vector-index-tuning

# Vector Index Tuning

Guide to optimizing vector indexes for production performance.

## When to Use This Skill

- Tuning HNSW parameters
- Implementing quantization
- Optimizing memory usage
- Reducing search latency
- Balancing recall vs speed
- Scaling to billions of vectors

## Core Concepts

### 1. Index Type Selection

```
Data Size           Recommended Index
────────────────────────────────────────
< 10K vectors  →    Flat (exact search)
10K - 1M       →    HNSW
1M - 100M      →    HNSW + Quantization
> 100M         →    IVF + PQ or DiskANN
```

### 2. HNSW Parameters

| Parameter          | Default | Effect                                               |
| ------------------ | ------- | ---------------------------------------------------- |
| **M**              | 16      | Connections per node, ↑ = better recall, more memory |
| **efConstruction** | 100     | Build quality, ↑ = better index, slower build        |
| **efSearch**       | 50      | Search quality, ↑ = better recall, slower search     |

### 3. Quantization Types

```
Full Precision (FP32): 4 bytes × dimensions
Half Precision (FP16): 2 bytes × dimensions
INT8 Scalar:           1 byte × dimensions
Product Quantization:  ~32-64 bytes total
Binary:                dimensions/8 bytes
```

## Templates

### Template 1: HNSW Parameter Tuning

```python
import numpy as np
from typing import List, Tuple
import time

def benchmark_hnsw_parameters(
    vectors: np.ndarray,
    queries: np.ndarray,
    ground_truth: np.ndarray,
    m_values: List[int] = [8, 16, 32, 64],
    ef_construction_values: List[int] = [64, 128, 256],
    ef_search_values: List[int] = [32, 64, 128, 256]
) -> List[dict]:
    """Benchmark different HNSW configurations."""
    import hnswlib

    results = []
    dim = vectors.shape[1]
    n = vectors.shape[0]

    for m in m_values:
        for ef_construction in ef_construction_values:
            # Build index
            index = hnswlib.Index(space='cosine', dim=dim)
            i