Restructure embeddings

frigate/api/event.py

@@ -472,7 +472,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
                 status_code=404,
             )
 
-        thumb_result = context.embeddings.search_thumbnail(search_event)
+        thumb_result = context.search_thumbnail(search_event)
         thumb_ids = dict(
             zip(
                 [result[0] for result in thumb_result],
@@ -487,7 +487,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
         search_types = search_type.split(",")
 
         if "thumbnail" in search_types:
-            thumb_result = context.embeddings.search_thumbnail(query)
+            thumb_result = context.search_thumbnail(query)
             thumb_ids = dict(
                 zip(
                     [result[0] for result in thumb_result],
@@ -504,7 +504,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
             )
 
         if "description" in search_types:
-            desc_result = context.embeddings.search_description(query)
+            desc_result = context.search_description(query)
             desc_ids = dict(
                 zip(
                     [result[0] for result in desc_result],
@@ -1033,8 +1033,8 @@ def delete_event(request: Request, event_id: str):
     # If semantic search is enabled, update the index
     if request.app.frigate_config.semantic_search.enabled:
         context: EmbeddingsContext = request.app.embeddings
-        context.embeddings.delete_thumbnail(id=[event_id])
+        context.db.delete_embeddings_thumbnail(id=[event_id])
-        context.embeddings.delete_description(id=[event_id])
+        context.db.delete_embeddings_description(id=[event_id])
     return JSONResponse(
         content=({"success": True, "message": "Event " + event_id + " deleted"}),
         status_code=200,

frigate/db/sqlitevecq.py

@@ -20,3 +20,15 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         conn.enable_load_extension(True)
         conn.load_extension(self.sqlite_vec_path)
         conn.enable_load_extension(False)
+
+    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
+        ids = ",".join(["?" for _ in event_ids])
+        self.execute_sql(
+            f"DELETE FROM vec_thumbnails WHERE id IN ({ids})", event_ids
+        )
+
+    def delete_embeddings_description(self, event_ids: list[str]) -> None:
+        ids = ",".join(["?" for _ in event_ids])
+        self.execute_sql(
+            f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids
+        )

frigate/embeddings/__init__.py

@@ -1,5 +1,6 @@
 """SQLite-vec embeddings database."""
 
+import base64
 import json
 import logging
 import multiprocessing as mp
@@ -7,7 +8,7 @@ import os
 import signal
 import threading
 from types import FrameType
-from typing import Optional
+from typing import Optional, Union
 
 from setproctitle import setproctitle
 
@@ -15,6 +16,7 @@ from frigate.config import FrigateConfig
 from frigate.const import CONFIG_DIR
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
+from frigate.util.builtin import deserialize, serialize
 from frigate.util.services import listen
 
 from .embeddings import Embeddings
@@ -71,7 +73,7 @@ def manage_embeddings(config: FrigateConfig) -> None:
 
 class EmbeddingsContext:
     def __init__(self, config: FrigateConfig, db: SqliteVecQueueDatabase):
-        self.embeddings = Embeddings(config.semantic_search, db)
+        self.db = db
         self.thumb_stats = ZScoreNormalization()
         self.desc_stats = ZScoreNormalization()
 
@@ -92,3 +94,91 @@ class EmbeddingsContext:
         }
         with open(os.path.join(CONFIG_DIR, ".search_stats.json"), "w") as f:
             json.dump(contents, f)
+
+    def search_thumbnail(
+        self, query: Union[Event, str], event_ids: list[str] = None
+    ) -> list[tuple[str, float]]:
+        if query.__class__ == Event:
+            cursor = self.db.execute_sql(
+                """
+                SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
+                """,
+                [query.id],
+            )
+
+            row = cursor.fetchone() if cursor else None
+
+            if row:
+                query_embedding = deserialize(
+                    row[0]
+                )  # Deserialize the thumbnail embedding
+            else:
+                # If no embedding found, generate it and return it
+                thumbnail = base64.b64decode(query.thumbnail)
+                query_embedding = self.upsert_thumbnail(query.id, thumbnail)
+        else:
+            query_embedding = self.text_embedding([query])[0]
+
+        sql_query = """
+            SELECT
+                id,
+                distance
+            FROM vec_thumbnails
+            WHERE thumbnail_embedding MATCH ?
+                AND k = 100
+        """
+
+        # Add the IN clause if event_ids is provided and not empty
+        # this is the only filter supported by sqlite-vec as of 0.1.3
+        # but it seems to be broken in this version
+        if event_ids:
+            sql_query += " AND id IN ({})".format(",".join("?" * len(event_ids)))
+
+        # order by distance DESC is not implemented in this version of sqlite-vec
+        # 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)]
+        )
+
+        results = self.db.execute_sql(sql_query, parameters).fetchall()
+
+        return results
+
+    def search_description(
+        self, query_text: str, event_ids: list[str] = None
+    ) -> list[tuple[str, float]]:
+        query_embedding = self.text_embedding([query_text])[0]
+
+        # Prepare the base SQL query
+        sql_query = """
+            SELECT
+                id,
+                distance
+            FROM vec_descriptions
+            WHERE description_embedding MATCH ?
+                AND k = 100
+        """
+
+        # Add the IN clause if event_ids is provided and not empty
+        # this is the only filter supported by sqlite-vec as of 0.1.3
+        # but it seems to be broken in this version
+        if event_ids:
+            sql_query += " AND id IN ({})".format(",".join("?" * len(event_ids)))
+
+        # order by distance DESC is not implemented in this version of sqlite-vec
+        # 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)]
+        )
+
+        results = self.db.execute_sql(sql_query, parameters).fetchall()
+
+        return results

frigate/embeddings/embeddings.py

@@ -3,11 +3,8 @@
 import base64
 import io
 import logging
-import struct
 import time
-from typing import List, Tuple, Union
 
-import numpy as np
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -17,6 +14,7 @@ from frigate.const import UPDATE_MODEL_STATE
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
 from frigate.types import ModelStatusTypesEnum
+from frigate.util.builtin import serialize
 
 from .functions.onnx import GenericONNXEmbedding
 
@@ -54,30 +52,6 @@ def get_metadata(event: Event) -> dict:
     )
 
 
-def serialize(vector: Union[List[float], np.ndarray, float]) -> 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
-        vector = vector.flatten().tolist()
-    elif isinstance(vector, (float, np.float32, np.float64)):
-        # Handle single float values
-        vector = [vector]
-    elif not isinstance(vector, list):
-        raise TypeError(
-            f"Input must be a list of floats, a numpy array, or a single float. Got {type(vector)}"
-        )
-
-    try:
-        return struct.pack("%sf" % len(vector), *vector)
-    except struct.error as e:
-        raise ValueError(f"Failed to pack vector: {e}. Vector: {vector}")
-
-
-def deserialize(bytes_data: bytes) -> List[float]:
-    """Deserializes a compact "raw bytes" format into a list of floats"""
-    return list(struct.unpack("%sf" % (len(bytes_data) // 4), bytes_data))
-
-
 class Embeddings:
     """SQLite-vec embeddings database."""
 
@@ -190,106 +164,6 @@ class Embeddings:
 
         return embedding
 
-    def delete_thumbnail(self, event_ids: List[str]) -> None:
-        ids = ",".join(["?" for _ in event_ids])
-        self.db.execute_sql(
-            f"DELETE FROM vec_thumbnails WHERE id IN ({ids})", event_ids
-        )
-
-    def delete_description(self, event_ids: List[str]) -> None:
-        ids = ",".join(["?" for _ in event_ids])
-        self.db.execute_sql(
-            f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids
-        )
-
-    def search_thumbnail(
-        self, query: Union[Event, str], event_ids: List[str] = None
-    ) -> List[Tuple[str, float]]:
-        if query.__class__ == Event:
-            cursor = self.db.execute_sql(
-                """
-                SELECT thumbnail_embedding FROM vec_thumbnails WHERE id = ?
-                """,
-                [query.id],
-            )
-
-            row = cursor.fetchone() if cursor else None
-
-            if row:
-                query_embedding = deserialize(
-                    row[0]
-                )  # Deserialize the thumbnail embedding
-            else:
-                # If no embedding found, generate it and return it
-                thumbnail = base64.b64decode(query.thumbnail)
-                query_embedding = self.upsert_thumbnail(query.id, thumbnail)
-        else:
-            query_embedding = self.text_embedding([query])[0]
-
-        sql_query = """
-            SELECT
-                id,
-                distance
-            FROM vec_thumbnails
-            WHERE thumbnail_embedding MATCH ?
-                AND k = 100
-        """
-
-        # Add the IN clause if event_ids is provided and not empty
-        # this is the only filter supported by sqlite-vec as of 0.1.3
-        # but it seems to be broken in this version
-        if event_ids:
-            sql_query += " AND id IN ({})".format(",".join("?" * len(event_ids)))
-
-        # order by distance DESC is not implemented in this version of sqlite-vec
-        # 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)]
-        )
-
-        results = self.db.execute_sql(sql_query, parameters).fetchall()
-
-        return results
-
-    def search_description(
-        self, query_text: str, event_ids: List[str] = None
-    ) -> List[Tuple[str, float]]:
-        query_embedding = self.text_embedding([query_text])[0]
-
-        # Prepare the base SQL query
-        sql_query = """
-            SELECT
-                id,
-                distance
-            FROM vec_descriptions
-            WHERE description_embedding MATCH ?
-                AND k = 100
-        """
-
-        # Add the IN clause if event_ids is provided and not empty
-        # this is the only filter supported by sqlite-vec as of 0.1.3
-        # but it seems to be broken in this version
-        if event_ids:
-            sql_query += " AND id IN ({})".format(",".join("?" * len(event_ids)))
-
-        # order by distance DESC is not implemented in this version of sqlite-vec
-        # 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)]
-        )
-
-        results = self.db.execute_sql(sql_query, parameters).fetchall()
-
-        return results
-
     def reindex(self) -> None:
         logger.info("Indexing event embeddings...")
 

frigate/util/builtin.py

@@ -8,10 +8,11 @@ import multiprocessing as mp
 import queue
 import re
 import shlex
+import struct
 import urllib.parse
 from collections.abc import Mapping
 from pathlib import Path
-from typing import Any, Optional, Tuple
+from typing import Any, Optional, Tuple, Union
 
 import numpy as np
 import pytz
@@ -342,3 +343,27 @@ def generate_color_palette(n):
         colors.append(interpolate(color1, color2, factor))
 
     return colors
+
+
+def serialize(vector: Union[list[float], np.ndarray, float]) -> 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
+        vector = vector.flatten().tolist()
+    elif isinstance(vector, (float, np.float32, np.float64)):
+        # Handle single float values
+        vector = [vector]
+    elif not isinstance(vector, list):
+        raise TypeError(
+            f"Input must be a list of floats, a numpy array, or a single float. Got {type(vector)}"
+        )
+
+    try:
+        return struct.pack("%sf" % len(vector), *vector)
+    except struct.error as e:
+        raise ValueError(f"Failed to pack vector: {e}. Vector: {vector}")
+
+
+def deserialize(bytes_data: bytes) -> list[float]:
+    """Deserializes a compact "raw bytes" format into a list of floats"""
+    return list(struct.unpack("%sf" % (len(bytes_data) // 4), bytes_data))