Entity resolution struggles when systems must choose between the rigid precision of BM25 and the fuzzy flexibility of Vector Search. Part 4 reveals why simple linear weighting fails and introduces Reciprocal Rank Fusion (RRF) as the superior alternative. We explore the architectural shift to Hybrid Search, demonstrating how to merge rank positions rather than raw scores using Spring Boot and ParadeDB.

Exact match is easy. Similarity is hard. This post climbs the ladder of structures that make vector lookups fast: linked lists (slow scans), skip lists (express lanes), small-world graphs, and HNSW. Then it shows how pgvector brings HNSW into PostgreSQL so entity resolution can happen where your records already live.