Miscellaneous fixes (0.18 beta) (#23763)

docker/main/build_sqlite_vec.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-SQLITE_VEC_VERSION="0.1.3"
+SQLITE_VEC_VERSION="0.1.9"
 
 source /etc/os-release
 

docs/docs/configuration/audio_detectors.md

@@ -272,7 +272,7 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
 
 #### Transcription and translation of `speech` audio events
 
-Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
+Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
 
 In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
 
@@ -294,7 +294,7 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
 
    Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
 
-   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
+   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
 
    Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
 

docs/docs/configuration/face_recognition.md

@@ -232,7 +232,21 @@ Once front-facing images are performing well, start choosing slightly off-angle
 
 Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
 
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
+1. Enable debug logs to see exactly what Frigate is doing.
+   - Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
+
+     ```yaml
+     logger:
+       default: info
+       logs:
+         # highlight-next-line
+         frigate.data_processing.real_time.face: debug
+     ```
+
+   - These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
+   - If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
+
+2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
    - Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
@@ -242,7 +256,7 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
    - Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
    - You may need to lower your `detection_threshold` if faces are not being detected.
 
-2. Any detected faces will then be _recognized_.
+3. Any detected faces will then be _recognized_.
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 

docs/docs/configuration/genai/config.md

@@ -78,7 +78,7 @@ All llama.cpp native options can be passed through `provider_options`, including
    - Set **Provider** to `llamacpp`
    - Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
    - Set **Model** to the name of your model
-   - Under **Provider Options**, set `context_size` to tell Frigate your context size so it can send the appropriate amount of information
+   - Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
 
 </TabItem>
 <TabItem value="yaml">
@@ -89,12 +89,14 @@ genai:
   base_url: http://localhost:8080
   model: your-model-name
   provider_options:
-    context_size: 16000 # Tell Frigate your context size so it can send the appropriate amount of information.
+    context_size: 16000 # Optional, overrides the context size reported by the server.
 ```
 
 </TabItem>
 </ConfigTabs>
 
+Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
+
 ### Ollama
 
 [Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.

frigate/api/event.py

@@ -1538,15 +1538,18 @@ async def set_description(
     event.data["description"] = new_description
     event.save()
 
-    # If semantic search is enabled, update the index
+    context: EmbeddingsContext | None = request.app.embeddings
-    if request.app.frigate_config.semantic_search.enabled:
+
-        context: EmbeddingsContext = request.app.embeddings
+    if context is not None:
         if len(new_description) > 0:
-            context.update_description(
+            # If semantic search is enabled, update the index
-                event_id,
+            if request.app.frigate_config.semantic_search.enabled:
-                new_description,
+                context.update_description(
-            )
+                    event_id,
+                    new_description,
+                )
         else:
+            # embeddings are always cleaned up so they don't outlive their description
             context.db.delete_embeddings_description(event_ids=[event_id])
 
     response_message = (
@@ -1675,9 +1678,11 @@ async def delete_single_event(event_id: str, request: Request) -> dict:
     event.delete_instance()
     Timeline.delete().where(Timeline.source_id == event_id).execute()
 
-    # If semantic search is enabled, update the index
+    # embeddings are always cleaned up, even when semantic search is disabled,
-    if request.app.frigate_config.semantic_search.enabled:
+    # so that they don't outlive their events
-        context: EmbeddingsContext = request.app.embeddings
+    context: EmbeddingsContext | None = request.app.embeddings
+
+    if context is not None:
         context.db.delete_embeddings_thumbnail(event_ids=[event_id])
         context.db.delete_embeddings_description(event_ids=[event_id])
 

frigate/app.py

@@ -270,7 +270,7 @@ class FrigateApp:
                 10
                 * len([c for c in self.config.cameras.values() if c.enabled_in_config]),
             ),
-            load_vec_extension=self.config.semantic_search.enabled,
+            load_vec_extension=True,
         )
         models = [
             Event,

frigate/db/sqlitevecq.py

@@ -1,9 +1,12 @@
+import logging
 import sqlite3
 from typing import Any
 
 import regex
 from playhouse.sqliteq import SqliteQueueDatabase
 
+logger = logging.getLogger(__name__)
+
 REGEXP_TIMEOUT_SECONDS = 1.0
 
 
@@ -28,8 +31,14 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
     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)
+        try:
+            conn.load_extension(self.sqlite_vec_path)
+        except conn.OperationalError:
+            logger.error("Unable to load the sqlite-vec extension")
+            self.load_vec_extension = False
+        finally:
+            conn.enable_load_extension(False)
 
     def _register_regexp(self, conn: sqlite3.Connection) -> None:
         def regexp(expr: str, item: str | None) -> bool:
@@ -44,13 +53,33 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
         conn.create_function("REGEXP", 2, regexp)
 
-    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
+    def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
+        """Delete embeddings for the given events, if the table exists.
+
+        Embeddings outlive the events they belong to when semantic search is
+        disabled, so deletes are attempted regardless of the current config.
+        """
+        if not event_ids or not self.load_vec_extension:
+            return
+
+        # the embeddings tables are only created once semantic search has run
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+
+        if cursor.fetchone() is None:
+            logger.debug("Skipping %s cleanup, table does not exist", table)
+            return
+
         ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM vec_thumbnails WHERE id IN ({ids})", event_ids)
+        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+
+    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
+        self._delete_embeddings("vec_thumbnails", event_ids)
 
     def delete_embeddings_description(self, event_ids: list[str]) -> None:
-        ids = ",".join(["?" for _ in event_ids])
+        self._delete_embeddings("vec_descriptions", 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("""

frigate/events/cleanup.py

@@ -366,9 +366,10 @@ class EventCleanup(threading.Thread):
                     logger.debug(f"Deleting {len(chunk)} events from the database")
                     Event.delete().where(Event.id << chunk).execute()
 
-                    if self.config.semantic_search.enabled:
+                    # embeddings are always cleaned up, even when semantic search
-                        self.db.delete_embeddings_description(event_ids=chunk)
+                    # is disabled, so that they don't outlive their events
-                        self.db.delete_embeddings_thumbnail(event_ids=chunk)
+                    self.db.delete_embeddings_description(event_ids=chunk)
-                        logger.debug(f"Deleted {len(ids_to_delete)} embeddings")
+                    self.db.delete_embeddings_thumbnail(event_ids=chunk)
+                    logger.debug(f"Deleted {len(chunk)} embeddings")
 
         logger.info("Exiting event cleanup...")

frigate/genai/plugins/llama_cpp.py

@@ -192,7 +192,7 @@ class LlamaCppClient(GenAIClient):
         logger.info(
             "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s, reasoning: %s",
             configured_model,
-            self._context_size or "unknown",
+            self.get_context_size(),
             self._supports_vision,
             self._supports_audio,
             self._supports_tools,

frigate/test/test_genai_providers.py

@@ -491,6 +491,34 @@ class TestLlamaCppProvider(unittest.TestCase):
         final = _final_message(self._run_with_lines(client, lines, MULTIMODAL_MESSAGES))
         self.assertEqual(final["content"], "ok")
 
+    def _validated_client(self, server_context_size, provider_options=None):
+        """Build a client as if the server reported the given context size."""
+        cfg = GenAIConfig(
+            provider="llamacpp",
+            model="m",
+            base_url="http://localhost:9999",
+            provider_options=provider_options or {},
+        )
+        info = {
+            "context_size": server_context_size,
+            "supports_vision": False,
+            "supports_audio": False,
+            "supports_tools": False,
+            "supports_reasoning": False,
+            "media_marker": "<__media__>",
+        }
+        cls = PROVIDERS[GenAIProviderEnum.llamacpp]
+        with patch.object(cls, "_get_model_info", return_value=info):
+            return cls(cfg, timeout=5)
+
+    def test_server_context_size_used_without_override(self):
+        client = self._validated_client(4096)
+        self.assertEqual(client.get_context_size(), 4096)
+
+    def test_provider_options_context_size_overrides_server(self):
+        client = self._validated_client(4096, {"context_size": 32768})
+        self.assertEqual(client.get_context_size(), 32768)
+
 
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_sqlitevecq_embeddings.py

@@ -0,0 +1,63 @@
+"""Tests for embedding cleanup on the main Frigate database.
+
+Embeddings are deleted whether or not semantic search is currently enabled, so
+the delete path has to tolerate databases where the vec0 tables were never
+created and installs where the sqlite-vec extension is unavailable.
+"""
+
+import os
+import tempfile
+import unittest
+
+from frigate.db.sqlitevecq import SqliteVecQueueDatabase
+
+
+class TestDeleteEmbeddings(unittest.TestCase):
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.db = SqliteVecQueueDatabase(os.path.join(self.tmp_dir.name, "test.db"))
+        self.db.start()
+        # the extension is not available to tests, so stand in for a database
+        # that has it loaded and use a plain table for the deletes
+        self.db.load_vec_extension = True
+
+    def tearDown(self) -> None:
+        self.db.stop()
+        self.db.close()
+        self.tmp_dir.cleanup()
+
+    def _flush_writes(self) -> None:
+        # writes are queued and applied by a worker thread, and the queue is
+        # FIFO, so awaiting a later write means the earlier ones are done
+        self.db.execute_sql("PRAGMA user_version = 0").fetchall()
+
+    def _create_thumbnails_table(self) -> None:
+        self.db.execute_sql("CREATE TABLE vec_thumbnails (id TEXT PRIMARY KEY)")
+        self.db.execute_sql("INSERT INTO vec_thumbnails (id) VALUES ('a'), ('b')")
+        self._flush_writes()
+
+    def _thumbnail_ids(self) -> list[str]:
+        return [row[0] for row in self.db.execute_sql("SELECT id FROM vec_thumbnails")]
+
+    def test_delete_without_tables_does_not_raise(self) -> None:
+        # semantic search was never enabled, so event cleanup has nothing to do
+        self.db.delete_embeddings_thumbnail(event_ids=["1700000000.0-abc"])
+        self.db.delete_embeddings_description(event_ids=["1700000000.0-abc"])
+
+    def test_delete_removes_embeddings(self) -> None:
+        self._create_thumbnails_table()
+
+        self.db.delete_embeddings_thumbnail(event_ids=["a"])
+        self._flush_writes()
+
+        self.assertEqual(self._thumbnail_ids(), ["b"])
+
+    def test_delete_skipped_without_extension(self) -> None:
+        self._create_thumbnails_table()
+        self.db.load_vec_extension = False
+
+        self.db.delete_embeddings_thumbnail(event_ids=["a"])
+        self._flush_writes()
+
+        # the vec0 tables cannot be written without the extension
+        self.assertEqual(self._thumbnail_ids(), ["a", "b"])

web/e2e/specs/settings/semantic-search.spec.ts

@@ -0,0 +1,129 @@
+/**
+ * Semantic Search settings tests -- MEDIUM tier.
+ *
+ * Focuses on the model_size field, which is unused when a GenAI embeddings
+ * provider is selected as the semantic search model. The resolved config always
+ * reports model_size (it has a schema default of "small"), even when the YAML
+ * file has no such key. Clearing model_size for a provider used to run
+ * unconditionally, which falsely marked the section dirty on load and asked the
+ * backend to delete a key that wasn't in the config file (KeyError: 'model_size').
+ */
+
+import { readFileSync } from "node:fs";
+import { resolve, dirname } from "node:path";
+import { fileURLToPath } from "node:url";
+import { test, expect } from "../../fixtures/frigate-test";
+import type { Page } from "@playwright/test";
+import { configFactory } from "../../fixtures/mock-data/config";
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+const CONFIG_SCHEMA = JSON.parse(
+  readFileSync(
+    resolve(__dirname, "../../fixtures/mock-data/config-schema.json"),
+    "utf-8",
+  ),
+);
+
+const PROVIDER = "llama_cpp";
+const SETTINGS_URL = "/settings?page=integrationSemanticSearch";
+const NOT_APPLICABLE = "Not applicable for GenAI providers";
+const UNSAVED = "You have unsaved changes";
+
+type SemanticSearch = {
+  enabled?: boolean;
+  model?: string;
+  model_size?: string;
+};
+
+async function installRoutes(page: Page, semanticSearch: SemanticSearch) {
+  const config = configFactory({
+    genai: { [PROVIDER]: { provider: PROVIDER, roles: ["embeddings"] } },
+    semantic_search: semanticSearch,
+  });
+
+  let lastSavedConfig: unknown = null;
+
+  await page.route("**/api/config/schema.json", (route) =>
+    route.fulfill({ json: CONFIG_SCHEMA }),
+  );
+  await page.route("**/api/config", (route) => {
+    if (route.request().method() === "GET") {
+      return route.fulfill({ json: config });
+    }
+    return route.fulfill({ json: { success: true } });
+  });
+  await page.route("**/api/config/set", async (route) => {
+    lastSavedConfig = route.request().postDataJSON();
+    await route.fulfill({ json: { success: true, require_restart: false } });
+  });
+  await page.route("**/api/config/raw_paths", (route) =>
+    route.fulfill({ json: { semantic_search: semanticSearch } }),
+  );
+
+  return { capturedConfig: () => lastSavedConfig };
+}
+
+test.describe("semantic search model_size @medium", () => {
+  test("a provider with a defaulted model_size is not dirty on load", async ({
+    frigateApp,
+  }) => {
+    // model_size stays at its schema default ("small"), i.e. it is not present
+    // in the YAML. This mirrors the reported bug: selecting a GenAI provider and
+    // returning to the page.
+    await installRoutes(frigateApp.page, {
+      enabled: true,
+      model: PROVIDER,
+    });
+    await frigateApp.goto(SETTINGS_URL);
+
+    // The provider path is active: model_size shows "Not applicable".
+    await expect(frigateApp.page.getByText(NOT_APPLICABLE)).toBeVisible();
+
+    // Give any clearing effect time to fire, then confirm the section stayed
+    // clean (no phantom unsaved-changes banner, Save disabled).
+    await frigateApp.page.waitForTimeout(1000);
+    await expect(frigateApp.page.getByText(UNSAVED)).toBeHidden();
+    await expect(
+      frigateApp.page.getByRole("button", { name: "Save", exact: true }),
+    ).toBeDisabled();
+  });
+
+  test("switching from a configured non-default model_size clears it", async ({
+    frigateApp,
+  }) => {
+    // A genuinely configured non-default model_size ("large") can only come from
+    // the YAML, so switching to a provider must still remove it.
+    const capture = await installRoutes(frigateApp.page, {
+      enabled: true,
+      model: "jinav2",
+      model_size: "large",
+    });
+    await frigateApp.goto(SETTINGS_URL);
+
+    // Starts clean on a Jina model.
+    await expect(frigateApp.page.getByText(UNSAVED)).toBeHidden();
+
+    // Switch the model to the GenAI provider.
+    await frigateApp.page
+      .getByRole("combobox", { name: /Semantic search model/ })
+      .click();
+    await frigateApp.page.getByRole("option", { name: PROVIDER }).click();
+
+    // The change is now dirty and model_size is no longer applicable.
+    await expect(frigateApp.page.getByText(NOT_APPLICABLE)).toBeVisible();
+    await expect(frigateApp.page.getByText(UNSAVED)).toBeVisible();
+
+    await frigateApp.page
+      .getByRole("button", { name: "Save", exact: true })
+      .click();
+
+    // The saved payload removes model_size (empty string = "remove" key).
+    await expect
+      .poll(() => capture.capturedConfig(), { timeout: 5_000 })
+      .toMatchObject({
+        config_data: {
+          semantic_search: { model: PROVIDER, model_size: "" },
+        },
+      });
+  });
+});

web/public/locales/en/views/settings.json

@@ -1936,7 +1936,8 @@
       "inputDimensionsNotDetectResolution": "Model input width and height are the input dimensions of the object detection model, not your camera's detect resolution. They should match the dimensions of the model you're using — typically a square size like 320x320 or 640x640."
     },
     "ffmpeg": {
-      "hwaccelManualNotRecommended": "Manual hardware acceleration arguments are not recommended. Unless a specific requirement exists, select the preset that matches your hardware."
+      "hwaccelManualNotRecommended": "Manual hardware acceleration arguments are not recommended. Unless a specific requirement exists, select the preset that matches your hardware.",
+      "inputsMissingGo2rtcStream": "An input below points at a go2rtc restream that no longer exists. Select an existing restream or enter the camera's URL manually, otherwise this camera will fail to connect."
     },
     "objects": {
       "genaiNoDescriptionsProvider": "You must configure a GenAI provider with the 'descriptions' role for descriptions to be generated."

web/src/components/config-form/section-configs/ffmpeg.ts

@@ -1,3 +1,4 @@
+import { parseRestreamStreamName } from "../theme/fields/streamSource";
 import type { SectionConfigOverrides } from "./types";
 
 const arrayAsTextWidget = {
@@ -42,6 +43,29 @@ const ffmpeg: SectionConfigOverrides = {
           return false;
         },
       },
+      {
+        key: "inputs-missing-go2rtc-stream",
+        field: "inputs",
+        position: "before",
+        messageKey: "configMessages.ffmpeg.inputsMissingGo2rtcStream",
+        severity: "warning",
+        docLink: "/configuration/restream",
+        condition: (ctx) => {
+          const streams = ctx.fullConfig?.go2rtc?.streams;
+          const inputs = ctx.formData?.inputs;
+          if (!Array.isArray(inputs)) {
+            return false;
+          }
+
+          return inputs.some((input) => {
+            const path = (input as { path?: unknown } | null)?.path;
+            const streamName = parseRestreamStreamName(
+              typeof path === "string" ? path : undefined,
+            );
+            return streamName !== undefined && !(streamName in (streams ?? {}));
+          });
+        },
+      },
     ],
     fieldDocs: {
       hwaccel_args: "/configuration/ffmpeg_presets#hwaccel-presets",

web/src/components/config-form/theme/fields/CameraInputsField.tsx

@@ -170,6 +170,12 @@ export function CameraInputsField(props: FieldProps) {
     [go2rtcStreamNames],
   );
 
+  useEffect(() => {
+    setSourceModeByIndex((previous) =>
+      Object.keys(previous).length > 0 ? {} : previous,
+    );
+  }, [formContext?.cameraName]);
+
   useEffect(() => {
     setOpenByIndex((previous) => {
       const next: Record<number, boolean> = {};
@@ -222,18 +228,12 @@ export function CameraInputsField(props: FieldProps) {
   const handleSourceModeChange = useCallback(
     (index: number, nextMode: StreamSourceMode) => {
       const input = inputs[index];
-      const currentPath =
-        typeof input?.path === "string" ? input.path : undefined;
 
-      if (nextMode === "manual") {
+      // Only revert the preset we set ourselves; never clobber custom args.
-        // Only revert the preset we set ourselves; never clobber custom args.
+      // The path is left alone until a stream is picked, so switching modes
-        if (input?.input_args === RESTREAM_PRESET) {
+      // never discards a typed URL or empties a required field.
-          handleFieldValuesChange(index, { input_args: undefined });
+      if (nextMode === "manual" && input?.input_args === RESTREAM_PRESET) {
-        }
+        handleFieldValuesChange(index, { input_args: undefined });
-      } else if (!parseRestreamStreamName(currentPath)) {
-        // Entering restream with a non-restream path: clear it so the dropdown
-        // shows its placeholder until a stream is chosen.
-        handleFieldValuesChange(index, { path: undefined });
       }
 
       setSourceModeByIndex((previous) => ({ ...previous, [index]: nextMode }));

web/src/components/config-form/theme/widgets/SemanticSearchModelSizeWidget.tsx

@@ -24,15 +24,19 @@ export function SemanticSearchModelSizeWidget(props: WidgetProps) {
     model !== "jinav1" &&
     model !== "jinav2";
 
-  // Clear model_size while on a provider (buildOverrides converts to ""
+  // model_size is unused on a GenAI provider. Only clear it (which the backend
-  // which the backend treats as "remove"). Restore the schema default
+  // treats as "remove") for a non-default value, which can only come from the
-  // when returning to a Jina model so the field isn't left empty.
+  // config file. A defaulted value is indistinguishable from unset in the
+  // resolved config, so clearing it would falsely dirty the field and delete a
+  // YAML key that isn't there. Restore the default when returning to a Jina model.
   const { value, onChange, schema } = props;
   const schemaDefault = schema?.default as string | undefined;
   useEffect(() => {
-    if (isProvider && value !== undefined) {
+    if (isProvider) {
-      onChange(undefined);
+      if (value !== undefined && value !== schemaDefault) {
-    } else if (!isProvider && value === undefined && schemaDefault) {
+        onChange(undefined);
+      }
+    } else if (value === undefined && schemaDefault) {
       onChange(schemaDefault);
     }
   }, [isProvider, value, onChange, schemaDefault]);