Files
2026-07-13 12:10:44 +08:00

73 lines
2.6 KiB
Python

import re
import sqlite3
from typing import Any
from playhouse.sqliteq import SqliteQueueDatabase
class SqliteVecQueueDatabase(SqliteQueueDatabase):
def __init__(
self, *args: Any, load_vec_extension: bool = False, **kwargs: Any
) -> 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: Any, **kwargs: Any) -> sqlite3.Connection:
conn: sqlite3.Connection = super()._connect(*args, **kwargs) # type: ignore[misc]
if self.load_vec_extension:
self._load_vec_extension(conn)
# register REGEXP support
self._register_regexp(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 _register_regexp(self, conn: sqlite3.Connection) -> None:
def regexp(expr: str, item: str | None) -> bool:
if item is None:
return False
try:
return re.search(expr, item) is not None
except re.error:
return False
conn.create_function("REGEXP", 2, regexp)
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 drop_embeddings_tables(self) -> None:
self.execute_sql("""
DROP TABLE vec_descriptions;
""")
self.execute_sql("""
DROP TABLE vec_thumbnails;
""")
def create_embeddings_tables(self) -> 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
);
""")