Build-An-LLM-RAG-Chatbot-With-LangChain-Python

NeuroLens is a premium, state-of-the-art Document Retrieval-Augmented Generation (RAG) system. It combines an immersive, holographic, sci-fi-themed React frontend with a high-performance FastAPI backend.

This course is designed to take you from the basics to advanced concepts, providing hands-on experience in building, deploying, and optimizing AI models using Langchain and Huggingface. Perfect for AI enthusiasts, developers, and professionals

Legally is an AI chatbot created to help people understand Indian law easily. The chatbot explains the legal consequences of different actions and provides information about the punishments for various crimes as outlined in Indian law.

A full-stack AI-powered knowledge management system where users can save links, PDFs, and images, automatically organize them using AI, search semantically, and chat with their own data using RAG. Includes a D3-based knowledge graph and Chrome extension for instant saving.

A sophisticated Retrieval-Augmented Generation (RAG) system designed for financial document analysis with temporal intelligence and memory capabilities.

Built a voice-enabled conversational RAG support agent with hybrid retrieval, multi-LLM orchestration, Whisper STT, and ElevenLabs TTS, improving grounded response accuracy (~40%) and reducing clarification loops (~50%).

AgenticXRAG: Event-Driven Retrieval-Augmented Generation Pipeline

AI-powered Incident Response System that automatically analyzes production incidents and recommends mitigation actions using historical data and machine learning.

📄 Enable smart querying of your documents with a RAG-based chatbot that retrieves context and generates accurate answers from your data.

GenAI | RAG-based system for semantic Q&A over YouTube transcripts using FAISS, Gemini embeddings, and LLM-driven context-aware generation

AI-powered Supply Chain Management Assistant using Flowise, Gemini 2.5 Flash, Pinecone, and FastAPI with RAG-based document retrieval and supplier analytics.

Official repository for our accepted SETN 2026 full paper on compact LLMs (1B–8B) as RAG generators, accuracy, latency, and bottleneck analysis under a unified pipeline.

"My complete LangChain learning journey — from basics to advanced RAG, LCEL, LangGraph, LangServe, LangSmith with hands-on code examples."

AI-powered Question Answering Assistant capable of answering questions from uploaded documents using a Retrieval-Augmented Generation (RAG) approach.The goal of this project is to evaluate your ability to design an AI pipeline, integrate LLMs, implement vector search, and expose the solution through clean APIs.