Hybrid Search: Combine Lexical and Vector Rankings
Combine lexical and vector candidates with deliberate ranking. Explore reciprocal rank fusion, candidate windows, duplication, and segment-level tests.
VECTORTOKEN LAB / CATEGORY
Search & Retrieval
Find the evidence. Then prove the ranking helps.
Retrieval quality depends on the task, source content, representation, index, and ranking policy. This collection separates those stages so an improvement can be attributed to the part of the system that changed. An exact vector neighbor is not automatically an answer, and a fast approximate query does not establish useful search for every query type.
Build a small relevance set with the token vector search guide. Compare index behavior against exact search with the HNSW and IVFFlat article. Turn to hybrid ranking when identifier-heavy and paraphrase-heavy queries reveal complementary weaknesses in lexical and vector baselines. Preserve each baseline while experimenting, and keep the eligible corpus and model contract constant when comparing one component.
Evaluate permission filters, duplicate results, candidate coverage, and queries with no supported answer. Report weak segments alongside averages. A reproducible failed example is often a better starting point for the next experiment than a single headline metric that hides several unrelated causes.
THE READING PATH
03 articles to explore.
HNSW vs IVFFlat: Evaluate Your Vector Database Index
Compare HNSW and IVFFlat with your own workload. Measure recall, filtering, latency, resource use, updates, and recovery against an exact baseline.
Token Vector Search: A Practical Retrieval Guide
Build an inspectable semantic retrieval baseline. Evaluate compatible embeddings, exact search, relevance labels, permissions, and candidate coverage.