Announcing Native BM25 Ranking in AlloyDB and Cloud SQL

Vector search is a critical component of generative AI, retrieval-augmented generation (RAG), and data agent architectures, but sometimes vector search alone isn't enough. While vector embeddings are incredible at understanding conceptual meaning, they stumble on specific alphanumeric IDs and exact product SKU numbers. To build truly robust search and AI applications, you may need the combination of semantic vector search and traditional exact keyword full-text search — what we call hybrid search.

In search, Best Matching 25, or BM25, is a key algorithm used to estimate how relevant a document is to a given query. Until today, if you wanted BM25 ranking with AlloyDB or Cloud SQL, you needed to add an additional full-text search backend. This introduced data silos, sync lags, and operational complexity. Today, we are eliminating the friction of maintaining a separate full-text search backend altogether, with the preview of the native BM25 index in AlloyDB and Cloud SQL for PostgreSQL 17+, made possible through the open-source pg_textsearch extension created by TigerData.

Now, with a unified hybrid search backend, you no longer need to provision, manage, or pay for separate systems to get state-of-the-art full-text retrieval. It all happens directly inside your database, where your operational data lives, delivering: Industry-standard keyword ranking: Powered by TigerData's pg_textsearch , bring lightning-fast, C-optimized BM25 scoring directly to your Postgres tables. No complexity, total consistency: Eliminate the data duplication, ETL pipelines, and synchronization lag that you get when you maintain multiple backends for vector and full-text retrieval.

Supercharged semantic search (AlloyDB exclusive): Get up to 6x and 10x faster vector search queries (when compared to standard PostgreSQL) with ScaNN and HNSW index types. If you’ve used PostgreSQL's built-in ts_rank for full-text search at any meaningful scale, you already know its limitations. Ranking quality degrades as your corpus grows. There’s no support for inverse document frequency, so common words carry the same weight as rare ones. There’s no term-frequency saturation, so a document that mentions "database" 50 times outranks one that mentions it once.