the content of a previously uploaded PDF.

• Endpoint: POST /api/pdf/chat
• Content-Type: application/json
• Request Body:

  {
    "query": "What is the main topic of this document?",
    "collectionName": "filename.pdf"
  }

• Response:

  {
    "message": "Chat completed successfully",
    "response": "The main topic of this document is..."
  }

Project Structure

.
├── src/                  # Source code
│   ├── controller/       # API controllers
│   │   ├── chat.ts       # Chat controller
│   │   └── upload-pdf.ts # PDF upload controller
│   ├── middleware/       # Express middleware
│   │   └── multer-config.ts # File upload configuration
│   ├── router/           # API routes
│   │   ├── chat-router.ts # Chat routes
│   │   └── pdf-router.ts # PDF upload routes
│   └── server.ts         # Main server file
├── docker-compose.db.yml # Docker configuration for Qdrant
├── .env                  # Environment variables (create from .env.sample)
├── package.json          # Project metadata and dependencies
├── tsconfig.json         # TypeScript configuration
└── README.md             # Project documentation