Refactor ONNX embedding class to use a base class and type-specific classes (#16703)

* Move onnx runner

* Build out base embedding

* Convert text embedding to separate class

* Move image embedding to separate

* Move LPR to separate class

* Remove mono embedding

* Simplify model downloading

* Reorganize jina v1 embeddings

* Cleanup

* Cleanup for review

frigate/embeddings/embeddings.py

@@ -22,7 +22,7 @@ from frigate.types import ModelStatusTypesEnum
 from frigate.util.builtin import serialize
 from frigate.util.path import get_event_thumbnail_bytes
 
-from .functions.onnx import GenericONNXEmbedding, ModelTypeEnum
+from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -97,36 +97,14 @@ class Embeddings:
                 },
             )
 
-        self.text_embedding = GenericONNXEmbedding(
-            model_name="jinaai/jina-clip-v1",
-            model_file="text_model_fp16.onnx",
-            tokenizer_file="tokenizer",
-            download_urls={
-                "text_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx",
-            },
+        self.text_embedding = JinaV1TextEmbedding(
             model_size=config.semantic_search.model_size,
-            model_type=ModelTypeEnum.text,
             requestor=self.requestor,
             device="CPU",
         )
 
-        model_file = (
-            "vision_model_fp16.onnx"
-            if self.config.semantic_search.model_size == "large"
-            else "vision_model_quantized.onnx"
-        )
-
-        download_urls = {
-            model_file: f"https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/{model_file}",
-            "preprocessor_config.json": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/preprocessor_config.json",
-        }
-
-        self.vision_embedding = GenericONNXEmbedding(
-            model_name="jinaai/jina-clip-v1",
-            model_file=model_file,
-            download_urls=download_urls,
+        self.vision_embedding = JinaV1ImageEmbedding(
             model_size=config.semantic_search.model_size,
-            model_type=ModelTypeEnum.vision,
             requestor=self.requestor,
             device="GPU" if config.semantic_search.model_size == "large" else "CPU",
         )