Query Expansion RAG: 3X Better AI Retrieval in 10 Minutes! (LangChain + ChromaDB Tutorial)

Description

🚀 Build an Advanced Query Expansion RAG System that retrieves 3X more relevant context! In this tutorial, you'll learn how to implement Query Transformation RAG - an advanced retrieval technique that generates multiple query variations to dramatically improve your AI's retrieval accuracy. Perfect for building production-ready AI applications! github- https://github.com/siddharth-Kharche/Query-Transformation-RAG- 🔥 What You'll Learn: ✅ Query Expansion & Transformation techniques ✅ Multi-query retrieval for better context coverage ✅ ChromaDB vector store implementation ✅ LangChain framework integration ✅ Using Groq's FREE open-source LLMs ✅ Nomic embeddings for semantic search ✅ Document deduplication strategies ✅ Production-ready RAG architecture 💻 Tech Stack: - LangChain (RAG Framework) - ChromaDB (Vector Database) - Groq API (Free LLM Access) - Nomic Embeddings (nomic-embed-text-v1.5) - Python + HuggingFace 📌 Key Features: - Generates 3 alternative query versions automatically - Retrieves using both original + transformed queries - Removes duplicate documents intelligently - Combines context for comprehensive responses - Works with any web data source 🔗 Resources: - GitHub Code: [Your Link] - Groq API Key: https://console.groq.com/keys - HuggingFace Token: https://huggingface.co/settings/tokens - ChromaDB Docs: https://www.trychroma.com/ 🎯 Perfect For: - AI/ML Developers - RAG System Builders - NLP Engineers - Anyone building chatbots or Q&A systems 💡 Why Query Expansion RAG? Unlike basic RAG systems that use a single query, Query Expansion generates multiple query variations to capture different aspects and terminology. This improves recall by 2-3X compared to standard RAG implementations! 👨‍💻 About This Project: This implementation uses FREE tools and models, making it perfect for learning and prototyping. The code is production-ready and can be adapted for various use cases including document Q&A, knowledge bases, and AI assistants. #RAG #AI #MachineLearning #LangChain #VectorDatabase #NLP #Python #AIProjects #GenAI #QueryExpansion 🔔 Subscribe for more AI/ML tutorials and RAG system implementations! 📧 Questions? Drop them in the comments below!