hybrid-search-implementation

SKILL.md

Hybrid Search Implementation

Patterns for combining vector similarity and keyword-based search.

When to Use This Skill

Building RAG systems with improved recall

Combining semantic understanding with exact matching

Handling queries with specific terms (names, codes)

Improving search for domain-specific vocabulary

When pure vector search misses keyword matches

Core Concepts

1. Hybrid Search Architecture
Query → ┬─► Vector Search ──► Candidates ─┐
        │                                  │
        └─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results

2. Fusion Methods

Method
Description
Best For

RRF
Reciprocal Rank Fusion
General purpose

Linear
Weighted sum of scores
Tunable balance

Cross-encoder
Rerank with neural model
Highest quality

Cascade
Filter then rerank
Efficiency

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

Tune weights empirically - Test on your data

Use RRF for simplicity - Works well without tuning

Add reranking - Significant quality improvement

Log both scores - Helps with debugging

A/B test - Measure real user impact

Don'ts

Don't assume one size fits all - Different queries need different weights

Don't skip keyword search - Handles exact matches better

Don't over-fetch - Balance recall vs latency

Don't ignore edge cases - Empty results, single word queries