What is hybrid search in the context of LLM applications?

Hybrid search combines semantic (vector) search, which understands intent and meaning, with lexical (keyword) search, which handles exact matches and specific terminology, to provide more accurate results for RAG systems.

When should I use Reciprocal Rank Fusion (RRF)?

Use RRF when you want to combine results from multiple search methods (like vector and BM25) without needing to manually tune weights, as it provides a robust way to fuse different scoring scales.

How does cross-encoder reranking improve search quality?

Cross-encoder reranking takes the top candidates from an initial fast search and uses a more powerful neural model to re-evaluate their relevance to the query, significantly increasing the precision of the final results.

Does this skill support PostgreSQL implementations?

Yes, it includes specialized templates for PostgreSQL using the pgvector extension for vector storage and built-in full-text search for keyword matching.

Hybrid Search Implementation

Name: Hybrid Search Implementation
Author: ccf

byccf

Data Science & ML

Implements advanced hybrid search architectures combining vector similarity and keyword matching for superior information retrieval.

About

This skill provides standardized patterns and implementation templates for combining semantic vector search with lexical keyword-based retrieval. It helps developers overcome common limitations in pure vector search—such as missing exact terms, names, or domain-specific codes—by integrating Reciprocal Rank Fusion (RRF), linear scoring, and cross-encoder reranking. Whether you are building RAG systems in PostgreSQL with pgvector or utilizing Elasticsearch, this skill provides the necessary code structures to ensure higher recall and more precise search results for LLM applications.

Key Features

Linear score normalization techniques
Elasticsearch hybrid search patterns
Cross-encoder reranking strategies
PostgreSQL & pgvector integration templates
0 GitHub stars
Reciprocal Rank Fusion (RRF) algorithms

Use Cases

Creating robust search engines that handle both semantic meaning and exact matches
Improving search precision for domain-specific terminology or product codes
Building high-accuracy RAG (Retrieval-Augmented Generation) systems

About

Key Features

Linear score normalization techniques
Elasticsearch hybrid search patterns
Cross-encoder reranking strategies
PostgreSQL & pgvector integration templates
0 GitHub stars
Reciprocal Rank Fusion (RRF) algorithms

Use Cases

Creating robust search engines that handle both semantic meaning and exact matches
Improving search precision for domain-specific terminology or product codes
Building high-accuracy RAG (Retrieval-Augmented Generation) systems