Fix semantic search reindex (#24407)

* fix semantic search reindex

sqlite-vec added the `_info` shadow table in 0.1.6 and drops it unconditionally when a vec0 table is destroyed, so `DROP TABLE` on a table written by 0.17 failed with "SQL logic error" once 0.18 moved to 0.1.9. `SqliteQueueDatabase` queues non-SELECT statements and stores the exception on the cursor it returns, and nothing read those cursors, so the failed drop and every write after it went unreported while reindex still logged "Embedded N thumbnails". `drop_embeddings_tables()` now recreates the missing `_info` stub before dropping, and writes go through `execute_write()`, which waits on the cursor so failures raise. `INSERT OR REPLACE` is gone too, since vec0 implements neither REPLACE nor UPSERT and it always failed on an id already in the table, including under the 0.1.3 build 0.17 shipped.

* use lock

* show reindex failure in status bar

frigate/embeddings/embeddings.py

@@ -6,9 +6,10 @@ import logging
 import os
 import threading
 import time
+from typing import Any
 
 import numpy as np
-from peewee import DoesNotExist, IntegrityError
+from peewee import DatabaseError, DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -207,12 +208,10 @@ class Embeddings:
         embedding = self.vision_embedding([thumbnail])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_thumbnails",
+                "thumbnail_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -251,19 +250,12 @@ class Embeddings:
         embeddings = self.vision_embedding(valid_thumbs)
 
         if upsert:
-            items = []
+            items = {}
             for i in range(len(valid_ids)):
-                items.append(valid_ids[i])
-                items.append(serialize(embeddings[i]))
+                items[valid_ids[i]] = serialize(embeddings[i])
                 self.image_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
-                items,
-            )
+            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
 
         duration = datetime.datetime.now().timestamp() - start
         self.image_inference_speed.update(duration / len(valid_ids))
@@ -277,12 +269,10 @@ class Embeddings:
         embedding = self.text_embedding([description])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_descriptions",
+                "description_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -302,19 +292,14 @@ class Embeddings:
 
         if upsert:
             ids = list(event_descriptions.keys())
-            items = []
+            items = {}
 
             for i in range(len(ids)):
-                items.append(ids[i])
-                items.append(serialize(embeddings[i]))
+                items[ids[i]] = serialize(embeddings[i])
                 self.text_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(ids))),
-                items,
+            self.db.upsert_embeddings(
+                "vec_descriptions", "description_embedding", items
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -322,6 +307,17 @@ class Embeddings:
         return embeddings
 
     def reindex(self) -> None:
+        """Rebuild every tracked object embedding from scratch."""
+        totals: dict[str, Any] = {"status": "indexing"}
+
+        try:
+            self._reindex(totals)
+        except DatabaseError:
+            logger.exception("Unable to reindex tracked object embeddings")
+            totals["status"] = "failed"
+            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
+
+    def _reindex(self, totals: dict[str, Any]) -> None:
         logger.info("Indexing tracked object embeddings...")
 
         self.db.drop_embeddings_tables()
@@ -346,14 +342,18 @@ class Embeddings:
             batch_size = 32
         current_page = 1
 
-        totals = {
-            "thumbnails": 0,
-            "descriptions": 0,
-            "processed_objects": total_events - 1 if total_events < batch_size else 0,
-            "total_objects": total_events,
-            "time_remaining": 0 if total_events < batch_size else -1,
-            "status": "indexing",
-        }
+        totals.update(
+            {
+                "thumbnails": 0,
+                "descriptions": 0,
+                "processed_objects": total_events - 1
+                if total_events < batch_size
+                else 0,
+                "total_objects": total_events,
+                "time_remaining": 0 if total_events < batch_size else -1,
+                "status": "indexing",
+            }
+        )
 
         self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)