Performance: load a cached Hugging Face embedding model without contacting the Hub

Every chat question and AI suggestion request builds the embedding model, and sentence-transformers checks the Hub for a newer revision each time. With the model already cached that check cost about 2 seconds per request (0.2 seconds without it), and it fails outright when the Hub is unreachable.

Load from the local cache first and only download when the model is missing. A full rebuild still checks the Hub, so a new model release is picked up together with re-embedding every document instead of silently mixing revisions.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SRX1rCtSx62vSDaiFuBFCM

src-ui/src/app/components/document-detail/document-detail.component.html

@@ -465,7 +465,7 @@
     @switch (archiveContentRenderType) {
       @case (ContentRenderType.PDF) {
         @if (!useNativePdfViewer) {
-          <div class="preview-sticky pdf-viewer-container">
+          <div class="preview-sticky pdf-viewer-container" tabindex="-1">
             <pngx-pdf-viewer
               [src]="pdfSource()"
               [sourceRevision]="previewRevision()"
@@ -477,7 +477,7 @@
               (loadError)="onError($event)"
               (afterLoadComplete)="pdfPreviewLoaded($event)">
             </pngx-pdf-viewer>
-            <div class="position-absolute bottom-0 start-50 translate-middle-x z-1 mb-3 d-flex gap-2 p-1 bg-body border rounded shadow">
+            <div class="preview-controls position-absolute bottom-0 start-50 translate-middle-x z-1 mb-3 d-flex gap-2 p-1 bg-body border rounded shadow">
               @if (previewNumPages()) {
                 <div class="input-group input-group-sm w-auto flex-nowrap">
                   <input class="form-control flex-grow-0 w-auto" type="number" min="1" [max]="previewNumPages()" [ngModelOptions]="{standalone: true}" [ngModel]="previewCurrentPage()" (ngModelChange)="previewCurrentPage.set($event)" aria-label="Page" i18n-aria-label />

src-ui/src/app/components/document-detail/document-detail.component.scss

@@ -12,6 +12,22 @@
     width: 100%;
     height: 100%;
   }
+
+  &:focus {
+    outline: none;
+  }
+
+  .preview-controls {
+    opacity: 0;
+    pointer-events: none;
+    transition: opacity 0.15s ease-in-out;
+  }
+
+  &:hover .preview-controls,
+  &:focus-within .preview-controls {
+    opacity: 1;
+    pointer-events: auto;
+  }
 }
 
 .btn-group .dropdown-toggle-split {

src/paperless_ai/embedding.py

@@ -20,7 +20,18 @@ OCR_LEADER_REGEX = re.compile(r"[._\-\u00b7]{4,}")
 HORIZONTAL_WHITESPACE_REGEX = re.compile(r"[ \t\u00a0]+")
 
 
-def get_embedding_model(config: AIConfig) -> "BaseEmbedding":
+def get_embedding_model(
+    config: AIConfig,
+    *,
+    refresh: bool = False,
+) -> "BaseEmbedding":
+    """Build the configured embedding model.
+
+    For the Hugging Face backend, a model already in the local cache is loaded
+    without contacting the Hub, which saves seconds per call. ``refresh=True``
+    checks the Hub for a newer revision first; index rebuilds use it so a new
+    model release is picked up together with re-embedding every document.
+    """
     match config.llm_embedding_backend:
         case LLMEmbeddingBackend.OPENAI_LIKE:
             from llama_index.embeddings.openai_like import OpenAILikeEmbedding
@@ -52,11 +63,18 @@ def get_embedding_model(config: AIConfig) -> "BaseEmbedding":
         case LLMEmbeddingBackend.HUGGINGFACE:
             from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
-            return HuggingFaceEmbedding(
-                model_name=config.llm_embedding_model
+            hf_kwargs = {
+                "model_name": config.llm_embedding_model
                 or "sentence-transformers/all-MiniLM-L6-v2",
-                cache_folder=str(settings.DATA_DIR / "hf_cache"),
-            )
+                "cache_folder": str(settings.DATA_DIR / "hf_cache"),
+            }
+            if refresh:
+                return HuggingFaceEmbedding(**hf_kwargs)
+            try:
+                return HuggingFaceEmbedding(**hf_kwargs, local_files_only=True)
+            except OSError:
+                # Not cached yet, download it
+                return HuggingFaceEmbedding(**hf_kwargs)
         case LLMEmbeddingBackend.OLLAMA:
             from llama_index.embeddings.ollama import OllamaEmbedding
             from ollama import AsyncClient

src/paperless_ai/indexing.py

@@ -470,7 +470,7 @@ def update_llm_index(
         logger.warning("No documents found to index.")
 
     chunk_size = config.llm_embedding_chunk_size
-    embed_model = get_embedding_model(config)
+    embed_model = get_embedding_model(config, refresh=rebuild)
 
     with write_store(embed_model_name=model_name) as store:
         if rebuild or not store.table_exists():

src/paperless_ai/tests/test_embedding.py

@@ -167,6 +167,7 @@ def test_get_embedding_model_huggingface(mock_ai_config):
         MockHuggingFaceEmbedding.assert_called_once_with(
             model_name="sentence-transformers/all-MiniLM-L6-v2",
             cache_folder=str(settings.DATA_DIR / "hf_cache"),
+            local_files_only=True,
         )
         assert model == MockHuggingFaceEmbedding.return_value
 

src/paperless_ai/tests/test_embedding_hf_local_files.py

@@ -0,0 +1,106 @@
+from pathlib import Path
+from unittest.mock import MagicMock
+from unittest.mock import call
+from unittest.mock import patch
+
+import pytest
+import pytest_mock
+from django.conf import settings
+
+from paperless.models import LLMEmbeddingBackend
+from paperless_ai import indexing
+from paperless_ai.embedding import get_embedding_model
+from paperless_ai.tests.conftest import FakeEmbedding
+from paperless_testing.factories import DocumentFactory
+
+MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+CACHE = str(settings.DATA_DIR / "hf_cache")
+
+
+@pytest.fixture
+def hf_config() -> MagicMock:
+    config = MagicMock()
+    config.llm_embedding_backend = LLMEmbeddingBackend.HUGGINGFACE
+    config.llm_embedding_model = MODEL
+    return config
+
+
+@pytest.fixture
+def hf_embedding(mocker: pytest_mock.MockerFixture) -> MagicMock:
+    return mocker.patch("llama_index.embeddings.huggingface.HuggingFaceEmbedding")
+
+
+class TestHuggingFaceLocalFirst:
+    def test_uses_cached_model_without_hub(
+        self,
+        hf_config: MagicMock,
+        hf_embedding: MagicMock,
+    ) -> None:
+        model = get_embedding_model(hf_config)
+
+        hf_embedding.assert_called_once_with(
+            model_name=MODEL,
+            cache_folder=CACHE,
+            local_files_only=True,
+        )
+        assert model is hf_embedding.return_value
+
+    def test_downloads_when_not_cached(
+        self,
+        hf_config: MagicMock,
+        hf_embedding: MagicMock,
+    ) -> None:
+        downloaded = MagicMock()
+        hf_embedding.side_effect = [OSError("not cached"), downloaded]
+
+        model = get_embedding_model(hf_config)
+
+        assert hf_embedding.call_args_list == [
+            call(model_name=MODEL, cache_folder=CACHE, local_files_only=True),
+            call(model_name=MODEL, cache_folder=CACHE),
+        ]
+        assert model is downloaded
+
+    def test_refresh_checks_the_hub(
+        self,
+        hf_config: MagicMock,
+        hf_embedding: MagicMock,
+    ) -> None:
+        get_embedding_model(hf_config, refresh=True)
+
+        hf_embedding.assert_called_once_with(model_name=MODEL, cache_folder=CACHE)
+
+
+@pytest.mark.django_db
+class TestIndexUpdateRefresh:
+    @pytest.fixture(autouse=True)
+    def _index_inputs(self, temp_llm_index_dir: Path) -> None:
+        DocumentFactory.create()
+
+    def _update(self, mock_embed_model: FakeEmbedding, *, rebuild: bool) -> MagicMock:
+        mock_config = MagicMock()
+        mock_config.llm_embedding_chunk_size = 512
+        with (
+            patch("paperless_ai.indexing.AIConfig", return_value=mock_config),
+            patch("paperless_ai.indexing.build_document_node", return_value=[]),
+            patch(
+                "paperless_ai.indexing.get_embedding_model",
+                return_value=mock_embed_model,
+            ) as get_model,
+        ):
+            indexing.update_llm_index(rebuild=rebuild)
+        get_model.assert_called_once()
+        return get_model
+
+    def test_rebuild_refreshes(self, mock_embed_model: FakeEmbedding) -> None:
+        get_model = self._update(mock_embed_model, rebuild=True)
+
+        assert get_model.call_args.kwargs == {"refresh": True}
+
+    def test_incremental_update_stays_local(
+        self,
+        mock_embed_model: FakeEmbedding,
+    ) -> None:
+        get_model = self._update(mock_embed_model, rebuild=False)
+
+        assert get_model.call_args.kwargs == {"refresh": False}