blakeblackshear.frigate/frigate/db/sqlitevecq.py
2024-11-16 07:49:14 -07:00

69 lines
2.6 KiB
Python

import sqlite3
from playhouse.sqliteq import SqliteQueueDatabase
class SqliteVecQueueDatabase(SqliteQueueDatabase):
def __init__(self, *args, load_vec_extension: bool = False, **kwargs) -> None:
self.load_vec_extension: bool = load_vec_extension
# no extension necessary, sqlite will load correctly for each platform
self.sqlite_vec_path = "/usr/local/lib/vec0"
super().__init__(*args, **kwargs)
def _connect(self, *args, **kwargs) -> sqlite3.Connection:
conn: sqlite3.Connection = super()._connect(*args, **kwargs)
if self.load_vec_extension:
self._load_vec_extension(conn)
return conn
def _load_vec_extension(self, conn: sqlite3.Connection) -> None:
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)
def delete_embeddings_face(self, face_ids: list[str]) -> None:
ids = ",".join(["?" for _ in face_ids])
self.execute_sql(f"DELETE FROM vec_faces WHERE id IN ({ids})", face_ids)
def drop_embeddings_tables(self) -> None:
self.execute_sql("""
DROP TABLE vec_descriptions;
""")
self.execute_sql("""
DROP TABLE vec_thumbnails;
""")
self.execute_sql("""
DROP TABLE vec_faces;
""")
def create_embeddings_tables(self, face_recognition: bool) -> None:
"""Create vec0 virtual table for embeddings"""
self.execute_sql("""
CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
id TEXT PRIMARY KEY,
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] distance_metric=cosine
);
""")
if face_recognition:
self.execute_sql("""
CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
id TEXT PRIMARY KEY,
face_embedding FLOAT[512] distance_metric=cosine
);
""")