feat: pool fastembed, batch embeddings, and reconcile embedding config on startup

html-router/src/routes/admin/handlers.rs

@@ -1,5 +1,3 @@
-use std::sync::Arc;
-
 use async_openai::types::ListModelResponse;
 use axum::{
     extract::{Query, State},
@@ -11,17 +9,15 @@ use common::{
     error::AppError,
     storage::types::{
         analytics::Analytics,
-        knowledge_entity::KnowledgeEntity,
         system_prompts::{
             DEFAULT_IMAGE_PROCESSING_PROMPT, DEFAULT_INGRESS_ANALYSIS_SYSTEM_PROMPT,
             DEFAULT_QUERY_SYSTEM_PROMPT,
         },
         system_settings::{SystemSettings, SystemSettingsPatch},
-        text_chunk::TextChunk,
     },
     utils::embedding::EmbeddingBackend,
 };
-use tracing::{error, info};
+use tracing::info;
 
 use crate::{
     html_state::HtmlState,
@@ -209,41 +205,15 @@ pub async fn update_model_settings(
     .await?;
 
     if reembedding_needed {
-        info!("Embedding dimensions changed. Spawning background re-embedding task...");
-
-        let db_for_task = Arc::clone(&state.db);
-        let openai_for_task = Arc::clone(&state.openai_client);
-        let new_model_for_task = new_settings.embedding_model.clone();
-        let new_dims_for_task = new_settings.embedding_dimensions;
-
-        tokio::spawn(async move {
-            // First, update all text chunks
-            if let Err(e) = TextChunk::update_all_embeddings(
-                &db_for_task,
-                &openai_for_task,
-                &new_model_for_task,
-                new_dims_for_task,
-            )
-            .await
-            {
-                error!("Background re-embedding task failed for TextChunks: {}", e);
-            }
-
-            // Second, update all knowledge entities
-            if let Err(e) = KnowledgeEntity::update_all_embeddings(
-                &db_for_task,
-                &openai_for_task,
-                &new_model_for_task,
-                new_dims_for_task,
-            )
-            .await
-            {
-                error!(
-                    "Background re-embedding task failed for KnowledgeEntities: {}",
-                    e
-                );
-            }
-        });
+        // Re-embedding is owned by startup (the worker/combined binary), not the admin request.
+        // Doing it inline here would leave the live, startup-built embedding provider embedding
+        // queries at the old dimension while stored vectors move to the new one — broken retrieval
+        // until restart. Persisting the new settings is enough: on the next restart the maintainer
+        // detects the index/dimension mismatch and re-embeds before rebuilding indexes.
+        info!(
+            new_dimensions = new_settings.embedding_dimensions,
+            "Embedding dimensions changed; restart the worker/server to re-embed and apply"
+        );
     }
 
     let available_models = state

html-router/src/routes/chat/message_response_stream.rs

@@ -359,8 +359,7 @@ async fn prepare_chat_request(
 
     let retrieval_result = match retrieval_pipeline::retrieve(
         &state.db,
-        &state.openai_client,
-        Some(&*state.embedding_provider),
+        &state.embedding_provider,
         &user_message.content,
         &user.id,
         config,

html-router/src/routes/knowledge/handlers.rs

@@ -24,7 +24,7 @@ use common::{
             user::User,
         },
     },
-    utils::embedding::{generate_embedding_with_provider, EmbeddingProvider},
+    utils::embedding::EmbeddingProvider,
 };
 use retrieval_pipeline::{
     normalize_fts_terms, reciprocal_rank_fusion, RetrievalTuning, RrfConfig, Scored,
@@ -187,8 +187,11 @@ pub async fn create_knowledge_entity(
 
     let embedding_input =
         format!("name: {name}, description: {description}, type: {entity_type:?}");
-    let embedding =
-        generate_embedding_with_provider(&state.embedding_provider, &embedding_input).await?;
+    let embedding = state
+        .embedding_provider
+        .embed(&embedding_input)
+        .await
+        .map_err(AppError::from)?;
 
     let source_id = format!("manual::{}", Uuid::new_v4());
     let new_entity = KnowledgeEntity::new(
@@ -373,8 +376,7 @@ async fn suggest_related_entities(
         "name: {}, description: {}, type: {:?}",
         draft.name, draft.description, draft.entity_type
     );
-    let embedding =
-        generate_embedding_with_provider(embedding_provider, &embedding_input).await?;
+    let embedding = embedding_provider.embed(&embedding_input).await?;
 
     let take = MAX_RELATIONSHIP_SUGGESTIONS * 2;
     let tuning = RetrievalTuning::default();

html-router/src/routes/search/handlers.rs

@@ -171,8 +171,7 @@ async fn perform_search(
 
     let result = retrieve(
         &state.db,
-        &state.openai_client,
-        Some(&state.embedding_provider),
+        &state.embedding_provider,
         trimmed_query,
         &user.id,
         config,