Build Production-Ready Retrieval RAG Pipeline in LangChain | Hybrid Search (BM25), Re-ranking & HyDE

Your RAG system is probably hallucinating because of bad retrieval. In this video, I'll show you exactly how to build production-grade RAG that actually works. We'll combine BM25 with semantic search, add re-ranking, and implement HyDE query enhancement - all with LangChain code examples. Complete source code + text tutorial (requires MLExpert Pro): https://www.mlexpert.io/academy/v1/context-engineering/advanced-retrieval Colbert Small: https://www.answer.ai/posts/2024-08-13-small-but-mighty-colbert.html HyDE paper: https://arxiv.org/abs/2212.10496 AI Academy: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 00:00 - Why naive RAG fails 04:04 - BM25 and hybrid search 07:03 - Re-ranking with ColBERT for precision 08:38 - HyDE query enhancement 10:32 - Full RAG retrieval pipeline with citations Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join