Vector and Hybrid Search

Course overview

Vector and hybrid search: fundamentals (cosine, dot product, HNSW, IVF), using pgvector for small-to-medium RAG, Qdrant for dedicated vector search with payload filtering, and hybrid BM25+vector+reranking strategies.

Introduces vector search principles (similarity measures, approximate indexes like HNSW/IVF) and their impact on latency/quality.

Explains integrating pgvector into Postgres as a practical option for small-to-medium RAG setups.

Describes Qdrant as a dedicated engine including payload filtering and appropriate usage scenarios.

Outlines hybrid architectures combining BM25, vector search and reranking stages to boost precision and recall.