🧠 RAG Document Assistant

A Retrieval-Augmented Generation (RAG) system that allows querying documents intelligently using LangChain, Google Gemini API, and Pinecone Vector Database.

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🚀 Tech Stack

• Backend: Node.js

• AI Model: Google Gemini (@google/generative-ai)

• Vector Database: Pinecone

• Framework: LangChain.js

• File Parsing: pdf-parse

• Embedding Store: LangChain’s PineconeStore

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🔑 Environment Variables

Create .env:

GEMINI_API_KEY=your_gemini_api_key
PINECONE_API_KEY=your_pinecone_api_key
PINECONE_INDEX_NAME=your_index_name
PINECONE_ENVIRONMENT=your_pinecone_environment

📁 Project Structure

RAG/
├── Prepare.js          # Creates embeddings from documents & stores them in Pinecone
├── chat.js             # Handles user queries using Gemini + vector retrieval
├── node_modules/       # Node.js dependencies
├── package.json        # Project metadata & dependencies
├── .env                # API keys & environment variables
└── README.md           # Project documentation