Advanced Retrieval Pipeline for RAG - HyDE, Hybrid Search, Reranking - Build 100% Local Retrieval

Learn to build an advanced retrieval pipeline for Retrieval-Augmented Generation (RAG) systems that goes beyond basic vector search to handle real-world production challenges. Discover why simple semantic embeddings fail when users search for specific IDs, acronyms, and keywords, and master the implementation of a comprehensive solution using Python and PostgreSQL. Explore the combination of Vector Search for semantic understanding with Full-Text Search for keyword matching, enhanced by reranking algorithms to achieve optimal retrieval performance. Configure pgvector and Full-Text Search in PostgreSQL, write SQL hybrid search functions using Reciprocal Rank Fusion (RRF), implement Hypothetical Document Embeddings (HyDE) for query enhancement, and integrate FlashRank for result reranking. Build a complete 100% local retrieval pipeline that addresses the limitations of traditional RAG tutorials and delivers production-ready performance for complex search scenarios.