How do cross-encoders improve search results?

Cross-encoders are used as a secondary 'reranking' step. They evaluate the query and document pairs more deeply than initial search methods to find the best possible match among top candidates.

Does this skill work with PostgreSQL?

Yes, it provides specific SQL and Python patterns for using the pgvector extension alongside PostgreSQL's native full-text search capabilities.

When should I use Reciprocal Rank Fusion (RRF)?

RRF is ideal for general-purpose applications where you want to combine multiple search results without needing to manually tune the weights of different scoring systems.

Why use keyword search if I already have vector embeddings?

Vector search often struggles with specific product IDs, acronyms, or rare technical terms that keyword-based search handles perfectly.

What is hybrid search in the context of RAG?

Hybrid search combines semantic vector search (understanding meaning) with traditional keyword search (exact matching) to ensure the most relevant documents are retrieved for an LLM.

Hybrid Search Implementation

Name: Hybrid Search Implementation
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Combines semantic vector similarity with keyword-based retrieval to maximize search recall and accuracy in AI applications.

About

This skill provides standardized patterns for implementing hybrid search architectures, which are essential for robust Retrieval-Augmented Generation (RAG) systems. It bridges the gap between semantic understanding and exact keyword matching by offering production-ready templates for Reciprocal Rank Fusion (RRF), linear weighting, and cross-encoder reranking. Whether you are working with PostgreSQL (pgvector) or Elasticsearch, this skill ensures your search engine handles domain-specific terminology and nuances that pure vector search might miss, leading to more relevant and reliable context retrieval.

Key Features

Normalization strategies for combining disparate search scores into a single ranked list.
Production-grade implementation templates for PostgreSQL with pgvector and full-text search.
1 GitHub stars
High-accuracy reranking patterns using neural Cross-Encoders.
Advanced ranking fusion methods including Reciprocal Rank Fusion (RRF) and linear weighting.
Elasticsearch hybrid search configurations for dense vector and BM25 integration.

Use Cases

Optimizing RAG systems for improved context retrieval and answer quality.
Enhancing domain-specific search where technical jargon requires exact lexical matching.
Building enterprise search engines that handle both semantic intent and specific SKU/code identifiers.

About

Key Features

Normalization strategies for combining disparate search scores into a single ranked list.
Production-grade implementation templates for PostgreSQL with pgvector and full-text search.
1 GitHub stars
High-accuracy reranking patterns using neural Cross-Encoders.
Advanced ranking fusion methods including Reciprocal Rank Fusion (RRF) and linear weighting.
Elasticsearch hybrid search configurations for dense vector and BM25 integration.

Use Cases

Optimizing RAG systems for improved context retrieval and answer quality.
Enhancing domain-specific search where technical jargon requires exact lexical matching.
Building enterprise search engines that handle both semantic intent and specific SKU/code identifiers.