Exercise 3: ts_rank() vs. BM25 — "Smaller-Scale Approximation," Not "Broken" — Possible Solution ==================================================================== WHAT ts_rank() DOES PROVIDE ------------------------------ Per this chapter, "ts_rank() does incorporate real signals — term- frequency-adjusted weighting via setweight(), some proximity awareness." Postgres's own ts_rank() function is genuinely doing real relevance-scoring work, not simply returning an arbitrary or fixed value — it accounts for how often terms appear, respects deliberate field weighting (a title mattering more than body text, via setweight(), exactly as postgres1-6 covered), and has some awareness of how close together matched terms appear within the document. WHAT ts_rank() DOES NOT PROVIDE, COMPARED TO BM25 ------------------------------ Per this chapter, "it doesn't implement the same level of length normalization or frequency saturation refinement, and Postgres's own full-text search generally wasn't designed to be tuned to the same degree." Specifically, ts_rank() doesn't apply BM25's own term- frequency-saturation refinement (this chapter's own Exercise 2 material — diminishing returns for repeated terms) or BM25's own field-length normalization (proportionally weighting a match more heavily in a short document than a long one) with the same rigor and tunability BM25 provides. Postgres's own full-text search system also generally offers less ability to fine-tune the underlying scoring formula itself compared to the level of configuration BM25 supports in Elasticsearch/OpenSearch. WHY THIS IS FRAMED AS "SMALLER-SCALE APPROXIMATION," NOT "BROKEN" ------------------------------ Per this chapter, "this isn't 'Postgres's own version is broken' — it's a genuine, honest 'smaller-scale approximation vs. purpose-built, heavily-refined algorithm' difference, matching this course's own consistent pattern of fair, not dismissive, comparisons." ts_rank() genuinely, correctly computes a real relevance signal using real, legitimate information-retrieval principles (term frequency, field weighting, some proximity awareness) — it isn't producing wrong or meaningless results. It's simply a LESS elaborate, less heavily- refined implementation of the same underlying idea BM25 represents, appropriate to Postgres's own actual design center (a relational engine with full-text search as a genuinely useful but secondary feature, per postgres1-6/search1-1's own framing) rather than a purpose-built search engine whose entire reason for existing is delivering the most sophisticated possible relevance ranking. Calling it "broken" would misrepresent a real, working, useful tool that's simply scoped more modestly than a specialized alternative — exactly the same fair-comparison discipline this course has applied throughout (per search1-1's own warn-box on not treating this engine as a general database replacement either). WHY THIS WORKS AS AN ANSWER ------------------------------ It states precisely what ts_rank() genuinely does provide and what it specifically lacks compared to BM25 using the chapter's own wording, and explains why the "approximation, not broken" framing is accurate — a real, working tool scoped to a different design center, not a failed attempt at the same goal.