Agent Skills
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hybrid-search-implementation
GitHub结合向量与关键词搜索以提升检索召回率，适用于RAG系统及搜索引擎构建。提供RRF、线性加权等融合方法及最佳实践，解决单一搜索方式效果不足的问题。
Trigger Scenarios
实现RAG系统
构建搜索引擎
提升领域特定词汇检索效果
Install
npx skills add wshobson/agents --skill hybrid-search-implementation -g -y
SKILL.md
Frontmatter
{
"name": "hybrid-search-implementation",
"description": "Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall."
}
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
Version History
- 367cb6a Current 2026-08-20 06:52
