Advanced Retrieval Pipeline for RAG (HyDE, Hybrid Search, Reranking) | Build 100% Local Retrieval

Venelin Valkov · Advanced ·🔍 RAG & Vector Search ·3mo ago

Skills: Advanced RAG90%Vector Stores80%RAG Evaluation80%

Most RAG tutorials stop at simple vector search. In production, this fails. Users search for specific IDs, acronyms, and keywords that semantic embeddings miss. In this video, we'll build an Advanced Retrieval Pipeline from scratch using Python and PostgreSQL. We'll combine Vector Search (Semantic) with Full-Text Search (Keyword) and layer on Reranking algorithms to get the best of both worlds. AI Academy: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 00:00 - Why Vectors aren't enough 01:07 - The Advanced Retrieval Architecture (HyDE + Hybrid + Rerank) 03:35 - Configuring pgvector & Full-Text Search in PostgreSQL 04:30 - Writing the SQL Hybrid Search Function (RRF) 06:25 - Implementing HyDE 09:11 - Reranking with FlashRank 12:04 - Complete Retrieval Pipeline 14:25 - Conclusion Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join

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Chapters (8)

Why Vectors aren't enough

1:07 The Advanced Retrieval Architecture (HyDE + Hybrid + Rerank)

3:35 Configuring pgvector & Full-Text Search in PostgreSQL

4:30 Writing the SQL Hybrid Search Function (RRF)

6:25 Implementing HyDE

9:11 Reranking with FlashRank

12:04 Complete Retrieval Pipeline

14:25 Conclusion

Watch this before applying for jobs as a developer.