Lecture 31-a: Hybrid Search & Reranking - Boost RAG Accuracy with Advanced Retrieval скачать в хорошем качестве

Lecture 31-a: Hybrid Search & Reranking - Boost RAG Accuracy with Advanced Retrieval

Master advanced retrieval techniques! Learn hybrid search combining semantic and keyword-based methods, reranking strategies, and how to dramatically improve RAG system accuracy. 🎯 What You'll Learn: • Retrieval methods comparison and trade-offs • Semantic search vs. keyword-based search • BM25 algorithm for keyword matching • TF-IDF fundamentals • Hybrid search architecture • Combining semantic and keyword results • Reranking strategies for accuracy • LLM-based reranking • Improving relevance scoring • Production optimization techniques 💡 Key Topics Covered: ✅ Cosine similarity for semantic matching ✅ MMR (Maximum Marginal Relevance) for diversity ✅ Keyword-based search limitations ✅ BM25 algorithm deep dive ✅ Hybrid search implementation ✅ Combining multiple retrieval methods ✅ Reranking with language models ✅ Score recalibration and ranking ✅ Accuracy vs. speed trade-offs ✅ Interview-ready explanations 📚 Practical Examples: • Implementing hybrid search pipelines • Setting up BM25 keyword search • Combining semantic and keyword results • Reranking top-K results • Using LLMs for intelligent reranking • Measuring retrieval accuracy • Optimizing relevance for production • Real-world accuracy improvement strategies 🛠️ Tools & Libraries: Python • Langchain • BM25 • Elasticsearch • LLM Reranker Cohere Rerank • LLaMA Index • Vector Stores with Hybrid Search Perfect for building production-grade RAG systems with maximum accuracy! #GenAI #HybridSearch #Reranking #RAGSystem #RetrievalAugmentedGeneration #SemanticSearch #BM25 #Python #Langchain #MachineLearning #AI