New Crowdin translations by GitHub Action

src/paperless_ai/chat.py

@@ -46,16 +46,6 @@ def _build_document_reference(
     }
 
 
-def _permitted_documents_filter(store, permitted_ids: set[int]):
-    """Scope retrieval to ``permitted_ids`` using whichever of an inclusion or
-    exclusion list is shorter, since a long inclusion list is much slower for
-    the vector store than a long exclusion list."""
-    denied_ids = store.indexed_document_ids() - permitted_ids
-    if len(permitted_ids) <= len(denied_ids):
-        return document_id_filters(str(pk) for pk in permitted_ids)
-    return exclude_document_ids_filter(str(pk) for pk in denied_ids)
-
-
 def _get_document_references(
     documents: QuerySet[Document],
     top_nodes: list,
@@ -138,23 +128,24 @@ def _stream_chat_with_documents(
     from llama_index.core.retrievers import VectorIndexRetriever
 
     config = AIConfig()
-    permitted_ids: set[int] | None = None
     if unrestricted:
-        # Exclude trashed ids (usually few) rather than send every permitted
-        # id on each query. Trashed documents stay indexed until permanent
-        # deletion (post_delete, not trash), so must be excluded explicitly.
+        # Exclude trashed ids (usually few) instead of an IN filter over the
+        # full permitted set, which risks the vector store's bound parameter
+        # limit (_MAX_IN_VALUES) on large installs. Trashed documents stay
+        # indexed until permanent deletion (delete_document_from_llm_index
+        # hangs off post_delete, not trash), so must be excluded explicitly.
         trashed_ids = Document.deleted_objects.values_list("pk", flat=True)
         filters = exclude_document_ids_filter(str(pk) for pk in trashed_ids)
     else:
-        permitted_ids = set(documents.values_list("pk", flat=True))
+        filters = document_id_filters(
+            str(pk) for pk in documents.values_list("pk", flat=True)
+        )
 
     # Hold the shared read lock only while reading the index, so the compaction
     # swap cannot replace the database file while this connection is open.
     # Everything after retrieval works on the nodes already in memory, so a slow
     # LLM stream never holds the lock.
     with read_store() as store:
-        if permitted_ids is not None:
-            filters = _permitted_documents_filter(store, permitted_ids)
         index = load_or_build_index(config, store)
         retriever = VectorIndexRetriever(
             index=index,
@@ -168,15 +159,6 @@ def _stream_chat_with_documents(
         with db_connection_released():
             top_nodes = retriever.retrieve(query_str)
 
-    if permitted_ids is not None:
-        # An exclusion filter fails open for a document indexed after the
-        # permitted set was read, so drop anything not explicitly permitted.
-        top_nodes = [
-            node
-            for node in top_nodes
-            if int(node.metadata["document_id"]) in permitted_ids
-        ]
-
     if not top_nodes:
         logger.warning("No nodes found for the given documents.")
         yield CHAT_NO_CONTENT_MESSAGE

src/paperless_ai/tables.py

@@ -141,15 +141,6 @@ class DocumentMetaTable:
                 (DocumentMetaRow(r["document_id"], r["modified"]) for r in batch),
             )
 
-    @staticmethod
-    def all_document_ids(conn: sqlite3.Connection) -> set[int]:
-        """Every indexed document_id. document_meta has one row per document,
-        so this is a cheap primary-key scan, unlike reading the vec0 table."""
-        return {
-            row["document_id"]
-            for row in conn.execute("SELECT document_id FROM document_meta")
-        }
-
     @staticmethod
     def all_modified_times(conn: sqlite3.Connection) -> dict[str, str]:
         """Full document_id -> modified map, for get_modified_times()'s

src/paperless_ai/tests/test_chat.py

@@ -13,7 +13,6 @@ from django.utils import timezone
 from llama_index.core import settings as llama_settings
 from llama_index.core.embeddings.mock_embed_model import MockEmbedding
 from llama_index.core.schema import TextNode
-from llama_index.core.vector_stores.types import FilterOperator
 
 from documents.models import Document
 from paperless_ai import chat
@@ -413,8 +412,10 @@ class TestStreamChatRetrieval:
         WHEN:
             - stream_chat_with_documents is called with unrestricted=True
         THEN:
-            - The retriever receives a NOT IN filter excluding zero ids
-              rather than an IN list of every permitted id
+            - The retriever receives a NOT IN filter excluding zero ids, so
+              the whole index is effectively searched -- and no IN-list is
+              built from the full permitted set, which is what risks the
+              vector store's safety limit on large installs
         """
         document = DocumentFactory.create(content="indexed document content")
         indexing.llm_index_add_or_update_document(document)
@@ -467,70 +468,6 @@ class TestStreamChatRetrieval:
         assert str(trashed.pk) in filter_values
         assert str(kept.pk) not in filter_values
 
-    def test_restricted_chat_excludes_denied_ids_when_few_are_denied(
-        self,
-        temp_llm_index_dir: Path,
-        mock_embed_model: pytest_mock.MockType,
-        captured_filters: list[Any],
-    ) -> None:
-        """
-        GIVEN:
-            - Three indexed documents, one of which the caller may not see
-        WHEN:
-            - A restricted caller chats
-        THEN:
-            - The retriever receives a NOT IN filter on the denied id, not an
-              IN list of the permitted ids
-        """
-        docs = DocumentFactory.create_batch(3, content="indexed document content")
-        for doc in docs:
-            indexing.llm_index_add_or_update_document(doc)
-        permitted = Document.objects.filter(pk__in=[docs[0].pk, docs[1].pk])
-
-        list(chat.stream_chat_with_documents("question?", permitted))
-
-        metadata_filter = captured_filters[0].filters[0]
-        assert metadata_filter.operator == FilterOperator.NIN
-        assert metadata_filter.value == [str(docs[2].pk)]
-
-    def test_restricted_chat_drops_retrieved_nodes_of_unpermitted_documents(
-        self,
-        temp_llm_index_dir: Path,
-        mock_embed_model: pytest_mock.MockType,
-        mocker: pytest_mock.MockerFixture,
-    ) -> None:
-        """
-        GIVEN:
-            - A retriever that returns a node for a document the caller may
-              not see, as when it is indexed after the permitted set was read
-        WHEN:
-            - A restricted caller chats
-        THEN:
-            - Only the node of the permitted document reaches the synthesizer
-        """
-        permitted_doc = DocumentFactory.create(content="permitted content")
-        denied_doc = DocumentFactory.create(content="denied content")
-        permitted_node = MagicMock(metadata={"document_id": str(permitted_doc.pk)})
-        denied_node = MagicMock(metadata={"document_id": str(denied_doc.pk)})
-        mocker.patch("paperless_ai.chat.AIClient")
-        retriever = mocker.patch(
-            "llama_index.core.retrievers.VectorIndexRetriever",
-        ).return_value
-        retriever.retrieve.return_value = [denied_node, permitted_node]
-        synthesizer = mocker.patch(
-            "llama_index.core.response_synthesizers.get_response_synthesizer",
-        ).return_value
-        synthesizer.synthesize.return_value.response_gen = iter(["chunk"])
-
-        list(
-            chat.stream_chat_with_documents(
-                "question?",
-                Document.objects.filter(pk=permitted_doc.pk),
-            ),
-        )
-
-        synthesizer.synthesize.assert_called_once_with("question?", [permitted_node])
-
     @pytest.mark.django_db
     def test_get_document_references_only_queries_referenced_documents(
         self,

src/paperless_ai/tests/test_vector_store.py

@@ -18,7 +18,7 @@ from paperless_ai.migrations import Migration
 from paperless_ai.migrations import m0001_v1_to_v2
 from paperless_ai.tables import DocumentChunksTable
 from paperless_ai.tables import DocumentMetaTable
-from paperless_ai.vector_store import _MAX_INLINE_IN_VALUES
+from paperless_ai.vector_store import _MAX_IN_VALUES
 from paperless_ai.vector_store import DB_FILENAME
 from paperless_ai.vector_store import DB_PAGE_SIZE
 from paperless_ai.vector_store import DEFAULT_TABLE_NAME
@@ -354,39 +354,37 @@ class TestBuildWhere:
         assert params == []
 
     @pytest.mark.parametrize(
-        ("build_filter", "expected_ids"),
-        [
-            pytest.param(_in_filter, ["a", "c"], id="in"),
-            pytest.param(_nin_filter, ["b"], id="nin"),
-        ],
+        "build_filter",
+        [_in_filter, _nin_filter],
+        ids=["in", "nin"],
     )
-    def test_query_with_oversized_filter_scopes_results(
+    def test_fails_closed_when_filter_exceeds_max_values(
         self,
-        store: PaperlessSqliteVecVectorStore,
         build_filter: Callable[[list[str]], MetadataFilters],
-        expected_ids: list[str],
+        caplog: pytest.LogCaptureFixture,
     ) -> None:
         """
         GIVEN:
-            - A store holding documents 1, 2 and 40000
-            - An IN or NOT IN filter naming documents 1 and 40000, padded
-              past _MAX_INLINE_IN_VALUES with unindexed ids
+            - An IN or NOT IN filter with more values than _MAX_IN_VALUES
+              (SQLite's own bound-parameter limit is 32766; this guard sits
+              below that with headroom for the query's other bound parameters)
         WHEN:
-            - The store is queried and get_nodes() is called with that filter
+            - _build_where() translates it to SQL
         THEN:
-            - Both return exactly the documents the filter allows
+            - It fails closed ("1 = 0", no params) instead of building a
+              clause SQLite would reject, and logs a warning -- this filter
+              scopes document access, so refusing to build it must never
+              widen the scope to "everything" by accident. Failing open on
+              a NOT IN would surface exactly the excluded rows
         """
-        store.add(
-            [make_node("a", 1), make_node("b", 2), make_node("c", 40000)],
-        )
-        padding = range(100, 100 + _MAX_INLINE_IN_VALUES)
-        filters = build_filter([str(i) for i in (1, 40000, *padding)])
+        oversized = build_filter([str(i) for i in range(_MAX_IN_VALUES + 1)])
 
-        result = _query(store, [0.0] * DIM, top_k=5, filters=filters)
-        nodes = store.get_nodes(filters=filters)
+        with caplog.at_level("WARNING"):
+            where, params = _build_where(oversized)
 
-        assert sorted(result.ids) == expected_ids
-        assert sorted(n.node_id for n in nodes) == expected_ids
+        assert where == "(1 = 0)"
+        assert params == []
+        assert "document_id" in caplog.text
 
     def test_query_with_untranslatable_filter_returns_no_rows(
         self,

src/paperless_ai/vector_store.py

@@ -89,10 +89,15 @@ class _Row(NamedTuple):
     embedding: bytes
 
 
-# _build_where(): the largest IN / NOT IN list bound as one parameter per value,
-# leaving headroom under SQLite's 32766-variable limit for the query's other
-# parameters. Longer lists are bound as a single JSON array via json_each().
-_MAX_INLINE_IN_VALUES = 32700
+# _build_where(): the largest IN value list translated into bound SQL
+# parameters. SQLite's own hard limit (SQLITE_MAX_VARIABLE_NUMBER) is 32766
+# by default; this leaves headroom below that for the query's other bound
+# parameters (the embedding blob, k, and any NE clause) and for the limit
+# itself to move. An IN filter this large should not happen in practice --
+# callers are expected to pass None (no filter) rather than every id when
+# the filter would not actually narrow anything -- so this is a guard
+# against a future regression, not a normal code path.
+_MAX_IN_VALUES = 32700
 
 
 def _pack(embedding: Sequence[float]) -> bytes:
@@ -115,7 +120,7 @@ def _vec0_params(rows: list[_Row]) -> list[tuple[str, int, str, bytes]]:
     return [(r.chunk_id, r.document_id, r.node_content, r.embedding) for r in rows]
 
 
-def _build_where(filters: MetadataFilters | None) -> tuple[str, list[int | str]]:
+def _build_where(filters: MetadataFilters | None) -> tuple[str, list[int]]:
     """Translate the EQ / IN / NIN / NE filters we use into a parameterized
     SQL clause on vec0 metadata columns. Returns ("", []) when there is
     nothing to filter. document_id is vec0's only filterable column and is
@@ -127,7 +132,7 @@ def _build_where(filters: MetadataFilters | None) -> tuple[str, list[int | str]]
     if filters is None or not filters.filters:
         return "", []
     clauses: list[str] = []
-    params: list[int | str] = []
+    params: list[int] = []
     for f in filters.filters:
         # filters.filters is Union[MetadataFilter, ExactMatchFilter, MetadataFilters];
         # we only build MetadataFilter entries, so skip anything else at runtime.
@@ -144,11 +149,21 @@ def _build_where(filters: MetadataFilters | None) -> tuple[str, list[int | str]]
                 # excludes nothing, so it matches everything.
                 clauses.append("1 = 0" if is_in else "1 = 1")
                 continue
-            if len(values) > _MAX_INLINE_IN_VALUES:
-                clauses.append(
-                    f"{f.key} {sql_op} (SELECT value FROM json_each(?))",
+            if len(values) > _MAX_IN_VALUES:
+                # Refuse rather than risk SQLite's own bound-parameter limit
+                # ("too many SQL variables"): a list this large must match no
+                # rows, never widen the scope to "everything" -- true for
+                # NOT IN too, where failing open would surface every
+                # excluded row.
+                logger.warning(
+                    "Refusing to build a %s filter on %r with %d values "
+                    "(over the %d-value safety limit); returning no rows.",
+                    sql_op,
+                    f.key,
+                    len(values),
+                    _MAX_IN_VALUES,
                 )
-                params.append(json.dumps(values))
+                clauses.append("1 = 0")
                 continue
             placeholders = ",".join("?" for _ in values)
             clauses.append(f"{f.key} {sql_op} ({placeholders})")
@@ -543,12 +558,6 @@ class PaperlessSqliteVecVectorStore(BasePydanticVectorStore):
             return {}
         return DocumentMetaTable.all_modified_times(self._conn)
 
-    def indexed_document_ids(self) -> set[int]:
-        """Return the ids of all indexed documents."""
-        if not self.table_exists():
-            return set()
-        return DocumentMetaTable.all_document_ids(self._conn)
-
     @property
     def _db_path(self) -> str:
         return str(Path(self._uri) / DB_FILENAME)