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hybrid-search-implementation

de wshobson

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

Resumen

SKILL.mdLeída en 46891e7

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
MethodDescriptionBest For
RRFReciprocal Rank FusionGeneral purpose
LinearWeighted sum of scoresTunable balance
Cross-encoderRerank with neural modelHighest quality
CascadeFilter then rerankEfficiency

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

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Instálala a mano

Deska ejecuta esto por ti, para cada agente que elijas. Para ejecutarlo tú en una terminal:

npx skills add wshobson/agents --skill hybrid-search-implementation

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