Pg_textsearch – BM25 Ranking for Postgres
I built pg_textsearch, a Postgres extension that brings proper BM25 ranking to full-text search. It's designed for AI/RAG workloads where search quality directly impacts LLM output. Postgres native ts_rank lacks corpus-aware signals (no IDF, no TF saturation, no length normalization). This causes mediocre documents to rank above excellent matches, which matters when your LLM depends on retrieval quality. Quick example: CREATE EXTENSION pg_textsearch; CREATE INDEX articles_idx ON articles USING bm25(content); SELECT title, content to_bm25query('database performance', 'articles_idx') AS…
Pg_textsearch – BM25 Ranking for Postgres launched on October 23, 2025 with 13 votes, #768 of 8,178 launches that month and more than 75% of that year's launches.
In plain words
pg_textsearch is a Postgres extension that implements BM25 ranking for full-text search. It addresses limitations in Postgres's native ts_rank function by adding corpus-aware signals like inverse document frequency, term frequency saturation, and length normalization. Built for AI and retrieval-augmented generation workloads where search result quality directly affects language model outputs, it integrates with pgvector for hybrid search and operates fully transactionally without sync jobs. The preview release uses an in-memory architecture with disk-based segments planned.
written from the facts on this page · September 2026
From the sources
Install and tune pg_textsearch (v1.1.0) for BM25 full-text search on Tiger Cloud and self-hosted deployments
In the maker’s words, at launch
I built pg_textsearch, a Postgres extension that brings proper BM25 ranking to full-text search. It's designed for AI/RAG workloads where search quality directly impacts LLM output. Postgres native ts_rank lacks corpus-aware signals (no IDF, no TF saturation, no length normalization). This causes mediocre…
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