Hybrid RAG System: Vector Search + BM25 for Better Retrieval | LangChain Tutorial | Humanitarians AI

Description

🧠 Build a production-ready Hybrid RAG system that combines semantic vector search with BM25 keyword matching for more accurate and context-aware retrieval. In this tutorial, we show how to create a hybrid question-answering system using LangChain, ChromaDB, and Groq’s Llama 3.3 70B, optimized for precision and scalability. ✅ What You’ll Learn: 🔍 Combine vector similarity search with BM25 keyword retrieval for hybrid accuracy 💾 Set up ChromaDB using HuggingFace embeddings ⚙️ Implement a weighted ensemble retriever with adjustable parameters 🧩 Explore document chunking strategies for improved recall 🚀 Build a QA agent powered by Groq API for fast and reliable inference 🧠 Project Highlight: Hybrid Retrieval System • Integrates semantic understanding with exact keyword matching • Handles both conceptual and literal queries effectively • Ideal for enterprise search, documentation Q&A, and technical support bots This tutorial helps you build a retrieval pipeline that’s smarter, faster, and more adaptable — a perfect foundation for real-world AI systems. 🔗 Explore More from Humanitarians AI: 🌐 Website: https://www.humanitarians.ai/ 📺 YouTube Channel: https://www.youtube.com/@humanitariansai 💼 LinkedIn: https://www.linkedin.com/company/105696953/ 🏷️ Tags / Keywords Hybrid RAG, Vector Search, BM25, LangChain, ChromaDB, Retrieval Augmented Generation, Question Answering, LLM, Groq API, Llama 3, Semantic Search, NLP, MLOps, Machine Learning, Python Tutorial, AI Systems, Production ML, HuggingFace