Which extensions does this skill require?

It requires the pgvector extension for semantic search and the pg_textsearch extension for specialized BM25 keyword search capabilities.

Does it support language-specific search?

Yes, it provides guidance on configuring text_config for various languages like English, French, and German to ensure accurate BM25 processing.

What is hybrid search in PostgreSQL?

Hybrid search is a technique that combines keyword-based BM25 search with semantic vector search to improve result relevance and recall, capturing both exact matches and meaning.

Can this skill handle large datasets?

Yes, it includes instructions for using pgvectorscale and StreamingDiskANN, which are optimized for high-performance filtered vector search on datasets exceeding 10 million records.

How does Reciprocal Rank Fusion (RRF) work?

RRF is an algorithm used to merge ranked results from different search methods (like BM25 and vectors) into a single list by summing the reciprocal of their ranks, avoiding the need for complex score normalization.

PostgreSQL Hybrid Text Search

Name: PostgreSQL Hybrid Text Search
Author: timescale

bytimescale

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1,486

Database Management

Implements advanced search strategies by combining BM25 keyword matching with semantic vector search using Reciprocal Rank Fusion.

About

This skill provides a comprehensive framework for building high-performance search systems within PostgreSQL by merging the precision of keyword-based BM25 search with the conceptual understanding of semantic vector embeddings. It guides users through setting up the pg_textsearch and pgvector extensions, implementing efficient parallel query patterns, and applying Reciprocal Rank Fusion (RRF) to produce unified, high-relevance search results. Ideal for developers building RAG applications or enterprise search engines that require both exact matches and semantic depth.

Key Features

Integration patterns for ML reranking models and cross-encoders
Client-side Reciprocal Rank Fusion (RRF) for Python and TypeScript
Configurable weighting strategies for keyword vs. semantic relevance
Scaling guidance for large datasets using pgvectorscale and StreamingDiskANN
1,486 GitHub stars
Dual-index optimization for BM25 and HNSW vector search

Use Cases

Building RAG systems that handle both exact technical terms and conceptual queries
Improving product search relevance in e-commerce catalogs
Optimizing technical documentation search for developers and support teams

About

Key Features

Integration patterns for ML reranking models and cross-encoders
Client-side Reciprocal Rank Fusion (RRF) for Python and TypeScript
Configurable weighting strategies for keyword vs. semantic relevance
Scaling guidance for large datasets using pgvectorscale and StreamingDiskANN
1,486 GitHub stars
Dual-index optimization for BM25 and HNSW vector search

Use Cases

Building RAG systems that handle both exact technical terms and conceptual queries
Improving product search relevance in e-commerce catalogs
Optimizing technical documentation search for developers and support teams