End-to-End RAG Pipeline Explained | Build AI Chatbot with LangChain + FAISS

Description

In this video, I demonstrate a complete RAG (Retrieval-Augmented Generation) based AI application that can read documents and answer questions using semantic search and LLMs. This is not just a simple chatbot — it is a full system built with a focus on retrieval, explainability, and deployment. 🚀 What you’ll learn: - How RAG works (simple explanation) - End-to-end pipeline (ingestion → chunking → embedding → retrieval → generation) - How vector search (FAISS) is used - How LLM generates grounded answers - How to make RAG systems explainable (chunks + similarity scores) - Real project demo 🛠 Tech Stack: - Python - LangChain - FAISS (Vector Database) - LLaMA 3 (via Groq) - Streamlit - GitHub Actions (CI/CD) 📌 Key Features: - Multi-document support - Semantic search using embeddings - Top-K retrieval tuning - Batch processing for efficient indexing - Explainable AI (retrieved chunks + scores) 🔗 GitHub Repository: https://github.com/himanshu231204/ragnova-rag-chatbot 🌐 Live Demo: https://ragnova-bot.streamlit.app/ 💡 Key Insight: RAG is not about the LLM — it’s about retrieval quality. If you're learning AI/ML or building GenAI projects, this video will help you understand how real systems are built. 👍 Like, Share & Subscribe for more AI engineering content. #RAG #GenerativeAI #LangChain #Python #AI #MachineLearning #LLM