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hybrid-search-implementation

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

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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
Repository
wshobson/agents
Commit

5cc2549

Last updated
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