mirror of
https://github.com/blakeblackshear/frigate.git
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8a8a0c7dec
* 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
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
import sqlite3
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from playhouse.sqliteq import SqliteQueueDatabase
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class SqliteVecQueueDatabase(SqliteQueueDatabase):
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def __init__(self, *args, load_vec_extension: bool = False, **kwargs) -> None:
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self.load_vec_extension: bool = load_vec_extension
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# no extension necessary, sqlite will load correctly for each platform
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self.sqlite_vec_path = "/usr/local/lib/vec0"
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super().__init__(*args, **kwargs)
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def _connect(self, *args, **kwargs) -> sqlite3.Connection:
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conn: sqlite3.Connection = super()._connect(*args, **kwargs)
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if self.load_vec_extension:
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self._load_vec_extension(conn)
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return conn
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def _load_vec_extension(self, conn: sqlite3.Connection) -> None:
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conn.enable_load_extension(True)
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conn.load_extension(self.sqlite_vec_path)
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conn.enable_load_extension(False)
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def delete_embeddings_thumbnail(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_thumbnails WHERE id IN ({ids})", event_ids)
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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] distance_metric=cosine
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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] distance_metric=cosine
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);
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""")
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