This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

BOOK

This repository will be my walk through of the book "RAG Driven Generative AI" by Denis Rothman.

RAG-Driven Generative AI Pipelines

Generative AI has made remarkable strides in creating human-like text, images, and even code. However, traditional models like GPT rely solely on pre-trained knowledge, which can lead to outdated, inaccurate, or hallucinated responses. Retrieval-Augmented Generation (RAG) addresses these limitations. We offer various types of RAG here

RAG Driven Gen AI practice

No description available

No description available

No description available

No description available

No description available

No description available

Repo guide us to Implement basic Gen AI driven RAG Applications

GEN AI – Agentic Approval & Risk Intelligence Platform for enterprises. Automates document approvals, risk assessment, and multi-level workflows using GenAI agents and RAG pipelines. Built with FastAPI, PostgreSQL, and LLM-driven intelligence.

CareerPath-Gen is an AI-driven application that analyzes a candidate’s resume, evaluates role-fit for selected career paths, identifies missing skills, and generates a personalized 12-week learning roadmap using advanced Generative AI concepts. This project demonstrates end-to-end Generative AI system design, including: (RAG)

⚡ AuraEnergy AI: Next-Gen Smart Grid Optimizer A high-performance Agentic RAG system built with FastAPI & React to monitor energy flow in real-time. Features AI-driven anomaly detection, solar efficiency tracking, and carbon footprint analysis. Powered by PostgreSQL (Supabase) & LSTM models for a sustainable future. 🌿📊🤖