What is hybrid search in PGVector?

Hybrid search combines semantic vector search (capturing meaning) with keyword search (capturing exact terms) using Reciprocal Rank Fusion to provide more accurate results than either method alone.

What is Reciprocal Rank Fusion (RRF)?

RRF is an algorithm used to merge results from multiple search systems by looking at their relative ranks rather than raw scores, eliminating the need for complex score normalization.

Is this compatible with Python backends?

Yes, the skill provides detailed implementation patterns specifically for Python using SQLAlchemy and the pgvector-python library.

How does this skill improve filtered vector searches?

It leverages PGVector 0.8's iterative scan features like 'relaxed_order' to ensure that queries with metadata filters return the full requested result count without sacrificing performance.

Why use HNSW instead of IVFFlat for vector indexing?

HNSW offers significantly faster query speeds (up to 100x) and better scalability for large datasets, making it the preferred choice for production environments.

PGVector Hybrid Search

Name: PGVector Hybrid Search
Author: yonatangross

byyonatangross

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Database Management

Implements production-grade hybrid search combining semantic vector similarity with keyword matching using PostgreSQL and PGVector.

About

The PGVector Hybrid Search skill provides a comprehensive framework for building high-performance retrieval systems within PostgreSQL. It combines the semantic power of vector embeddings with the precision of keyword-based full-text search using Reciprocal Rank Fusion (RRF). This skill is essential for developers building RAG applications, knowledge bases, or recommendation engines that require the reliability of a relational database alongside modern AI retrieval techniques. It includes optimized indexing strategies for HNSW, metadata filtering patterns, and performance tuning for PGVector 0.8+.

Key Features

Advanced iterative scan modes for filtered queries in PGVector 0.8+
Reciprocal Rank Fusion (RRF) algorithm for robust result ranking
Production-ready SQLAlchemy and Python implementation patterns
8 GitHub stars
Hybrid retrieval merging vector similarity and BM25 keyword matching
Optimized HNSW index configurations for million-scale vector sets

Use Cases

Implementing enterprise search with complex metadata filtering requirements
Scaling semantic search systems to handle millions of document chunks
Building high-accuracy Retrieval-Augmented Generation (RAG) pipelines

About

Key Features

Advanced iterative scan modes for filtered queries in PGVector 0.8+
Reciprocal Rank Fusion (RRF) algorithm for robust result ranking
Production-ready SQLAlchemy and Python implementation patterns
8 GitHub stars
Hybrid retrieval merging vector similarity and BM25 keyword matching
Optimized HNSW index configurations for million-scale vector sets

Use Cases

Implementing enterprise search with complex metadata filtering requirements
Scaling semantic search systems to handle millions of document chunks
Building high-accuracy Retrieval-Augmented Generation (RAG) pipelines