RAG with NVIDIA AI and Azure SQL

This Jupyter Notebook implements Retrieval-Augmented Generation (RAG) using NVIDIA AI models for embeddings and chat, and Azure SQL Database for storing and retrieving resume embeddings.

Features

• Uses NVIDIA AI models (meta/llama-3.3-70b-instruct and nvidia/embed-qa-4).
• Stores resume embeddings in Azure SQL Database.
• Supports optimized vector search for relevant candidates.
• Implements streaming responses for better chatbot experience.

Setup Instructions

1. Install dependencies:

   pip install -r requirements.txt
2. Set up your .env file with API keys:

   NVIDIA_API_KEY=your_nvidia_api_key_here
   NVIDIA_CHAT_API_KEY=your_nvidia_chat_model_api_key_here
   AZUREDOCINTELLIGENCE_ENDPOINT=your_azure_doc_intelligence_endpoint_here   AZUREDOCINTELLIGENCE_API_KEY=your_azure_doc_intelligence_api_key_here
   AZURE_SQL_CONNECTION_STRING=your_azure_sql_connection_string_here
   FILE_PATH=Path to the resume dataset
3. Run the notebook.

File Structure

• NVIDIA-RAG-with-resumes.ipynb → Main Jupyter Notebook
• .env → Environment variables for API keys
• README.md → Documentation
• CreateTable.sql → Create Table for Azure SQL Database

Dataset

We use a sample dataset from Kaggle containing PDF resumes for this tutorial. For the purpose of this tutorial we will use 120 resumes from the Information-Technology folder