Build a RAG System with LLaMA-2, LangChain & MPNet Embeddings

Learn how to build a production-ready Retrieval-Augmented Generation (RAG) system using LLaMA-2, LangChain, and MPNet embeddings. This hands-on project teaches you how to combine large language models with semantic search to create intelligent, context-aware AI applications. This project is ideal for developers who want to master LLM pipelines, vector search, and enterprise-grade AI architectures used in modern AI products. 🚀 What You Will Build: • End-to-end RAG pipeline using LLaMA-2 • Semantic document search with MPNet embeddings • Context-aware response generation using LangChain • Scalable architecture for real-world AI applications 🛠️ TECH STACK & TOOLS USED: • LLaMA-2 – Large language model for reasoning & generation • LangChain – Orchestration framework for LLM pipelines • MPNet – High-quality embedding model for semantic search • FAISS – Vector similarity search engine • Python – Core backend development 👨‍💻 WHO IS THIS FOR? • AI/ML Engineers building production-grade RAG systems • Developers working with LLMs and vector databases • Students learning advanced AI system design • Job seekers adding LLM + RAG expertise to their resume 📝 PROJECT CHAPTERS: 0:00 – Intro & RAG Architecture Overview 3:20 – Understanding MPNet Embeddings 7:10 – Building the Vector Store with FAISS 12:40 – Integrating LLaMA-2 with LangChain 18:20 – End-to-End RAG Pipeline Execution 26:10 – Final Thoughts & Optimization Tips 🔗 RESOURCES & LINKS: https://www.udemy.com/course/generati... 🔖 Hashtags #GenerativeAI #RAG #LLaMA2 #LangChain #MPNet #AIProjects #VectorDatabase #LLM #AIEngineer #MachineLearning #AIDevelopment #LearnAI #ScratchLearn #BuildInPublic