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Drew Cain
9fac9a9416
Proper nouns in a query carry no extra weight against generic semantic similarity, so documents about a different entity on the same topic can outrank the document that actually names the queried entity (#951 cross-conversation confusion in the LoCoMo benchmark). Add an optional, lexical-only re-scoring pass to hybrid fusion: - Extract candidate entity terms from the query (capitalized / proper-noun tokens that are not common stopwords; trailing possessives stripped). - Count how many distinct query entity terms appear in each fused candidate's entity name (title) or a relation row's linked entity names. - Multiply matching candidates' fused scores by 1 + weight * min(matches, max_terms), promoting entity-matching docs. The boost runs over the full fused candidate set before the limit/offset cut, so a matching doc below the cutoff can be promoted into the returned window. It adds no model inference (index/lexical lookups only), so per-query latency overhead is trivial, and only affects hybrid retrieval. Behind three config flags, DEFAULT OFF pending LoCoMo benchmark validation: search_entity_boost_enabled, search_entity_boost_weight, search_entity_boost_max_terms. Documented in docs/semantic-search.md. Tests: unit coverage for entity-term extraction and the boost math; a hybrid-pipeline test showing reordering when enabled and unchanged ordering when disabled; and a service-level integration test over a real DB with a deterministic stub embedding provider proving an entity-matching doc outranks a higher-similarity non-matching doc only when enabled. Co-Authored-By: Claude <noreply@anthropic.com> Signed-off-by: Drew Cain <groksrc@gmail.com>