Chore: use document type ids in the bulk edit selection_data test

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

src-ui/messages.xlf

@@ -10334,18 +10334,18 @@
           <context context-type="linenumber">294,295</context>
         </context-group>
       </trans-unit>
-      <trans-unit id="3420321797707163677" datatype="html">
-        <source>Preview document</source>
+      <trans-unit id="2179847500064178686" datatype="html">
+        <source>Edit document</source>
         <context-group purpose="location">
           <context context-type="sourcefile">src/app/components/document-list/document-list.component.html</context>
           <context context-type="linenumber">328</context>
         </context-group>
       </trans-unit>
-      <trans-unit id="2179847500064178686" datatype="html">
-        <source>Edit document</source>
+      <trans-unit id="3420321797707163677" datatype="html">
+        <source>Preview document</source>
         <context-group purpose="location">
           <context context-type="sourcefile">src/app/components/document-list/document-list.component.html</context>
-          <context context-type="linenumber">331</context>
+          <context context-type="linenumber">329</context>
         </context-group>
       </trans-unit>
       <trans-unit id="4512084577073831437" datatype="html">

src-ui/src/app/components/common/custom-fields-dropdown/custom-fields-dropdown.component.scss

@@ -1,3 +1,10 @@
 .custom-fields-dropdown {
     min-width: 300px;
+
+    // correct position on mobile
+    @media (max-width: 575.98px) {
+        &.show {
+            margin-left: -245px !important;
+        }
+    }
 }

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

@@ -308,7 +308,7 @@
                   </tr>
                   <tr class="align-middle">
                     <td i18n>Original SHA256 checksum</td>
-                    <td><span class="badge bg-light text-dark font-monospace text-wrap text-break text-start">{{metadata()?.original_checksum}}</span></td>
+                    <td><span class="badge bg-light font-monospace text-wrap text-break text-start">{{metadata()?.original_checksum}}</span></td>
                   </tr>
                   <tr>
                     <td i18n>Original file size</td>
@@ -465,7 +465,7 @@
     @switch (archiveContentRenderType) {
       @case (ContentRenderType.PDF) {
         @if (!useNativePdfViewer) {
-          <div class="preview-sticky pdf-viewer-container" tabindex="-1">
+          <div class="preview-sticky pdf-viewer-container">
             <pngx-pdf-viewer
               [src]="pdfSource()"
               [sourceRevision]="previewRevision()"
@@ -477,7 +477,7 @@
               (loadError)="onError($event)"
               (afterLoadComplete)="pdfPreviewLoaded($event)">
             </pngx-pdf-viewer>
-            <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">
+            <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">
               @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

@@ -12,22 +12,6 @@
     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-ui/src/app/components/document-list/document-list.component.html

@@ -208,7 +208,7 @@
                   i18n>Correspondent</th>
               }
               @if (activeDisplayFields.includes(DisplayField.TITLE)) {
-                <th class="cursor-pointer ps-5"
+                <th class="cursor-pointer"
                   pngxSortable="title"
                   title="Sort by title" i18n-title
                   [currentSortField]="list.sortField"
@@ -322,13 +322,13 @@
                   </td>
                 }
                 @if (activeDisplayFields.includes(DisplayField.TITLE) || activeDisplayFields.includes(DisplayField.TAGS)) {
-                  <td width="30%" class="position-relative" [class.ps-5]="activeDisplayFields.includes(DisplayField.TITLE)">
+                  <td width="30%">
                     @if (activeDisplayFields.includes(DisplayField.TITLE)) {
                       <div class="d-inline-block" (mouseleave)="popupPreview.close()">
-                        <pngx-preview-popup class="position-absolute start-0" [document]="d" linkClasses="btn btn-sm btn-link text-secondary" linkTitle="Preview document" (click)="$event.stopPropagation()" i18n-linkTitle #popupPreview>
+                        <a routerLink="/documents/{{d.id}}" title="Edit document" i18n-title style="overflow-wrap: anywhere;">{{d.title | documentTitle}}</a>
+                        <pngx-preview-popup [document]="d" linkClasses="btn btn-sm btn-link text-secondary" linkTitle="Preview document" (click)="$event.stopPropagation()" i18n-linkTitle #popupPreview>
                           <i-bs name="eye"></i-bs>
                         </pngx-preview-popup>
-                        <a routerLink="/documents/{{d.id}}" title="Edit document" i18n-title style="overflow-wrap: anywhere;">{{d.title | documentTitle}}</a>
                       </div>
                     }
                     @if (activeDisplayFields.includes(DisplayField.TAGS)) {

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

@@ -87,10 +87,6 @@ $paperless-card-breakpoints: (
   }
 }
 
-.table .ps-5 {
-  padding-left: 2.2rem !important;
-}
-
 a {
   cursor: pointer;
 }

src/documents/permissions.py

@@ -576,12 +576,9 @@ def annotate_document_count_by_ids(
                        or CustomFieldInstance)
         related_object_field: field on the relation pointing back to queryset pk
         document_ids: the document ids to count against -- a concrete list/set,
-                       or a simple (already resolved) queryset of ids. Callers
-                       that need this filtered by a complex condition (e.g. a
-                       permission check) should resolve it to a concrete list
-                       first if the same ids will be reused across multiple
-                       calls, rather than passing the complex queryset itself
-                       into each -- see ``_get_selection_data_for_queryset``.
+                       or a simple (already resolved) queryset of ids. Keep
+                       the list small: it is embedded in the statement as
+                       literal parameters.
         target_field: field on the relation pointing to Document id
     """
 

src/documents/tests/test_api_bulk_edit.py

@@ -1141,8 +1141,8 @@ class TestBulkEditAPI(DirectoriesMixin, APITestCase):
         self.assertCountEqual(
             response.data["selected_document_types"],
             [
-                {"id": self.c1.id, "document_count": 1},
-                {"id": self.c2.id, "document_count": 0},
+                {"id": self.dt1.id, "document_count": 1},
+                {"id": self.dt2.id, "document_count": 0},
             ],
         )
 

src/documents/tests/test_api_documents.py

@@ -49,6 +49,7 @@ from documents.models import WorkflowTrigger
 from documents.search import get_backend
 from documents.signals.handlers import run_workflows
 from documents.tests.utils import ConsumeTaskMixin
+from documents.views import DocumentViewSet
 from paperless_testing.dirs import DirectoriesMixin
 from paperless_testing.factories import DocumentFactory
 from paperless_testing.factories import TagFactory
@@ -1755,6 +1756,106 @@ class TestDocumentApi(DirectoriesMixin, ConsumeTaskMixin, APITestCase):
         }
         self.assertEqual(counts_by_field[custom_field.id], 1)
 
+    def test_selection_data_counts_with_shared_by_filter(self) -> None:
+        """
+        GIVEN:
+            - Several documents owned by one user with the same correspondent
+              and document type, only one of which is shared
+        WHEN:
+            - Requesting the document list filtered by shared_by__id with
+              include_selection_data=true
+        THEN:
+            - Counts reflect only the shared document, not every document the
+              owner has with that correspondent or document type, however the
+              link-table lookups are batched
+        """
+        owner = User.objects.create_user(username="owner")
+        other = User.objects.create_user(username="other")
+        correspondent = Correspondent.objects.create(name="c1")
+        doc_type = DocumentType.objects.create(name="dt1")
+        tag = Tag.objects.create(name="tag")
+        docs = [
+            Document.objects.create(
+                checksum=str(i),
+                owner=owner,
+                correspondent=correspondent,
+                document_type=doc_type,
+            )
+            for i in range(3)
+        ]
+        for doc in docs:
+            doc.tags.add(tag)
+        grant_object(other, docs[0], "view_document")
+
+        for batch_size in (10_000, 1):
+            with (
+                self.subTest(link_batch_size=batch_size),
+                mock.patch.object(
+                    DocumentViewSet,
+                    "selection_data_link_batch_size",
+                    batch_size,
+                ),
+            ):
+                response = self.client.get(
+                    f"/api/documents/?shared_by__id={owner.id}"
+                    "&include_selection_data=true",
+                )
+
+                self.assertEqual(response.status_code, status.HTTP_200_OK)
+                self.assertEqual(response.data["count"], 1)
+                selection_data = response.data["selection_data"]
+                for key, obj in (
+                    ("selected_correspondents", correspondent),
+                    ("selected_document_types", doc_type),
+                    ("selected_tags", tag),
+                ):
+                    counts = {
+                        item["id"]: item["document_count"]
+                        for item in selection_data[key]
+                    }
+                    self.assertEqual(counts[obj.id], 1, key)
+
+    def test_selection_data_counts_documents_once_when_filter_joins(self) -> None:
+        """
+        GIVEN:
+            - A document matching a multi-valued filter through two tags
+        WHEN:
+            - Requesting the document list with include_selection_data=true
+        THEN:
+            - The document is counted once per correspondent, not once per
+              matching tag, however the link-table lookups are batched
+        """
+        correspondent = Correspondent.objects.create(name="c1")
+        tag_a = Tag.objects.create(name="a")
+        tag_b = Tag.objects.create(name="b")
+        both = Document.objects.create(checksum="1", correspondent=correspondent)
+        both.tags.add(tag_a, tag_b)
+        only_a = Document.objects.create(checksum="2", correspondent=correspondent)
+        only_a.tags.add(tag_a)
+
+        for batch_size in (10_000, 1):
+            with (
+                self.subTest(link_batch_size=batch_size),
+                mock.patch.object(
+                    DocumentViewSet,
+                    "selection_data_link_batch_size",
+                    batch_size,
+                ),
+            ):
+                response = self.client.get(
+                    f"/api/documents/?tags__id__in={tag_a.id},{tag_b.id}"
+                    "&include_selection_data=true",
+                )
+
+                self.assertEqual(response.status_code, status.HTTP_200_OK)
+                counts = {
+                    item["id"]: item["document_count"]
+                    for item in response.data["selection_data"][
+                        "selected_correspondents"
+                    ]
+                }
+                self.assertEqual(counts[correspondent.id], 2)
+
     def test_statistics(self) -> None:
         doc1 = Document.objects.create(
             title="none1",

src/documents/views.py

@@ -5,6 +5,7 @@ import platform
 import re
 import tempfile
 import zipfile
+from collections import Counter
 from collections import deque
 from collections.abc import Callable
 from datetime import datetime
@@ -168,7 +169,6 @@ from documents.permissions import PaperlessNotePermissions
 from documents.permissions import PaperlessObjectPermissions
 from documents.permissions import TrashPermissions
 from documents.permissions import ViewDocumentsPermissions
-from documents.permissions import annotate_document_count_by_ids
 from documents.permissions import annotate_document_count_for_related_queryset
 from documents.permissions import get_document_count_filter_for_user
 from documents.permissions import get_permission_grantees
@@ -1022,71 +1022,83 @@ class DocumentViewSet(
         "custom_field_",
     )
 
-    def _get_selection_data_for_queryset(self, queryset):
-        # Resolve once instead of once per model below. `queryset` can carry an
-        # arbitrarily expensive WHERE clause (user filters plus the permission
-        # filter); re-embedding it as a subquery inside 5 separate Count(...)
-        # calls forces the database to re-evaluate that whole thing 5 times, and
-        # -- for FK relations especially -- can defeat semi-join planning
-        # entirely at scale. A concrete id list is cheap to reuse.
-        # order_by() drops the default/user ordering -- irrelevant for a plain
-        # id list, but left in place it forces a sort over the full filtered
-        # set before the ids can even be collected.
-        document_ids = list(queryset.order_by().values_list("pk", flat=True))
+    # Matching document ids are looked up in the link tables in batches of this
+    # size: one literal IN (...) over every match is a multi-MB statement per
+    # relation and, on SQLite, exceeds the bound-parameter limit.
+    selection_data_link_batch_size = 10_000
 
-        correspondents = Correspondent.objects.annotate(
-            document_count=Count(
-                "documents",
-                filter=Q(documents__id__in=document_ids),
-                distinct=True,
+    def _get_selection_data_for_queryset(self, queryset):
+        # The filtered queryset is evaluated once, as the page's own filter is,
+        # and never wrapped in a subquery: re-deriving a join or a HAVING
+        # (shared_by__id) per statement is very slow on some databases, and
+        # regrouping the queryset by another column would miscount.
+        # order_by() drops the default/user ordering, which only costs a sort.
+        correspondents: Counter = Counter()
+        document_types: Counter = Counter()
+        storage_paths: Counter = Counter()
+        document_ids: list[int] = []
+        seen: set[int] = set()
+        rows = queryset.order_by().values_list(
+            "pk",
+            "correspondent_id",
+            "document_type_id",
+            "storage_path_id",
+        )
+        for pk, correspondent, document_type, storage_path in rows:
+            # A filter can join, repeating a document row.
+            if pk in seen:
+                continue
+            seen.add(pk)
+            document_ids.append(pk)
+            # Documents without a value count under None, which selection()
+            # never looks up.
+            correspondents[correspondent] += 1
+            document_types[document_type] += 1
+            storage_paths[storage_path] += 1
+
+        def link_table_counts(link_model, field_name: str) -> Counter:
+            result: Counter = Counter()
+            batch_size = self.selection_data_link_batch_size
+            for start in range(0, len(document_ids), batch_size):
+                result.update(
+                    dict(
+                        link_model.objects.filter(
+                            document_id__in=document_ids[start : start + batch_size],
+                        )
+                        .values_list(field_name)
+                        .annotate(document_count=Count("document_id")),
                     ),
                 )
-        document_types = DocumentType.objects.annotate(
-            document_count=Count(
-                "documents",
-                filter=Q(documents__id__in=document_ids),
-                distinct=True,
-            ),
-        )
-        storage_paths = StoragePath.objects.annotate(
-            document_count=Count(
-                "documents",
-                filter=Q(documents__id__in=document_ids),
-                distinct=True,
-            ),
-        )
-        # Tag and CustomField reach Document through an M2M/through-model table;
-        # a plain Count(filter=...) there is a much more expensive plan than the
-        # FK relations above once the bridge table is large -- see
-        # annotate_document_count_by_ids() for why.
-        tags = annotate_document_count_by_ids(
-            Tag.objects.all(),
-            through_model=Document.tags.through,
-            related_object_field="tag_id",
-            document_ids=document_ids,
-        )
-        custom_fields = annotate_document_count_by_ids(
-            CustomField.objects.all(),
-            through_model=CustomFieldInstance,
-            related_object_field="field_id",
-            document_ids=document_ids,
-        )
+            return result
+
+        def selection(model, counts: Counter) -> list[dict[str, int]]:
+            # Every object is listed, with 0 for those with no matching document.
+            return [
+                {"id": pk, "document_count": counts.get(pk, 0)}
+                for pk in model.objects.values_list("pk", flat=True)
+            ]
+
         return {
-            "selected_correspondents": [
-                {"id": t.id, "document_count": t.document_count} for t in correspondents
-            ],
-            "selected_tags": [
-                {"id": t.id, "document_count": t.document_count} for t in tags
-            ],
-            "selected_document_types": [
-                {"id": t.id, "document_count": t.document_count} for t in document_types
-            ],
-            "selected_storage_paths": [
-                {"id": t.id, "document_count": t.document_count} for t in storage_paths
-            ],
-            "selected_custom_fields": [
-                {"id": t.id, "document_count": t.document_count} for t in custom_fields
-            ],
+            "selected_correspondents": selection(
+                Correspondent,
+                correspondents,
+            ),
+            "selected_tags": selection(
+                Tag,
+                link_table_counts(Document.tags.through, "tag_id"),
+            ),
+            "selected_document_types": selection(
+                DocumentType,
+                document_types,
+            ),
+            "selected_storage_paths": selection(
+                StoragePath,
+                storage_paths,
+            ),
+            "selected_custom_fields": selection(
+                CustomField,
+                link_table_counts(CustomFieldInstance, "field_id"),
+            ),
         }
 
     @classmethod

src/paperless_ai/tests/test_vector_store_chunk_size.py

@@ -1,93 +0,0 @@
-import sqlite3
-from collections.abc import Generator
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-from llama_index.core.schema import TextNode
-
-from paperless_ai.tables import DocumentChunksTable
-from paperless_ai.vector_store import DB_FILENAME
-from paperless_ai.vector_store import DEFAULT_TABLE_NAME
-from paperless_ai.vector_store import VEC0_CHUNK_SIZE
-from paperless_ai.vector_store import PaperlessSqliteVecVectorStore
-
-DIM = 16
-
-
-def make_node(node_id: str, document_id: int) -> TextNode:
-    node = TextNode(
-        id_=node_id,
-        text="some text",
-        metadata={"document_id": document_id, "modified": "2026-06-10T00:00:00"},
-    )
-    node.relationships = {}
-    node.embedding = [document_id + i / 100 for i in range(DIM)]
-    return node
-
-
-def vec_table_sql(db_path: Path) -> str:
-    conn = sqlite3.connect(db_path)
-    try:
-        return conn.execute(
-            "SELECT sql FROM sqlite_master WHERE name = ?",
-            (DEFAULT_TABLE_NAME,),
-        ).fetchone()[0]
-    finally:
-        conn.close()
-
-
-def create_legacy_vec_table(conn: sqlite3.Connection, dim: int) -> None:
-    """The table definition from before chunk_size was set explicitly."""
-    conn.execute(
-        f"CREATE VIRTUAL TABLE {DEFAULT_TABLE_NAME} USING vec0("
-        "id TEXT PRIMARY KEY, document_id INTEGER, +node_content TEXT, "
-        f"embedding float[{dim}] distance_metric=cosine)",
-    )
-
-
-@pytest.fixture
-def store(tmp_path: Path) -> Generator[PaperlessSqliteVecVectorStore, None, None]:
-    with PaperlessSqliteVecVectorStore(uri=str(tmp_path)) as store:
-        yield store
-
-
-class TestVecChunkSize:
-    def test_new_table_uses_configured_chunk_size(
-        self,
-        store: PaperlessSqliteVecVectorStore,
-        tmp_path: Path,
-    ) -> None:
-        """
-        GIVEN an empty store
-        WHEN the first document is indexed and the vec0 table is created
-        THEN the table definition carries the configured chunk size
-        """
-        store.upsert_document(1, [make_node("1-a", 1)])
-
-        assert f"chunk_size={VEC0_CHUNK_SIZE}" in vec_table_sql(tmp_path / DB_FILENAME)
-
-    def test_compact_moves_legacy_table_to_configured_chunk_size(
-        self,
-        store: PaperlessSqliteVecVectorStore,
-        tmp_path: Path,
-    ) -> None:
-        """
-        GIVEN an index whose vec0 table was created with the default chunk size
-        WHEN the index is compacted
-        THEN the rebuilt table uses the configured chunk size
-        AND no indexed chunks are lost
-        """
-        with patch.object(
-            PaperlessSqliteVecVectorStore,
-            "_create_vec_table",
-            staticmethod(create_legacy_vec_table),
-        ):
-            store.upsert_document(1, [make_node("1-a", 1), make_node("1-b", 1)])
-            store.upsert_document(2, [make_node("2-a", 2)])
-        assert "chunk_size" not in vec_table_sql(tmp_path / DB_FILENAME)
-
-        store.compact(force=True)
-
-        assert f"chunk_size={VEC0_CHUNK_SIZE}" in vec_table_sql(tmp_path / DB_FILENAME)
-        assert DocumentChunksTable.count(store.client) == 3

src/paperless_ai/tests/test_vector_store_page_size.py

@@ -1,89 +0,0 @@
-import sqlite3
-from collections.abc import Generator
-from pathlib import Path
-
-import pytest
-from llama_index.core.schema import TextNode
-
-from paperless_ai.tables import DocumentChunksTable
-from paperless_ai.vector_store import DB_FILENAME
-from paperless_ai.vector_store import DB_PAGE_SIZE
-from paperless_ai.vector_store import PaperlessSqliteVecVectorStore
-
-DIM = 16
-LEGACY_PAGE_SIZE = 4096
-
-
-def make_node(node_id: str, document_id: int) -> TextNode:
-    node = TextNode(
-        id_=node_id,
-        text="some text",
-        metadata={"document_id": document_id, "modified": "2026-06-10T00:00:00"},
-    )
-    node.relationships = {}
-    node.embedding = [document_id + i / 100 for i in range(DIM)]
-    return node
-
-
-def page_size_of(db_path: Path) -> int:
-    conn = sqlite3.connect(db_path)
-    try:
-        return conn.execute("PRAGMA page_size").fetchone()[0]
-    finally:
-        conn.close()
-
-
-@pytest.fixture
-def legacy_page_size_db(tmp_path: Path) -> Path:
-    """An existing index file created before the page size was set."""
-    db_path = tmp_path / DB_FILENAME
-    conn = sqlite3.connect(db_path)
-    conn.execute(f"PRAGMA page_size={LEGACY_PAGE_SIZE}")
-    conn.execute("CREATE TABLE _seed(x)")
-    conn.execute("DROP TABLE _seed")
-    conn.close()
-    assert page_size_of(db_path) == LEGACY_PAGE_SIZE
-    return db_path
-
-
-@pytest.fixture
-def store(tmp_path: Path) -> Generator[PaperlessSqliteVecVectorStore, None, None]:
-    with PaperlessSqliteVecVectorStore(uri=str(tmp_path)) as store:
-        yield store
-
-
-class TestIndexPageSize:
-    def test_new_index_file_uses_configured_page_size(
-        self,
-        store: PaperlessSqliteVecVectorStore,
-        tmp_path: Path,
-    ) -> None:
-        """
-        GIVEN no existing index file
-        WHEN a store is opened and a document is indexed
-        THEN the database file uses the configured page size
-        """
-        store.upsert_document(1, [make_node("1-a", 1)])
-
-        assert page_size_of(tmp_path / DB_FILENAME) == DB_PAGE_SIZE
-
-    def test_compact_moves_legacy_file_to_configured_page_size(
-        self,
-        legacy_page_size_db: Path,
-    ) -> None:
-        """
-        GIVEN an existing index file created with a different page size
-        WHEN the index is compacted
-        THEN the rebuilt file uses the configured page size
-        AND no indexed chunks are lost
-        """
-        with PaperlessSqliteVecVectorStore(
-            uri=str(legacy_page_size_db.parent),
-        ) as store:
-            store.upsert_document(1, [make_node("1-a", 1), make_node("1-b", 1)])
-            assert page_size_of(legacy_page_size_db) == LEGACY_PAGE_SIZE
-
-            store.compact(force=True)
-
-            assert page_size_of(legacy_page_size_db) == DB_PAGE_SIZE
-            assert DocumentChunksTable.count(store.client) == 2

src/paperless_ai/vector_store.py

@@ -47,20 +47,6 @@ SCHEMA_VERSION = 2
 # a rebuild copies the live rows into a fresh table.
 COMPACT_BLOAT_RATIO = 2.0
 
-# Rows per vec0 vector chunk (vec0's default is 1024, which makes each chunk a
-# multi-megabyte blob). On spinning disks, inserts and re-indexing were dozens
-# of times faster with small chunks, at no cost in query speed or file size.
-# Only applies to tables created from here on; an existing index picks it up
-# the next time it is rebuilt (compact or rebuild).
-VEC0_CHUNK_SIZE = 32
-
-# SQLite page size for the index file, in bytes. With the small vec0 chunks above,
-# 64 KiB pages made cold scans about 35% faster and warm ones about 45% faster,
-# at the cost of a larger write-ahead log (up to about 70 MB). Only takes effect
-# on a newly created file, so like the chunk size it reaches an existing index
-# when it is rebuilt.
-DB_PAGE_SIZE = 65536
-
 # Number of rows fetched/copied per batch whenever this module streams rows
 # instead of materializing them all at once, keeping memory bounded regardless
 # of index size -- used by compact()'s rebuild, m0001_v1_to_v2's migration
@@ -236,7 +222,6 @@ class PaperlessSqliteVecVectorStore(BasePydanticVectorStore):
         conn.enable_load_extension(True)  # noqa: FBT003
         sqlite_vec.load(conn)
         conn.enable_load_extension(False)  # noqa: FBT003
-        conn.execute(f"PRAGMA page_size={DB_PAGE_SIZE}")
         conn.execute("PRAGMA journal_mode=WAL")
         conn.execute("PRAGMA synchronous=NORMAL")
         IndexMetaTable.create(conn)
@@ -344,8 +329,7 @@ class PaperlessSqliteVecVectorStore(BasePydanticVectorStore):
             + " +node_content TEXT,"
             + " embedding float["
             + str(int(dim))
-            + "] distance_metric=cosine, chunk_size="
-            + str(VEC0_CHUNK_SIZE)
+            + "] distance_metric=cosine"
             + ")",
         )