Fix: pass document chat queries as a QuerySet instead of a materialized list

In tracemalloc based profiling, not materializing the whole Document list
reduced memory to approximately 20% of the baseline, with a peak memory
that scaled with the library size.  Now, the lazt queryset is used and only
the needed pk value is actually contributing to memory

src/documents/views.py

@@ -2267,7 +2267,7 @@ class ChatStreamingView(GenericAPIView[Any]):
             if not has_perms_owner_aware(request.user, "view_document", document):
                 return HttpResponseForbidden("Insufficient permissions")
 
-            documents = [document]
+            documents = Document.objects.filter(pk=document.pk)
         else:
             documents = Document.objects.filter(
                 id__in=permitted_document_ids(request.user),

src/paperless_ai/chat.py

@@ -2,6 +2,8 @@ import json
 import logging
 import sys
 
+from django.db.models import QuerySet
+
 from documents.models import Document
 from paperless.config import AIConfig
 from paperless_ai.client import AIClient
@@ -82,10 +84,21 @@ def _build_document_reference(
 
 
 def _get_document_references(
-    documents: list[Document],
+    documents: QuerySet[Document],
     top_nodes: list,
 ) -> list[dict[str, int | str]]:
-    allowed_documents = {doc.pk: doc for doc in documents}
+    candidate_ids: set[int] = set()
+    for node in top_nodes:
+        try:
+            candidate_ids.add(int(node.metadata["document_id"]))
+        except (KeyError, TypeError, ValueError):  # pragma: no cover
+            continue
+
+    if not candidate_ids:
+        return []
+
+    allowed_documents = {doc.pk: doc for doc in documents.filter(pk__in=candidate_ids)}
+
     references: list[dict[str, int | str]] = []
     seen_document_ids: set[int] = set()
 
@@ -119,7 +132,7 @@ def _format_chat_metadata_trailer(references: list[dict[str, int | str]]) -> str
 
 def stream_chat_with_documents(
     query_str: str,
-    documents: list[Document],
+    documents: QuerySet[Document],
     output_language: str | None = None,
 ):
     try:
@@ -135,10 +148,10 @@ def stream_chat_with_documents(
 
 def _stream_chat_with_documents(
     query_str: str,
-    documents: list[Document],
+    documents: QuerySet[Document],
     output_language: str | None = None,
 ):
-    if not documents:
+    if not documents.exists():
         yield CHAT_NO_CONTENT_MESSAGE
         return
 
@@ -148,7 +161,9 @@ def _stream_chat_with_documents(
     from llama_index.core.retrievers import VectorIndexRetriever
 
     config = AIConfig()
-    filters = _document_id_filters(str(doc.pk) for doc in documents)
+    filters = _document_id_filters(
+        str(pk) for pk in documents.values_list("pk", flat=True)
+    )
 
     # Hold the shared read lock for the whole operation: the query engine
     # retrieves from the vector store again during synthesis, so the connection

src/paperless_ai/tests/test_chat.py

@@ -3,10 +3,12 @@ from unittest.mock import MagicMock
 from unittest.mock import patch
 
 import pytest
+from django.db.models.signals import post_init
 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 documents.models import Document
 from documents.tests.factories import DocumentFactory
 from paperless_ai import chat
 from paperless_ai import indexing
@@ -36,16 +38,6 @@ def patch_embed_nodes():
         yield mock_embed_nodes
 
 
-@pytest.fixture
-def mock_document():
-    doc = MagicMock()
-    doc.pk = 1
-    doc.title = "Test Document"
-    doc.filename = "test_file.pdf"
-    doc.content = "This is the document content."
-    return doc
-
-
 def assert_chat_output(
     output: list[str],
     *,
@@ -61,6 +53,13 @@ def assert_chat_output(
     }
 
 
+def _fake_documents_queryset(pks: list[int]) -> MagicMock:
+    qs = MagicMock()
+    qs.exists.return_value = bool(pks)
+    qs.values_list.return_value = pks
+    return qs
+
+
 @pytest.mark.parametrize(
     ("output_language", "expected_language_line"),
     [
@@ -107,9 +106,10 @@ def test_build_refine_prompt(
 
 @pytest.mark.django_db
 def test_stream_chat_with_one_document_retrieval(
-    mock_document,
     patch_embed_nodes,
 ) -> None:
+    document = DocumentFactory.create(title="Test Document", content="ignored")
+    documents = Document.objects.filter(pk=document.pk)
     with (
         patch("paperless_ai.chat.AIClient") as mock_client_cls,
         patch("paperless_ai.chat.load_or_build_index") as mock_load_index,
@@ -124,22 +124,19 @@ def test_stream_chat_with_one_document_retrieval(
         mock_client_cls.return_value = mock_client
         mock_client.llm = MagicMock()
 
-        mock_node = TextNode(
-            text="This is node content.",
-            metadata={"document_id": str(mock_document.pk), "title": "Test Document"},
-        )
         mock_index = MagicMock()
-        # Simulate get_nodes returning nodes (content exists)
-        mock_index.vector_store.get_nodes.return_value = [mock_node]
+        mock_index.vector_store.get_nodes.return_value = [
+            TextNode(
+                text="This is node content.",
+                metadata={"document_id": str(document.pk), "title": "Test Document"},
+            ),
+        ]
         mock_load_index.return_value = mock_index
 
         mock_retriever_instance = MagicMock()
         mock_retriever_instance.retrieve.return_value = [
             MagicMock(
-                metadata={
-                    "document_id": str(mock_document.pk),
-                    "title": "Test Document",
-                },
+                metadata={"document_id": str(document.pk), "title": "Test Document"},
             ),
         ]
 
@@ -153,7 +150,7 @@ def test_stream_chat_with_one_document_retrieval(
             "llama_index.core.retrievers.VectorIndexRetriever",
             return_value=mock_retriever_instance,
         ):
-            output = list(stream_chat_with_documents("What is this?", [mock_document]))
+            output = list(stream_chat_with_documents("What is this?", documents))
 
         mock_query_engine.query.assert_called_once_with("What is this?")
         synthesizer_kwargs = mock_get_response_synthesizer.call_args.kwargs
@@ -166,13 +163,16 @@ def test_stream_chat_with_one_document_retrieval(
             output,
             expected_chunks=["chunk1", "chunk2"],
             expected_references=[
-                {"id": mock_document.pk, "title": "Test Document"},
+                {"id": document.pk, "title": "Test Document"},
             ],
         )
 
 
 @pytest.mark.django_db
 def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> None:
+    doc1 = DocumentFactory.create(title="Document 1", content="ignored")
+    doc2 = DocumentFactory.create(title="Document 2", content="ignored")
+    documents = Document.objects.filter(pk__in=[doc1.pk, doc2.pk])
     with (
         patch("paperless_ai.chat.AIClient") as mock_client_cls,
         patch("paperless_ai.chat.load_or_build_index") as mock_load_index,
@@ -184,23 +184,23 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
         mock_client_cls.return_value = mock_client
         mock_client.llm = MagicMock()
 
-        mock_node1 = TextNode(
-            text="Content for doc 1.",
-            metadata={"document_id": "1", "title": "Document 1"},
-        )
-        mock_node2 = TextNode(
-            text="Content for doc 2.",
-            metadata={"document_id": "2", "title": "Document 2"},
-        )
         mock_index = MagicMock()
-        # Simulate get_nodes returning nodes (content exists)
-        mock_index.vector_store.get_nodes.return_value = [mock_node1, mock_node2]
+        mock_index.vector_store.get_nodes.return_value = [
+            TextNode(
+                text="Content for doc 1.",
+                metadata={"document_id": str(doc1.pk), "title": "Document 1"},
+            ),
+            TextNode(
+                text="Content for doc 2.",
+                metadata={"document_id": str(doc2.pk), "title": "Document 2"},
+            ),
+        ]
         mock_load_index.return_value = mock_index
 
         mock_retriever_instance = MagicMock()
         mock_retriever_instance.retrieve.return_value = [
-            MagicMock(metadata={"document_id": "1", "title": "Document 1"}),
-            MagicMock(metadata={"document_id": "2", "title": "Document 2"}),
+            MagicMock(metadata={"document_id": str(doc1.pk), "title": "Document 1"}),
+            MagicMock(metadata={"document_id": str(doc2.pk), "title": "Document 2"}),
         ]
 
         mock_response_stream = MagicMock()
@@ -210,14 +210,11 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
         mock_query_engine_cls.return_value = mock_query_engine
         mock_query_engine.query.return_value = mock_response_stream
 
-        doc1 = MagicMock(pk=1, title="Document 1", filename="doc1.pdf")
-        doc2 = MagicMock(pk=2, title="Document 2", filename="doc2.pdf")
-
         with patch(
             "llama_index.core.retrievers.VectorIndexRetriever",
             return_value=mock_retriever_instance,
         ):
-            output = list(stream_chat_with_documents("What's up?", [doc1, doc2]))
+            output = list(stream_chat_with_documents("What's up?", documents))
 
         mock_query_engine.query.assert_called_once_with("What's up?")
         patch_embed_nodes.assert_not_called()
@@ -225,15 +222,15 @@ def test_stream_chat_with_multiple_documents_retrieval(patch_embed_nodes) -> Non
             output,
             expected_chunks=["chunk1", "chunk2"],
             expected_references=[
-                {"id": 1, "title": "Document 1"},
-                {"id": 2, "title": "Document 2"},
+                {"id": doc1.pk, "title": "Document 1"},
+                {"id": doc2.pk, "title": "Document 2"},
             ],
         )
 
 
 def test_stream_chat_empty_document_list() -> None:
     with patch("paperless_ai.chat.load_or_build_index") as mock_load_index:
-        output = list(stream_chat_with_documents("Any info?", []))
+        output = list(stream_chat_with_documents("Any info?", Document.objects.none()))
         mock_load_index.assert_not_called()
         assert output == ["Sorry, I couldn't find any content to answer your question."]
 
@@ -253,7 +250,9 @@ def test_stream_chat_no_matching_nodes() -> None:
         mock_index.vector_store.get_nodes.return_value = []
         mock_load_index.return_value = mock_index
 
-        output = list(stream_chat_with_documents("Any info?", [MagicMock(pk=1)]))
+        output = list(
+            stream_chat_with_documents("Any info?", _fake_documents_queryset([1])),
+        )
 
         assert output == ["Sorry, I couldn't find any content to answer your question."]
 
@@ -282,7 +281,9 @@ def test_stream_chat_unexpected_failure_returns_generic_error(caplog) -> None:
             )
             mock_retriever_cls.return_value = mock_retriever
 
-            output = list(stream_chat_with_documents("Any info?", [MagicMock(pk=1)]))
+            output = list(
+                stream_chat_with_documents("Any info?", _fake_documents_queryset([1])),
+            )
 
         assert output == [CHAT_ERROR_MESSAGE]
         assert "Failed to stream document chat response" in caplog.text
@@ -298,7 +299,12 @@ class TestStreamChatRetrieval:
     ) -> None:
         doc = DocumentFactory.create(content="hello world")
         # Nothing indexed for this document yet.
-        out = list(chat.stream_chat_with_documents("question?", [doc]))
+        out = list(
+            chat.stream_chat_with_documents(
+                "question?",
+                Document.objects.filter(pk=doc.pk),
+            ),
+        )
         assert chat.CHAT_NO_CONTENT_MESSAGE in out
 
     def test_chat_filter_contains_only_requested_document_ids(
@@ -332,7 +338,12 @@ class TestStreamChatRetrieval:
             side_effect=capture_retriever,
         )
 
-        list(chat.stream_chat_with_documents("question?", [included]))
+        list(
+            chat.stream_chat_with_documents(
+                "question?",
+                Document.objects.filter(pk=included.pk),
+            ),
+        )
 
         assert captured_filters, "VectorIndexRetriever was never constructed"
         filt = captured_filters[0]
@@ -340,3 +351,47 @@ class TestStreamChatRetrieval:
         filter_values = filt.filters[0].value
         assert str(included.pk) in filter_values
         assert str(excluded.pk) not in filter_values
+
+    @pytest.mark.django_db
+    def test_get_document_references_only_queries_referenced_documents(
+        self,
+        django_assert_num_queries,
+    ) -> None:
+        """Building references must not hydrate every document the caller is
+        permitted to see -- only the (<= CHAT_RETRIEVER_TOP_K) documents that
+        the retriever actually returned nodes for.
+        """
+        referenced = DocumentFactory.create(title="Referenced Document")
+        # Many more documents are "accessible" but never referenced by a node.
+        DocumentFactory.create_batch(200)
+
+        documents = Document.objects.all()
+        top_nodes = [
+            MagicMock(
+                metadata={
+                    "document_id": str(referenced.pk),
+                    "title": "Referenced Document",
+                },
+            ),
+        ]
+
+        hydrated_count = 0
+
+        def _count_hydration(sender, instance, **kwargs):
+            nonlocal hydrated_count
+            hydrated_count += 1
+
+        post_init.connect(_count_hydration, sender=Document)
+        try:
+            # One query: `documents.filter(pk__in=candidate_ids)` for the single
+            # referenced id. No query should scale with the 200 unreferenced documents.
+            with django_assert_num_queries(1):
+                references = chat._get_document_references(documents, top_nodes)
+        finally:
+            post_init.disconnect(_count_hydration, sender=Document)
+
+        # The bug this guards against: the old code hydrated all 201 accessible
+        # documents via `{doc.pk: doc for doc in documents}` before filtering by
+        # top_nodes. Only the referenced document should ever be constructed.
+        assert hydrated_count == 1
+        assert references == [{"id": referenced.pk, "title": "Referenced Document"}]