Hybrid Search Implementation

# 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