From 28139f265bd23a3370644f64b865fcf2fe82f978 Mon Sep 17 00:00:00 2001 From: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> Date: Tue, 15 Oct 2024 17:19:59 -0500 Subject: [PATCH] better match with ui settings --- web/src/views/settings/SearchSettingsView.tsx | 59 +++++++++---------- 1 file changed, 28 insertions(+), 31 deletions(-) diff --git a/web/src/views/settings/SearchSettingsView.tsx b/web/src/views/settings/SearchSettingsView.tsx index ccf3a5629..f5d80679d 100644 --- a/web/src/views/settings/SearchSettingsView.tsx +++ b/web/src/views/settings/SearchSettingsView.tsx @@ -161,10 +161,6 @@ export default function SearchSettingsView({ Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. - This feature works by creating embeddings — numerical vector - representations — for both the images and text descriptions of - your tracked objects. By comparing these embeddings, Frigate - assesses their similarities to deliver relevant search results.

@@ -182,52 +178,53 @@ export default function SearchSettingsView({
-
-
- -
+
{ handleSearchConfigChange({ enabled: isChecked }); }} /> -
-
- -
- Re-indexing will reprocess all thumbnails and descriptions (if - enabled) and apply the embeddings on each startup. Don't forget - to disable the option after restarting! + +
+
+
+
+ { + handleSearchConfigChange({ reindex: isChecked }); + }} + /> +
+
- { - handleSearchConfigChange({ reindex: isChecked }); - }} - /> +
+ Re-indexing will reprocess all thumbnails and descriptions (if + enabled) and apply the embeddings on each startup. Don't forget to + disable the option after restarting! +
- -
+
-
Model Size
-
+
Model Size
+

The size of the model used for semantic search embeddings.

  • Using small employs a quantized version of the - model that uses much less RAM and runs faster on CPU with a - very negligible difference in embedding quality. + model that uses less RAM and runs faster on CPU with a very + negligible difference in embedding quality.
  • Using large employs the full Jina model and will