pgvector-search

What problem does it solve?

This Skill enables production-grade hybrid search by combining semantic vector retrieval with keyword search (BM25) using Reciprocal Rank Fusion, providing fast and accurate results over large document collections.

Core Features & Use Cases

• Hybrid semantic + keyword search with RRF to balance recall and precision across 1024-dim embeddings and text indexes.
• Vector indexing with HNSW for scalable, low-latency retrieval on large corpora.
• BM25 keyword search via precomputed tsvector and gin indexes for exact phrase matching.
• Metadata boosting using section titles, document paths, and content types to improve relevance.
• Use cases: enterprise docs, knowledge bases, API docs, code repositories, and product FAQs.

Quick Start

Run a sample hybrid search by providing a query and obtaining top results from the search endpoint.