blakeblackshear.frigate/frigate/db/sqlitevecq.py

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Use sqlite-vec extension instead of chromadb for embeddings (#14163) * swap sqlite_vec for chroma in requirements * load sqlite_vec in embeddings manager * remove chroma and revamp Embeddings class for sqlite_vec * manual minilm onnx inference * remove chroma in clip model * migrate api from chroma to sqlite_vec * migrate event cleanup from chroma to sqlite_vec * migrate embedding maintainer from chroma to sqlite_vec * genai description for sqlite_vec * load sqlite_vec in main thread db * extend the SqliteQueueDatabase class and use peewee db.execute_sql * search with Event type for similarity * fix similarity search * install and add comment about transformers * fix normalization * add id filter * clean up * clean up * fully remove chroma and add transformers env var * readd uvicorn for fastapi * readd tokenizer parallelism env var * remove chroma from docs * remove chroma from UI * try removing custom pysqlite3 build * hard code limit * optimize queries * revert explore query * fix query * keep building pysqlite3 * single pass fetch and process * remove unnecessary re-embed * update deps * move SqliteVecQueueDatabase to db directory * make search thumbnail take up full size of results box * improve typing * improve model downloading and add status screen * daemon downloading thread * catch case when semantic search is disabled * fix typing * build sqlite_vec from source * resolve conflict * file permissions * try build deps * remove sources * sources * fix thread start * include git in build * reorder embeddings after detectors are started * build with sqlite amalgamation * non-platform specific * use wget instead of curl * remove unzip -d * remove sqlite_vec from requirements and load the compiled version * fix build * avoid race in db connection * add scale_factor and bias to description zscore normalization
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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)
Use sqlite-vec extension instead of chromadb for embeddings (#14163) * swap sqlite_vec for chroma in requirements * load sqlite_vec in embeddings manager * remove chroma and revamp Embeddings class for sqlite_vec * manual minilm onnx inference * remove chroma in clip model * migrate api from chroma to sqlite_vec * migrate event cleanup from chroma to sqlite_vec * migrate embedding maintainer from chroma to sqlite_vec * genai description for sqlite_vec * load sqlite_vec in main thread db * extend the SqliteQueueDatabase class and use peewee db.execute_sql * search with Event type for similarity * fix similarity search * install and add comment about transformers * fix normalization * add id filter * clean up * clean up * fully remove chroma and add transformers env var * readd uvicorn for fastapi * readd tokenizer parallelism env var * remove chroma from docs * remove chroma from UI * try removing custom pysqlite3 build * hard code limit * optimize queries * revert explore query * fix query * keep building pysqlite3 * single pass fetch and process * remove unnecessary re-embed * update deps * move SqliteVecQueueDatabase to db directory * make search thumbnail take up full size of results box * improve typing * improve model downloading and add status screen * daemon downloading thread * catch case when semantic search is disabled * fix typing * build sqlite_vec from source * resolve conflict * file permissions * try build deps * remove sources * sources * fix thread start * include git in build * reorder embeddings after detectors are started * build with sqlite amalgamation * non-platform specific * use wget instead of curl * remove unzip -d * remove sqlite_vec from requirements and load the compiled version * fix build * avoid race in db connection * add scale_factor and bias to description zscore normalization
2024-10-07 22:30:45 +02:00
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 drop_embeddings_tables(self) -> None:
self.execute_sql("""
DROP TABLE vec_descriptions;
""")
self.execute_sql("""
DROP TABLE vec_thumbnails;
""")
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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
);
""")