diff --git a/frigate/embeddings/embeddings.py b/frigate/embeddings/embeddings.py
index 8707f6f37..5351912fd 100644
--- a/frigate/embeddings/embeddings.py
+++ b/frigate/embeddings/embeddings.py
@@ -123,73 +123,97 @@ class Embeddings:
             device="GPU" if config.model_size == "large" else "CPU",
         )
 
-    def upsert_thumbnail(self, event_id: str, thumbnail: bytes) -> ndarray:
+    def embed_thumbnail(
+        self, event_id: str, thumbnail: bytes, upsert: bool = True
+    ) -> ndarray:
+        """Embed thumbnail and optionally insert into DB.
+
+        @param: event_id in Events DB
+        @param: thumbnail bytes in jpg format
+        @param: upsert If embedding should be upserted into vec DB
+        """
         # Convert thumbnail bytes to PIL Image
         embedding = self.vision_embedding([thumbnail])[0]
 
-        self.db.execute_sql(
-            """
-            INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-            VALUES(?, ?)
-            """,
-            (event_id, serialize(embedding)),
-        )
+        if upsert:
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
+            )
 
         return embedding
 
-    def batch_upsert_thumbnail(self, event_thumbs: dict[str, bytes]) -> list[ndarray]:
+    def batch_embed_thumbnail(
+        self, event_thumbs: dict[str, bytes], upsert: bool = True
+    ) -> list[ndarray]:
+        """Embed thumbnails and optionally insert into DB.
+
+        @param: event_thumbs Map of Event IDs in DB to thumbnail bytes in jpg format
+        @param: upsert If embedding should be upserted into vec DB
+        """
         ids = list(event_thumbs.keys())
         embeddings = self.vision_embedding(list(event_thumbs.values()))
 
-        items = []
+        if upsert:
+            items = []
 
-        for i in range(len(ids)):
-            items.append(ids[i])
-            items.append(serialize(embeddings[i]))
+            for i in range(len(ids)):
+                items.append(ids[i])
+                items.append(serialize(embeddings[i]))
+
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                VALUES {}
+                """.format(", ".join(["(?, ?)"] * len(ids))),
+                items,
+            )
 
-        self.db.execute_sql(
-            """
-            INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-            VALUES {}
-            """.format(", ".join(["(?, ?)"] * len(ids))),
-            items,
-        )
         return embeddings
 
-    def upsert_description(self, event_id: str, description: str) -> ndarray:
+    def embed_description(
+        self, event_id: str, description: str, upsert: bool = True
+    ) -> ndarray:
         embedding = self.text_embedding([description])[0]
-        self.db.execute_sql(
-            """
-            INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-            VALUES(?, ?)
-            """,
-            (event_id, serialize(embedding)),
-        )
+
+        if upsert:
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
+            )
 
         return embedding
 
-    def batch_upsert_description(self, event_descriptions: dict[str, str]) -> ndarray:
+    def batch_embed_description(
+        self, event_descriptions: dict[str, str], upsert: bool = True
+    ) -> ndarray:
         # upsert embeddings one by one to avoid token limit
         embeddings = []
 
         for desc in event_descriptions.values():
             embeddings.append(self.text_embedding([desc])[0])
 
-        ids = list(event_descriptions.keys())
+        if upsert:
+            ids = list(event_descriptions.keys())
+            items = []
 
-        items = []
+            for i in range(len(ids)):
+                items.append(ids[i])
+                items.append(serialize(embeddings[i]))
 
-        for i in range(len(ids)):
-            items.append(ids[i])
-            items.append(serialize(embeddings[i]))
-
-        self.db.execute_sql(
-            """
-            INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-            VALUES {}
-            """.format(", ".join(["(?, ?)"] * len(ids))),
-            items,
-        )
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                VALUES {}
+                """.format(", ".join(["(?, ?)"] * len(ids))),
+                items,
+            )
 
         return embeddings
 
@@ -256,10 +280,10 @@ class Embeddings:
                 totals["processed_objects"] += 1
 
             # run batch embedding
-            self.batch_upsert_thumbnail(batch_thumbs)
+            self.batch_embed_thumbnail(batch_thumbs)
 
             if batch_descs:
-                self.batch_upsert_description(batch_descs)
+                self.batch_embed_description(batch_descs)
 
             # report progress every batch so we don't spam the logs
             progress = (totals["processed_objects"] / total_events) * 100
diff --git a/frigate/embeddings/maintainer.py b/frigate/embeddings/maintainer.py
index 7ce63e7f8..1578a0fe3 100644
--- a/frigate/embeddings/maintainer.py
+++ b/frigate/embeddings/maintainer.py
@@ -86,7 +86,7 @@ class EmbeddingMaintainer(threading.Thread):
             try:
                 if topic == EmbeddingsRequestEnum.embed_description.value:
                     return serialize(
-                        self.embeddings.upsert_description(
+                        self.embeddings.embed_description(
                             data["id"], data["description"]
                         ),
                         pack=False,
@@ -94,7 +94,7 @@ class EmbeddingMaintainer(threading.Thread):
                 elif topic == EmbeddingsRequestEnum.embed_thumbnail.value:
                     thumbnail = base64.b64decode(data["thumbnail"])
                     return serialize(
-                        self.embeddings.upsert_thumbnail(data["id"], thumbnail),
+                        self.embeddings.embed_thumbnail(data["id"], thumbnail),
                         pack=False,
                     )
                 elif topic == EmbeddingsRequestEnum.generate_search.value:
@@ -270,7 +270,7 @@ class EmbeddingMaintainer(threading.Thread):
 
     def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None:
         """Embed the thumbnail for an event."""
-        self.embeddings.upsert_thumbnail(event_id, thumbnail)
+        self.embeddings.embed_thumbnail(event_id, thumbnail)
 
     def _embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
         """Embed the description for an event."""
@@ -290,8 +290,8 @@ class EmbeddingMaintainer(threading.Thread):
             {"id": event.id, "description": description},
         )
 
-        # Encode the description
-        self.embeddings.upsert_description(event.id, description)
+        # Embed the description
+        self.embeddings.embed_description(event.id, description)
 
         logger.debug(
             "Generated description for %s (%d images): %s",