diff --git a/docs/docs/configuration/semantic_search.md b/docs/docs/configuration/semantic_search.md
index 498d1ab67..a51301777 100644
--- a/docs/docs/configuration/semantic_search.md
+++ b/docs/docs/configuration/semantic_search.md
@@ -50,8 +50,8 @@ Differently weighted versions of the Jina models are available and can be select
 ```yaml
 semantic_search:
   enabled: True
-  local_model: "jinav1"
-  local_model_size: small
+  model: "jinav1"
+  model_size: small
 ```
 
 - Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable.
@@ -68,8 +68,8 @@ To use the V2 model, update the `model` parameter in your config:
 ```yaml
 semantic_search:
   enabled: True
-  local_model: "jinav2"
-  local_model_size: large
+  model: "jinav2"
+  model_size: large
 ```
 
 For most users, especially native English speakers, the V1 model remains the recommended choice.
@@ -123,7 +123,7 @@ The CLIP models are downloaded in ONNX format, and the `large` model can be acce
 ```yaml
 semantic_search:
   enabled: True
-  local_model_size: large
+  model_size: large
   # Optional, if using the 'large' model in a multi-GPU installation
   device: 0
 ```
diff --git a/frigate/config/classification.py b/frigate/config/classification.py
index e77eb7a3f..c2f03810d 100644
--- a/frigate/config/classification.py
+++ b/frigate/config/classification.py
@@ -159,11 +159,11 @@ class SemanticSearchConfig(FrigateBaseModel):
         default=SemanticSearchProviderEnum.local,
         title="The semantic search provider to use.",
     )
-    local_model: Optional[SemanticSearchModelEnum] = Field(
+    model: Optional[SemanticSearchModelEnum] = Field(
         default=SemanticSearchModelEnum.jinav1,
         title="The local CLIP model to use for semantic search.",
     )
-    local_model_size: str = Field(
+    model_size: str = Field(
         default="small", title="The size of the local embeddings model used."
     )
     device: Optional[str] = Field(
diff --git a/frigate/embeddings/embeddings.py b/frigate/embeddings/embeddings.py
index 7e20c3409..fbef10766 100644
--- a/frigate/embeddings/embeddings.py
+++ b/frigate/embeddings/embeddings.py
@@ -108,13 +108,13 @@ class Embeddings:
                     },
                 )
 
-            if self.config.semantic_search.local_model == SemanticSearchModelEnum.jinav2:
+            if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2:
                 # Single JinaV2Embedding instance for both text and vision
                 self.embedding = JinaV2Embedding(
-                    model_size=self.config.semantic_search.local_model_size,
+                    model_size=self.config.semantic_search.model_size,
                     requestor=self.requestor,
                     device=config.semantic_search.device
-                    or ("GPU" if config.semantic_search.local_model_size == "large" else "CPU"),
+                    or ("GPU" if config.semantic_search.model_size == "large" else "CPU"),
                 )
                 self.text_embedding = lambda input_data: self.embedding(
                     input_data, embedding_type="text"
@@ -124,15 +124,15 @@ class Embeddings:
                 )
             else:  # Default to jinav1
                 self.text_embedding = JinaV1TextEmbedding(
-                    model_size=config.semantic_search.local_model_size,
+                    model_size=config.semantic_search.model_size,
                     requestor=self.requestor,
                     device="CPU",
                 )
                 self.vision_embedding = JinaV1ImageEmbedding(
-                    model_size=config.semantic_search.local_model_size,
+                    model_size=config.semantic_search.model_size,
                     requestor=self.requestor,
                     device=config.semantic_search.device
-                    or ("GPU" if config.semantic_search.local_model_size == "large" else "CPU"),
+                    or ("GPU" if config.semantic_search.model_size == "large" else "CPU"),
                 )
         else:
             self.remote_embedding_client = get_embedding_client(self.config)