📚 PDF Chat RAG API

A Node.js API that enables intelligent conversations with PDF documents using Retrieval-Augmented Generation (RAG) powered by Google's Gemini AI and LangChain. Built with LangChain's powerful RAG framework for efficient document processing and contextual retrieval.

🌟 Features

• 📤 PDF Upload: Secure file upload with size and type validation
• 💾 Vector Storage: Document embeddings stored in Qdrant vector database
• 🤖 Smart Retrieval: Contextual search using similarity matching
• 💬 AI Chat: Natural conversations with PDF content using Gemini AI

🚀 Getting Started

Prerequisites

• Node.js
• Docker (for Qdrant)
• Google Gemini API Key

Environment Setup

Create a .env file with:

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

Installation

1. Clone the repository
2. Install dependencies:

   npm install

3. Start Qdrant:

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

4. Run the server:

   npm run dev

🔄 API Endpoints

Upload PDF

POST /upload-pdf
Content-Type: multipart/form-data

form-data:
- pdf: <file>

Chat with PDF

POST /chat
Content-Type: application/json

{
  "pdfName": "example.pdf",
  "query": "What is this document about?"
}

🛠️ Tech Stack

• Express.js - Web framework
• LangChain - RAG implementation
• Google Gemini AI - Embeddings and chat completion
• Qdrant - Vector database
• Multer - File upload handling

🔒 Security

• File size limit: 5MB
• PDF-only file validation
• Error handling and validation