先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
为 RAG 系统实现 RRF 融合：并行取向量与 BM25 结果，按 1/(k+rank) 合并后输出前十条。
输出
预期交付：融合排序后的文档列表，含各来源分数与重排分数。（编辑说明，非实际运行结果）
方法说明
---
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