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synced 2026-02-15 07:35:27 +03:00
Remove unneeded embeddings creations
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parent
8f5e05fc71
commit
157b1771ce
@ -28,3 +28,26 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
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def delete_embeddings_description(self, event_ids: list[str]) -> None:
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ids = ",".join(["?" for _ in event_ids])
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self.execute_sql(f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids)
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def drop_embeddings_tables(self) -> None:
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self.execute_sql("""
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DROP TABLE vec_descriptions;
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""")
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self.execute_sql("""
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DROP TABLE vec_thumbnails;
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""")
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def create_embeddings_tables(self) -> None:
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"""Create vec0 virtual table for embeddings"""
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
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id TEXT PRIMARY KEY,
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thumbnail_embedding FLOAT[768]
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);
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""")
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self.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
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id TEXT PRIMARY KEY,
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description_embedding FLOAT[768]
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);
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""")
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@ -19,7 +19,6 @@ from frigate.models import Event
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from frigate.util.builtin import serialize
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from frigate.util.services import listen
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from .embeddings import Embeddings
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from .maintainer import EmbeddingMaintainer
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from .util import ZScoreNormalization
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@ -57,12 +56,7 @@ def manage_embeddings(config: FrigateConfig) -> None:
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models = [Event]
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db.bind(models)
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embeddings = Embeddings(config.semantic_search, db)
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# Check if we need to re-index events
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if config.semantic_search.reindex:
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embeddings.reindex()
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print("creating embedding maintainer")
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maintainer = EmbeddingMaintainer(
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db,
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config,
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@ -63,7 +63,7 @@ class Embeddings:
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self.requestor = InterProcessRequestor()
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# Create tables if they don't exist
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self._create_tables()
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self.db.create_embeddings_tables()
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models = [
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"jinaai/jina-clip-v1-text_model_fp16.onnx",
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@ -110,31 +110,7 @@ class Embeddings:
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model_type="vision",
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device=self.config.device,
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)
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def _create_tables(self):
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# Create vec0 virtual table for thumbnail embeddings
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self.db.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
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id TEXT PRIMARY KEY,
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thumbnail_embedding FLOAT[768]
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);
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""")
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# Create vec0 virtual table for description embeddings
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self.db.execute_sql("""
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CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
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id TEXT PRIMARY KEY,
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description_embedding FLOAT[768]
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);
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""")
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def _drop_tables(self):
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self.db.execute_sql("""
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DROP TABLE vec_descriptions;
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""")
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self.db.execute_sql("""
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DROP TABLE vec_thumbnails;
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""")
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print("completed embeddings init")
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def upsert_thumbnail(self, event_id: str, thumbnail: bytes):
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# Convert thumbnail bytes to PIL Image
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@ -153,7 +129,6 @@ class Embeddings:
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def upsert_description(self, event_id: str, description: str):
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embedding = self.text_embedding([description])[0]
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self.db.execute_sql(
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"""
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INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
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@ -167,9 +142,9 @@ class Embeddings:
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def reindex(self) -> None:
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logger.info("Indexing tracked object embeddings...")
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self._drop_tables()
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self.db.drop_embeddings_tables()
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logger.debug("Dropped embeddings tables.")
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self._create_tables()
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self.db.create_embeddings_tables()
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logger.debug("Created embeddings tables.")
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st = time.time()
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@ -59,14 +59,17 @@ class GenericONNXEmbedding:
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self.feature_extractor = None
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self.session = None
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if not all(os.path.exists(os.path.join(self.download_path, n)) for n in self.download_urls.keys()):
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if not all(
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os.path.exists(os.path.join(self.download_path, n))
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for n in self.download_urls.keys()
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):
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print("starting model download")
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self.downloader = ModelDownloader(
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model_name=self.model_name,
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download_path=self.download_path,
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file_names=list(self.download_urls.keys())
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+ ([self.tokenizer_file] if self.tokenizer_file else []),
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download_func=self._download_model,
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model_name=self.model_name,
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download_path=self.download_path,
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file_names=list(self.download_urls.keys())
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+ ([self.tokenizer_file] if self.tokenizer_file else []),
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download_func=self._download_model,
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)
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self.downloader.ensure_model_files()
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else:
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@ -43,7 +43,14 @@ class EmbeddingMaintainer(threading.Thread):
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) -> None:
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super().__init__(name="embeddings_maintainer")
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self.config = config
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print("creating embeddings")
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self.embeddings = Embeddings(config.semantic_search, db)
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print("finished creating embeddings")
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# Check if we need to re-index events
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if config.semantic_search.reindex:
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self.embeddings.reindex()
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self.event_subscriber = EventUpdateSubscriber()
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self.event_end_subscriber = EventEndSubscriber()
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self.event_metadata_subscriber = EventMetadataSubscriber(
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@ -56,6 +63,7 @@ class EmbeddingMaintainer(threading.Thread):
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self.stop_event = stop_event
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self.tracked_events = {}
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self.genai_client = get_genai_client(config.genai)
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print("finished embed maintainer setup")
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def run(self) -> None:
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"""Maintain a SQLite-vec database for semantic search."""
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@ -11,7 +11,6 @@ from pathlib import Path
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from frigate.config import FrigateConfig
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from frigate.const import CLIPS_DIR
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from frigate.db.sqlitevecq import SqliteVecQueueDatabase
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from frigate.embeddings.embeddings import Embeddings
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from frigate.models import Event, Timeline
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logger = logging.getLogger(__name__)
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@ -34,9 +33,6 @@ class EventCleanup(threading.Thread):
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self.removed_camera_labels: list[str] = None
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self.camera_labels: dict[str, dict[str, any]] = {}
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if self.config.semantic_search.enabled:
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self.embeddings = Embeddings(self.config.semantic_search, self.db)
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def get_removed_camera_labels(self) -> list[Event]:
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"""Get a list of distinct labels for removed cameras."""
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if self.removed_camera_labels is None:
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