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

A curated collection of experiments and frameworks exploring the intersection of Agentic AI and Generative AI, including reasoning driven LLM agents, retrieval augmented generation (RAG), multimodal synthesis, and autonomous decision making systems.

Context AI is a RAG application using ChromaDB for vector storage and LangChain for retrieval workflows. It processes documents as context and generates intelligent answers with Gemini AI, combining efficient retrieval with generative capabilities for precise, document-driven Q&A solutions.

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

This repository explores Generative AI (GenAI) techniques for text processing, including chunking, embeddings, retrieval-augmented generation (RAG), semantic search, and text classification. It provides, optimizations, and integrations with LLMs, vector databases, and NLP models to enhance AI-driven text applications.

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 However, traditional models like GPT rely solely on pre-trained knowledge, which can lead to outdated, inaccurate, or hallucinated responses.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 Generative AI Pipelines

Spring Boot application implementing RAG with Ollama (Phi) and LangChain4j, using embeddings and vector search to deliver context-aware responses. Demonstrates LLM orchestration, AI-driven retrieval, generative AI workflows, and containerized deployment with Docker.

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This repo demonstrates how modern AI techniques like Generative AI and RAG can turn raw data into actionable insights, enabling smarter applications and knowledge-driven solutions.

Investment Guru is an intelligent, AI-driven financial assistant that helps users make smarter investment decisions using cutting-edge Generative AI and retrieval-augmented generation (RAG) techniques.

Generative AI based application that analyzes resumes, provides AI-driven feedback, and allows users to interact with a chatbot to ask questions about the resume using Retrieval-Augmented Generation (RAG).

A Python-based Retrieval-Augmented Generation (RAG) system that processes PDF documents, converts text data into structured formats, and generates AI-driven responses using generative language models.

Generative AI–driven job recommender system leveraging transformer embeddings and RAG to align candidate skills with job requirements; implemented fairness evaluation to ensure unbiased, transparent talent-role recommendations.

This repository contains various Generative AI applications, including Retrieval-Augmented Generation (RAG), AI-powered agents for content creation and other innovative AI-powered solutions. These projects utilize large language models (LLMs) and retrieval techniques to enhance AI-driven applications.

A collection of generative AI projects powered by Gemini AI, including chatbots, retrieval-augmented generation (RAG) with documents, integration with SQLite databases, and more. These projects showcase the versatility of generative models across various domains, enhancing user interaction and automating data-driven tasks.

GitLab's Chatbot is a Retrieval-Augmented Generation (RAG) chatbot designed to interact with document-based data. It supports PDFs, Markdown files, HTML, and OCR for images, enabling users to ask questions and get relevant, document-driven answers. #LLMs #Generative AI #RAG #Open AI #Chatbot

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)

This project focuses on *Agentic AI*, *Generative AI*, and *RAG systems*, exploring intelligent agents, content generation, and data-driven AI. It includes AI frameworks and workflows with tools like *LangChain*, *FAISS*, *GPT*, and *Gemini*. Contribute to advancing AI systems and building efficient, autonomous solutions.

IT & AI Fresher skilled in AI,SQL, Python, Java, Cloud, Machine Learning, NLP, LLMs, and Generative AI. Experienced in building AI-driven projects like RAG Chatbots and web apps. Certified in Google Cloud, Intel AI, and HP LIFE, eager to grow in AI/ML and IT.

🚀 Audio-Enhanced Conversational AI Chatbot using RAG 🎙️ A powerful Generative AI chatbot leveraging Retrieval-Augmented Generation (RAG) for accurate, context-aware responses. Integrated with speech-to-text and text-to-speech for seamless audio interactions. Core Features: Intelligent, context-driven conversations. Real-time audio and text suppo

A minimal Generative AI application using LangChain, Ollama's LLama2 model, and FAISS for vector search. It retrieves context from web-based documentation and generates intelligent answers using a prompt-driven RAG chain.

Agri-Crop-Management is a RAG-powered AI system for smart farming. It provides farmers with actionable insights on crop health, disease detection, irrigation, and fertilization by combining agricultural knowledge with generative AI, enabling data-driven and precision crop management.

GenAI-Powered Clinical Insights Assistant is an AI-driven healthcare analytics tool that uses NLP, RAG, and Generative AI to analyze clinical notes, EHRs, and medical guidelines, delivering evidence-backed summaries, risk insights, and actionable recommendations to support clinical and consulting decision-making.

🩺 Medical Diagnostics GenAI Chatbot The Medical Diagnostics GenAI Chatbot is an advanced, AI-driven healthcare assistant designed to revolutionize preliminary medical assessments through intelligent, conversational interaction. Leveraging the power of Generative-AI ,Retrieval-Augmented Generation (RAG), LangChain, and OpenAI’s language models

This RAG (Retrieval-Augmented Generation) system combines the power of LangChain, a framework for building AI-driven applications, with Google Gemini API, an advanced multimodal generative AI, to deliver accurate, contextually relevant, and interactive responses. This integration enhances the system’s ability to process queries, retrieve data