# Database

LanceDB-based storage for face embeddings and attributes.

## What is ReID Database?

`ReID_Database` is the underlying storage layer for face embeddings and person attributes in `degirum-face`. It uses LanceDB to store:

* Face embedding vectors for similarity search
* Person attributes (names, metadata)
* Associated face images
* Model metadata for validation

The database is automatically managed by `FaceRecognizer` and `FaceTracker` - you typically don't instantiate it directly.

## When to Use

**Automatic usage** - The database is created and managed when you initialize `FaceRecognizer` or `FaceTracker`:

```python
# Database created automatically at db_path
recognizer = degirum_face.FaceRecognizer(
    config=degirum_face.FaceRecognizerConfig(
        db_path="./my_faces.lance",  # Database location
        # ...
    )
)
```

**Direct usage** - Only needed for:

* Custom database operations
* Database migration/backup tools
* Advanced batch processing
* Direct embedding management

## How It Works

The database uses LanceDB with four internal tables:

| Table        | Purpose                                      |
| ------------ | -------------------------------------------- |
| `embeddings` | Face embedding vectors with image references |
| `attributes` | Person metadata (names, IDs)                 |
| `images`     | Face image storage (PNG format)              |
| `_metadata`  | Model validation metadata                    |

**Key features:**

* **Vector search** - Fast similarity search using cosine distance
* **Deduplication** - Automatic embedding deduplication via hashing
* **Thread-safe** - Safe for concurrent access
* **Model validation** - Ensures embeddings from same model
* **Connection pooling** - Single connection per database path

See [Configuration Guide](/face-recognition/guides/overview-3/configuration.md) for database initialization options.

See [Methods Reference](/face-recognition/guides/overview-3/methods.md) for complete API.


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