update enrichment docs to document the new device option.

docs/docs/configuration/face_recognition.md

@@ -73,6 +73,9 @@ Fine-tune face recognition with these optional parameters at the global level of
   - Default: `100`.
 - `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
   - Default: `True`.
+- `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
+  - Default: `None`.
+  - Note: This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)
 
 ## Usage
 

docs/docs/configuration/license_plate_recognition.md

@@ -67,9 +67,9 @@ Fine-tune the LPR feature using these optional parameters at the global level of
 - **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs.
   - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
   - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
-- **`device`**: Device to use to run license plate recognition models.
+- **`device`**: Device to use to run license plate detection *and* recognition models.
   - Default: `CPU`
-  - This can be `CPU` or `GPU`. For users without a model that detects license plates natively, using a GPU may increase performance of the models, especially the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
+  - This can be `CPU` or one of [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/). For users without a model that detects license plates natively, using a GPU may increase performance of the models, especially the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
 - **`model_size`**: The size of the model used to detect text on plates.
   - Default: `small`
   - This can be `small` or `large`. The `large` model uses an enhanced text detector and is more accurate at finding text on plates but slower than the `small` model. For most users, the small model is recommended. For users in countries with multiple lines of text on plates, the large model is recommended. Note that using the large model does not improve _text recognition_, but it may improve _text detection_.

docs/docs/configuration/semantic_search.md

@@ -78,17 +78,21 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 
 ### GPU Acceleration
 
-The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used.
+The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
 
 ```yaml
 semantic_search:
   enabled: True
   model_size: large
+  # Optional, if using the 'large' model in a multi GPU installation
+  device: 0 
 ```
 
 :::info
 
-If the correct build is used for your GPU and the `large` model is configured, then the GPU will be detected and used automatically.
+If the correct build is used for your GPU and the `large` model is configured, then the GPU will be detected and used automatically. 
+Specify the `device` option to target a specific GPU in a multi-GPU system (see [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)). 
+If you do not specify a device, the first available GPU will be used.
 
 See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.