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

frigate/embeddings/util.py

@@ -4,12 +4,15 @@ import math
 
 
 class ZScoreNormalization:
-    """Running Z-score normalization for search distance."""
-
-    def __init__(self):
+    def __init__(self, scale_factor: float = 1.0, bias: float = 0.0):
+        """Initialize with optional scaling and bias adjustments."""
+        """scale_factor adjusts the magnitude of each score"""
+        """bias will artificially shift the entire distribution upwards"""
         self.n = 0
         self.mean = 0
         self.m2 = 0
+        self.scale_factor = scale_factor
+        self.bias = bias
 
     @property
     def variance(self):
@@ -23,7 +26,10 @@ class ZScoreNormalization:
         self._update(distances)
         if self.stddev == 0:
             return distances
-        return [(x - self.mean) / self.stddev for x in distances]
+        return [
+            (x - self.mean) / self.stddev * self.scale_factor + self.bias
+            for x in distances
+        ]
 
     def _update(self, distances: list[float]):
         for x in distances: