Скачать или смотреть Advanced RAG Re-Ranking Tutorial | Build Better AI Search with LangChain, ChromaDB (FREE)

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https://github.com/siddharth-Kharche/...

🔥 Learn How to Build a Production-Ready RAG System with Advanced Re-Ranking!

In this comprehensive tutorial, you'll discover how to implement Re-Ranking in Retrieval-Augmented Generation (RAG) to dramatically improve AI search quality and relevance. Re-ranking acts as a smart filter that refines initial search results before feeding them to your LLM, ensuring the most relevant information is prioritized.​​

🚀 What You'll Learn:
✅ What Re-Ranking is and why it's critical for RAG systems​
✅ Complete setup with FREE tools: Groq API, ChromaDB, Nomic Embeddings​
✅ Implementation using LangChain framework and FlashRank reranker​
✅ Building a contextual compression retriever for better results​
✅ Web data loading, chunking, and vector store creation​
✅ End-to-end RAG pipeline with advanced re-ranking​

🛠️ Tech Stack:

LLM: Groq (Free Open Source Models)​

Vector Store: ChromaDB​

Embedding Model: Nomic-embed-text-v1.5​

Framework: LangChain​

Re-Ranker: FlashRank​

📚 Perfect For:
AI/ML developers, Data scientists, Python developers, Anyone building intelligent search systems, RAG system builders, NLP enthusiasts​​

🔗 Google Colab Notebook:
[Link to your Colab notebook]

💡 Why Re-Ranking Matters:
Re-ranking significantly enhances accuracy, relevance, and overall quality of AI-generated responses by intelligently sorting retrieved passages based on query intent and user context. This tutorial shows you how to implement it from scratch using completely FREE tools.​​


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