How does reranking improve search accuracy?

Reranking uses a cross-encoder model to evaluate the specific relevance of the top candidates returned by the initial search, significantly improving the precision of the final results.

When should I use Reciprocal Rank Fusion (RRF)?

Use RRF when you want to combine multiple search result lists without needing to manually normalize scores from different ranking algorithms, providing a robust general-purpose fusion.

Does this skill support PostgreSQL?

Yes, it includes specific implementation templates for PostgreSQL using the pgvector extension combined with native full-text search (tsvector).

What is hybrid search?

Hybrid search combines semantic vector similarity with traditional keyword-based search (like BM25) to provide more accurate results that capture both conceptual meaning and exact term matching.

Hybrid Search Implementation

Name: Hybrid Search Implementation
Author: drgaciw

bydrgaciw

データサイエンスとML

Combines vector similarity and keyword-based search to improve retrieval accuracy in RAG systems and search engines.

概要

This skill provides standardized patterns for implementing hybrid search, bridging the gap between semantic understanding and exact keyword matching. It includes implementation templates for Reciprocal Rank Fusion (RRF), linear score combination, and cross-encoder reranking, with specific configurations for popular databases like PostgreSQL (pgvector) and Elasticsearch. It is particularly useful for building robust Retrieval-Augmented Generation (RAG) pipelines where specific domain vocabulary, names, or exact codes must be retrieved alongside semantically relevant content.

主な機能

0 GitHub stars
Linear interpolation for tunable search balance
PostgreSQL and pgvector integration templates
Cross-encoder reranking patterns for high-precision results
Standardized Reciprocal Rank Fusion (RRF) logic
Elasticsearch hybrid search configurations

ユースケース

Building domain-specific search engines for academic, legal, or medical data
Optimizing search recall by balancing semantic and lexical matching
Improving RAG systems that miss specific technical terms or product codes

概要

主な機能

0 GitHub stars
Linear interpolation for tunable search balance
PostgreSQL and pgvector integration templates
Cross-encoder reranking patterns for high-precision results
Standardized Reciprocal Rank Fusion (RRF) logic
Elasticsearch hybrid search configurations

ユースケース

Building domain-specific search engines for academic, legal, or medical data
Optimizing search recall by balancing semantic and lexical matching
Improving RAG systems that miss specific technical terms or product codes