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paperless-ngx/src/paperless_ai/tests/test_chat_single_retrieve.py
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stumpylogandClaude Sonnet 5.5 7fdeb173f5 Fix: retrieve once per document chat question
Document chat searched the vector store twice per question: once
explicitly to build the reference list, and again inside
RetrieverQueryEngine.query(), which retrieves before synthesizing. That
meant two query-embedding calls and two full vector searches each time.

The engine added nothing beyond the retriever call (no node
postprocessors are configured), so drop it and synthesize directly from
the nodes already retrieved. The streaming response is unchanged.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SRX1rCtSx62vSDaiFuBFCM
2026-10-30 16:58:50 -07:00

55 lines
1.8 KiB
Python

from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from documents.models import Document
from paperless_ai.chat import stream_chat_with_documents
from paperless_testing.factories import DocumentFactory
if TYPE_CHECKING:
import pytest_mock
@pytest.mark.django_db
class TestChatSingleRetrieve:
def test_question_retrieves_once_and_synthesizes_those_nodes(
self,
mocker: pytest_mock.MockerFixture,
) -> None:
"""
GIVEN a document with indexed content
WHEN a chat question is streamed
THEN the vector store is searched exactly once
AND the answer is synthesized from the nodes that search returned
"""
document = DocumentFactory.create(title="Test Document", content="ignored")
top_nodes = [
mocker.MagicMock(
metadata={"document_id": str(document.pk), "title": "Test Document"},
),
]
retriever = mocker.MagicMock()
retriever.retrieve.return_value = top_nodes
mocker.patch("paperless_ai.chat.AIClient")
mocker.patch("paperless_ai.chat.load_or_build_index")
mocker.patch(
"llama_index.core.retrievers.VectorIndexRetriever",
return_value=retriever,
)
synthesizer = mocker.patch(
"llama_index.core.response_synthesizers.get_response_synthesizer",
).return_value
synthesizer.synthesize.return_value.response_gen = iter(["chunk"])
list(
stream_chat_with_documents(
"What is this?",
Document.objects.filter(pk=document.pk),
),
)
retriever.retrieve.assert_called_once_with("What is this?")
synthesizer.synthesize.assert_called_once_with("What is this?", top_nodes)