Handle serialization

frigate/comms/embeddings_updater.py

@@ -52,7 +52,7 @@ class EmbeddingsRequestor:
         self.socket = self.context.socket(zmq.REQ)
         self.socket.connect(SOCKET_REP_REQ)
 
-    def send_data(self, topic: str, data: any) -> any:
+    def send_data(self, topic: str, data: any) -> str:
         """Sends data and then waits for reply."""
         self.socket.send_json((topic, data))
         return self.socket.recv_json()

frigate/embeddings/__init__.py

@@ -1,6 +1,5 @@
 """SQLite-vec embeddings database."""
 
-import base64
 import json
 import logging
 import multiprocessing as mp
@@ -112,18 +111,20 @@ class EmbeddingsContext:
             row = cursor.fetchone() if cursor else None
 
             if row:
-                query_embedding = deserialize(
-                    row[0]
-                )  # Deserialize the thumbnail embedding
+                query_embedding = row[0]
             else:
                 # If no embedding found, generate it and return it
-                query_embedding = self.requestor.send_data(
-                    EmbeddingsRequestEnum.embed_thumbnail,
-                    {"id": query.id, "thumbnail": query.thumbnail},
+                query_embedding = serialize(
+                    self.requestor.send_data(
+                        EmbeddingsRequestEnum.embed_thumbnail.value,
+                        {"id": query.id, "thumbnail": query.thumbnail},
+                    )
                 )
         else:
-            query_embedding = self.requestor.send_data(
-                EmbeddingsRequestEnum.generate_search, query
+            query_embedding = serialize(
+                self.requestor.send_data(
+                    EmbeddingsRequestEnum.generate_search.value, query
+                )
             )
 
         sql_query = """
@@ -145,11 +146,7 @@ class EmbeddingsContext:
         # when it's implemented, we can use cosine similarity
         sql_query += " ORDER BY distance"
 
-        parameters = (
-            [serialize(query_embedding)] + event_ids
-            if event_ids
-            else [serialize(query_embedding)]
-        )
+        parameters = [query_embedding] + event_ids if event_ids else [query_embedding]
 
         results = self.db.execute_sql(sql_query, parameters).fetchall()
 
@@ -158,8 +155,10 @@ class EmbeddingsContext:
     def search_description(
         self, query_text: str, event_ids: list[str] = None
     ) -> list[tuple[str, float]]:
-        query_embedding = self.requestor.send_data(
-            EmbeddingsRequestEnum.generate_search, query_text
+        query_embedding = serialize(
+            self.requestor.send_data(
+                EmbeddingsRequestEnum.generate_search.value, query_text
+            )
         )
 
         # Prepare the base SQL query
@@ -182,11 +181,7 @@ class EmbeddingsContext:
         # when it's implemented, we can use cosine similarity
         sql_query += " ORDER BY distance"
 
-        parameters = (
-            [serialize(query_embedding)] + event_ids
-            if event_ids
-            else [serialize(query_embedding)]
-        )
+        parameters = [query_embedding] + event_ids if event_ids else [query_embedding]
 
         results = self.db.execute_sql(sql_query, parameters).fetchall()
 
@@ -194,6 +189,6 @@ class EmbeddingsContext:
 
     def update_description(self, event_id: str, description: str) -> None:
         self.requestor.send_data(
-            EmbeddingsRequestEnum.embed_description,
+            EmbeddingsRequestEnum.embed_description.value,
             {"id": event_id, "description": description},
         )

frigate/embeddings/maintainer.py

@@ -24,6 +24,7 @@ from frigate.const import CLIPS_DIR, UPDATE_EVENT_DESCRIPTION
 from frigate.events.types import EventTypeEnum
 from frigate.genai import get_genai_client
 from frigate.models import Event
+from frigate.util.builtin import serialize
 from frigate.util.image import SharedMemoryFrameManager, calculate_region
 
 from .embeddings import Embeddings
@@ -75,16 +76,20 @@ class EmbeddingMaintainer(threading.Thread):
     def _process_requests(self) -> None:
         """Process embeddings requests"""
 
-        def handle_request(topic: str, data: str) -> any:
-            if topic == EmbeddingsRequestEnum.embed_description:
-                return self.embeddings.upsert_description(
-                    data["id"], data["description"]
+        def handle_request(topic: str, data: str) -> str:
+            if topic == EmbeddingsRequestEnum.embed_description.value:
+                return serialize(
+                    self.embeddings.upsert_description(data["id"], data["description"]),
+                    pack=False,
                 )
-            elif topic == EmbeddingsRequestEnum.embed_thumbnail:
+            elif topic == EmbeddingsRequestEnum.embed_thumbnail.value:
                 thumbnail = base64.b64decode(data["thumbnail"])
-                return self.embeddings.upsert_thumbnail(data["id"], thumbnail)
-            elif topic == EmbeddingsRequestEnum.generate_search:
-                return self.embeddings.text_embedding([data])[0]
+                return serialize(
+                    self.embeddings.upsert_thumbnail(data["id"], thumbnail),
+                    pack=False,
+                )
+            elif topic == EmbeddingsRequestEnum.generate_search.value:
+                return serialize(self.embeddings.text_embedding([data])[0], pack=False)
 
         self.embeddings_responder.check_for_request(handle_request)
 

frigate/util/builtin.py

@@ -345,7 +345,7 @@ def generate_color_palette(n):
     return colors
 
 
-def serialize(vector: Union[list[float], np.ndarray, float]) -> bytes:
+def serialize(vector: Union[list[float], np.ndarray, float], pack: bool = True) -> bytes:
     """Serializes a list of floats, numpy array, or single float into a compact "raw bytes" format"""
     if isinstance(vector, np.ndarray):
         # Convert numpy array to list of floats
@@ -359,7 +359,10 @@ def serialize(vector: Union[list[float], np.ndarray, float]) -> bytes:
         )
 
     try:
-        return struct.pack("%sf" % len(vector), *vector)
+        if pack:
+            return struct.pack("%sf" % len(vector), *vector)
+        else:
+            return vector
     except struct.error as e:
         raise ValueError(f"Failed to pack vector: {e}. Vector: {vector}")