Elasticsearch's Hybrid Search, Now in Postgres (BM25 + Vector + RRF)

The blog post discusses the integration of Elasticsearch's hybrid search capabilities, specifically BM25 for keyword ranking, vector embeddings for semantic search, and Reciprocal Rank Fusion (RRF) for result merging, directly within PostgreSQL. This integration eliminates the need for complex data pipelines typically required to sync data between Postgres and Elasticsearch, thereby simplifying the infrastructure and management of search systems. The post explains how PostgreSQL extensions like pg_textsearch and pgvectorscale facilitate this hybrid search functionality, making use of RRF to combine keyword and semantic search results effectively. Additionally, it introduces pgai, which automates the synchronization of data changes and embedding updates within PostgreSQL, further reducing the need for external systems and ensuring that search data remains up-to-date in real-time. The overall aim is to highlight how PostgreSQL can now handle advanced search operations traditionally managed by Elasticsearch, providing a more streamlined and efficient approach for managing search capabilities within a single database system.