Embeddings normalization fixes (#14284)

* Use cosine distance metric for vec tables

* Only apply normalization to multi modal searches

* Catch possible edge case in stddev calc

* Use sigmoid function for normalization for multi modal searches only

* Ensure we get model state on initial page load

* Only save stats for multi modal searches and only use cosine similarity for image -> image search

frigate/api/event.py

@@ -473,12 +473,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
             )
 
         thumb_result = context.search_thumbnail(search_event)
-        thumb_ids = dict(
+        thumb_ids = {result[0]: result[1] for result in thumb_result}
-            zip(
-                [result[0] for result in thumb_result],
-                context.thumb_stats.normalize([result[1] for result in thumb_result]),
-            )
-        )
         search_results = {
             event_id: {"distance": distance, "source": "thumbnail"}
             for event_id, distance in thumb_ids.items()
@@ -486,15 +481,18 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
     else:
         search_types = search_type.split(",")
 
+        # only save stats for multi-modal searches
+        save_stats = "thumbnail" in search_types and "description" in search_types
+
         if "thumbnail" in search_types:
             thumb_result = context.search_thumbnail(query)
-            thumb_ids = dict(
+
-                zip(
+            thumb_distances = context.thumb_stats.normalize(
-                    [result[0] for result in thumb_result],
+                [result[1] for result in thumb_result], save_stats
-                    context.thumb_stats.normalize(
-                        [result[1] for result in thumb_result]
-                    ),
             )
+
+            thumb_ids = dict(
+                zip([result[0] for result in thumb_result], thumb_distances)
             )
             search_results.update(
                 {
@@ -505,12 +503,13 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
 
         if "description" in search_types:
             desc_result = context.search_description(query)
-            desc_ids = dict(
+
-                zip(
+            desc_distances = context.desc_stats.normalize(
-                    [result[0] for result in desc_result],
+                [result[1] for result in desc_result], save_stats
-                    context.desc_stats.normalize([result[1] for result in desc_result]),
-                )
             )
+
+            desc_ids = dict(zip([result[0] for result in desc_result], desc_distances))
+
             for event_id, distance in desc_ids.items():
                 if (
                     event_id not in search_results

frigate/db/sqlitevecq.py

@@ -42,12 +42,12 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         self.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
-                thumbnail_embedding FLOAT[768]
+                thumbnail_embedding FLOAT[768] distance_metric=cosine
             );
         """)
         self.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                 id TEXT PRIMARY KEY,
-                description_embedding FLOAT[768]
+                description_embedding FLOAT[768] distance_metric=cosine
             );
         """)

frigate/embeddings/util.py

@@ -20,9 +20,10 @@ class ZScoreNormalization:
 
     @property
     def stddev(self):
-        return math.sqrt(self.variance)
+        return math.sqrt(self.variance) if self.variance > 0 else 0.0
 
-    def normalize(self, distances: list[float]):
+    def normalize(self, distances: list[float], save_stats: bool):
+        if save_stats:
             self._update(distances)
         if self.stddev == 0:
             return distances

web/src/pages/Explore.tsx

@@ -2,6 +2,7 @@ import {
   useEmbeddingsReindexProgress,
   useEventUpdate,
   useModelState,
+  useWs,
 } from "@/api/ws";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
 import AnimatedCircularProgressBar from "@/components/ui/circular-progress-bar";
@@ -202,6 +203,14 @@ export default function Explore() {
 
   // model states
 
+  const { send: sendCommand } = useWs("model_state", "modelState");
+
+  useEffect(() => {
+    sendCommand("modelState");
+    // only run on mount
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
   const { payload: textModelState } = useModelState(
     "jinaai/jina-clip-v1-text_model_fp16.onnx",
   );

web/src/views/search/SearchView.tsx

@@ -187,13 +187,19 @@ export default function SearchView({
     }
   }, [searchResults, searchDetail]);
 
-  // confidence score - probably needs tweaking
+  // confidence score
 
   const zScoreToConfidence = (score: number) => {
-    // Sigmoid function: 1 / (1 + e^x)
+    // Normalizing is not needed for similarity searches
-    const confidence = 1 / (1 + Math.exp(score));
+    // Sigmoid function for normalized: 1 / (1 + e^x)
+    // Cosine for similarity
+    if (searchFilter) {
+      const notNormalized = searchFilter?.search_type?.includes("similarity");
+
+      const confidence = notNormalized ? 1 - score : 1 / (1 + Math.exp(score));
 
       return Math.round(confidence * 100);
+    }
   };
 
   const hasExistingSearch = useMemo(