643 lines
23 KiB
Python
643 lines
23 KiB
Python
"""A3: SearchResult.image_data, expand_context preservation, multimodal ToolReturn."""
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import base64
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from dataclasses import dataclass
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from unittest.mock import AsyncMock
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import pytest
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from pydantic_ai import RunContext
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from pydantic_ai.messages import BinaryContent, ToolReturn
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from pydantic_ai.models.test import TestModel
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from pydantic_ai.usage import RunUsage
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from haiku.rag.client import HaikuRAG
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from haiku.rag.client.search import _populate_image_data
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from haiku.rag.config import Config
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from haiku.rag.context import expand_with_items
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from haiku.rag.store.models.chunk import SearchResult
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from haiku.rag.store.models.document_item import DocumentItem
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from haiku.rag.tools.search import create_search_toolset
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PICTURE_BYTES = b"\x89PNG\r\n\x1a\nfake-picture-bytes"
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PICTURE_B64 = base64.b64encode(PICTURE_BYTES).decode("ascii")
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@pytest.mark.asyncio
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async def test_populate_image_data_attaches_base64(temp_db_path):
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async with HaikuRAG(temp_db_path, create=True) as rag:
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await rag.document_item_repository.create_items(
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"doc-1",
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[
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DocumentItem(
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document_id="doc-1",
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position=0,
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self_ref="#/texts/0",
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label="paragraph",
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text="Some text",
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),
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DocumentItem(
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document_id="doc-1",
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position=1,
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self_ref="#/pictures/0",
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label="picture",
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text="",
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picture_data=PICTURE_BYTES,
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),
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],
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)
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text_only = SearchResult(
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content="Some text",
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score=1.0,
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document_id="doc-1",
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doc_item_refs=["#/texts/0"],
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labels=["paragraph"],
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)
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with_picture = SearchResult(
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content="A figure caption",
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score=0.9,
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document_id="doc-1",
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doc_item_refs=["#/texts/0", "#/pictures/0"],
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labels=["paragraph", "picture"],
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)
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await _populate_image_data(rag, [text_only, with_picture])
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# Text-only result is unchanged
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assert text_only.image_data is None
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# Picture-bearing result has the bytes attached
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assert with_picture.image_data == {"#/pictures/0": PICTURE_B64}
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@pytest.mark.asyncio
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async def test_client_search_include_images_false_skips_lookup(temp_db_path):
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"""include_images=False must short-circuit the picture-bytes lookup."""
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async with HaikuRAG(temp_db_path, create=True) as rag:
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await rag.document_item_repository.create_items(
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"doc-1",
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[
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DocumentItem(
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document_id="doc-1",
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position=0,
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self_ref="#/pictures/0",
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label="picture",
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picture_data=PICTURE_BYTES,
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),
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],
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)
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# Spy that we never reach the picture-bytes accessor
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rag.document_item_repository.get_pictures_for_chunk = AsyncMock( # type: ignore[method-assign]
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wraps=rag.document_item_repository.get_pictures_for_chunk
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)
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from haiku.rag.client.search import search
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# Stub the chunk-search results so we don't depend on embeddings/FTS
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async def fake_chunk_search(*args, **kwargs):
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return []
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rag.chunk_repository.search = fake_chunk_search # type: ignore[method-assign]
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await search(rag, "anything", include_images=False)
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rag.document_item_repository.get_pictures_for_chunk.assert_not_called()
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@pytest.mark.asyncio
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async def test_expand_context_preserves_picture_refs_with_empty_text(temp_db_path):
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"""A picture item with empty text must keep its self_ref through expansion."""
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async with HaikuRAG(temp_db_path, create=True) as rag:
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# Build an items table with a section header + a paragraph match + an
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# adjacent picture row that has no text. expand_with_items used to
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# filter the picture out via the `if item.text:` guard; A3 keeps it.
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await rag.document_item_repository.create_items(
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"doc-1",
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[
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DocumentItem(
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document_id="doc-1",
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position=0,
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self_ref="#/texts/0",
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label="section_header",
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text="Methods",
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),
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DocumentItem(
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document_id="doc-1",
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position=1,
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self_ref="#/texts/1",
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label="paragraph",
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text="The figure below shows the architecture.",
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),
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DocumentItem(
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document_id="doc-1",
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position=2,
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self_ref="#/pictures/0",
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label="picture",
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text="",
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picture_data=PICTURE_BYTES,
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),
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DocumentItem(
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document_id="doc-1",
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position=3,
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self_ref="#/texts/2",
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label="paragraph",
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text="More commentary follows.",
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),
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],
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)
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# Match on the paragraph that mentions the figure.
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seed = SearchResult(
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content="The figure below shows the architecture.",
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score=1.0,
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document_id="doc-1",
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doc_item_refs=["#/texts/1"],
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labels=["paragraph"],
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)
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expanded = await expand_with_items(
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rag.document_item_repository,
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"doc-1",
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[seed],
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max_chars=10_000,
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)
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assert len(expanded) == 1
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out = expanded[0]
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assert "#/pictures/0" in out.doc_item_refs, (
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"Picture self_ref should survive expansion even with empty text"
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)
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assert "picture" in out.labels
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@pytest.mark.asyncio
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async def test_rechunk_preserves_picture_data(temp_db_path):
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"""``rebuild --rechunk`` keeps ``picture_data`` for every picture row."""
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from haiku.rag.client import RebuildMode
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from haiku.rag.client.documents import _store_document_with_chunks
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from haiku.rag.store.models.document import Document
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from tests.store.test_document_items import _docling_doc_with_picture
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docling_doc = _docling_doc_with_picture()
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async with HaikuRAG(temp_db_path, create=True) as rag:
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rag._config.processing.pictures = "image"
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document = Document(content="x", uri="test://doc")
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document.set_docling(docling_doc)
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created = await _store_document_with_chunks(rag, document, [], docling_doc)
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assert created.id is not None
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before = await rag.document_item_repository.get_all_picture_data(created.id)
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assert before.get("#/pictures/0") is not None
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async for _ in rag.rebuild_database(mode=RebuildMode.RECHUNK):
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pass
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after = await rag.document_item_repository.get_all_picture_data(created.id)
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assert after.get("#/pictures/0") == before.get("#/pictures/0")
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@pytest.mark.asyncio
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async def test_update_clears_picture_data_when_mode_none(temp_db_path):
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"""Switching to ``pictures="none"`` and re-running update_document
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drops picture_data — the snapshot/merge gate is what gives users a
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"rebuild reclaims storage" path when they downgrade modes."""
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from haiku.rag.client.documents import (
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_store_document_with_chunks,
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_update_document_with_chunks,
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)
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from haiku.rag.store.models.document import Document
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from tests.store.test_document_items import _docling_doc_with_picture
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docling_doc = _docling_doc_with_picture()
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async with HaikuRAG(temp_db_path, create=True) as rag:
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# Ingest under "image" so picture bytes land in document_items.
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rag._config.processing.pictures = "image"
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document = Document(content="x", uri="test://doc")
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document.set_docling(docling_doc)
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created = await _store_document_with_chunks(rag, document, [], docling_doc)
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assert created.id is not None
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before = await rag.document_item_repository.get_all_picture_data(created.id)
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assert before.get("#/pictures/0") is not None
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# Downgrade to "none" and re-run update with the (already stripped)
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# docling pulled from storage. The snapshot/merge must be skipped so
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# picture_data is cleared on the new items rows.
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rag._config.processing.pictures = "none"
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from_blob = created.get_docling_document()
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assert from_blob is not None
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await _update_document_with_chunks(rag, created, [], from_blob)
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after = await rag.document_item_repository.get_all_picture_data(created.id)
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assert after.get("#/pictures/0") is None
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@pytest.mark.asyncio
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async def test_expand_context_repopulates_image_data(temp_db_path):
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"""expand_context rebuilds SearchResult objects via expand_with_items, so
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it must re-attach picture bytes — otherwise vision flows downstream see
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empty image_data after expansion."""
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async with HaikuRAG(temp_db_path, create=True) as rag:
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await rag.document_item_repository.create_items(
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"doc-1",
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[
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DocumentItem(
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document_id="doc-1",
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position=0,
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self_ref="#/texts/0",
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label="section_header",
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text="Methods",
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),
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DocumentItem(
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document_id="doc-1",
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position=1,
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self_ref="#/texts/1",
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label="paragraph",
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text="The figure below shows the architecture.",
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),
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DocumentItem(
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document_id="doc-1",
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position=2,
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self_ref="#/pictures/0",
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label="picture",
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text="",
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picture_data=PICTURE_BYTES,
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),
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],
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)
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seed = SearchResult(
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content="The figure below shows the architecture.",
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score=1.0,
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document_id="doc-1",
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doc_item_refs=["#/texts/1"],
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labels=["paragraph"],
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image_data=None,
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)
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expanded = await rag.expand_context([seed])
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assert len(expanded) == 1
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out = expanded[0]
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assert "#/pictures/0" in out.doc_item_refs
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assert out.image_data == {"#/pictures/0": PICTURE_B64}
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@dataclass
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class _Deps:
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client: object
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@pytest.mark.asyncio
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async def test_search_tool_returns_multimodal_when_picture_present():
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"""The agent-facing search tool must wrap text + BinaryContent in ToolReturn
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whenever a result carries picture image_data AND the QA model is vision-capable."""
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from haiku.rag.config import AppConfig
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picture_result = SearchResult(
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content="A diagram of the layout",
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score=1.0,
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chunk_id="chunk-1",
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document_id="doc-1",
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doc_item_refs=["#/pictures/0"],
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labels=["picture"],
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image_data={"#/pictures/0": PICTURE_B64},
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)
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fake_client = AsyncMock()
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fake_client.search = AsyncMock(return_value=[picture_result])
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fake_client.expand_context = AsyncMock(return_value=[picture_result])
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config = AppConfig()
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config.qa.model.vision = True
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toolset = create_search_toolset(config, expand_context=False)
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func = toolset.tools["search"].function
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ctx = RunContext(
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deps=_Deps(client=fake_client), # type: ignore[arg-type]
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model=TestModel(),
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usage=RunUsage(),
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run_id="run-1",
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)
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result = await func(ctx, "anything")
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assert isinstance(result, ToolReturn)
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assert isinstance(result.return_value, str)
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assert "Type: picture" in result.return_value or "rank 1" in result.return_value
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assert result.content is not None
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assert len(result.content) == 1
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part = result.content[0]
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assert isinstance(part, BinaryContent)
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assert part.media_type == "image/png"
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assert part.identifier == "#/pictures/0"
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assert part.data == PICTURE_BYTES
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# B2: synthetic picture chunks at ingestion
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def test_build_picture_chunks_uses_live_uri():
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from haiku.rag.client.processing import build_picture_chunks
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from tests.store.test_document_items import _docling_doc_with_picture
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doc = _docling_doc_with_picture()
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chunks = build_picture_chunks(doc, document_id="doc-1")
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assert len(chunks) == 1
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chunk = chunks[0]
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assert chunk.metadata["doc_item_refs"] == ["#/pictures/0"]
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assert chunk.metadata["labels"] == ["picture"]
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assert chunk._picture_data is not None
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assert chunk._picture_data.startswith(b"\x89PNG")
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assert chunk.document_id == "doc-1"
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def test_build_picture_chunks_falls_back_to_existing_picture_data():
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"""When the live docling has its picture URIs stripped, the snapshot
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fills the gap so rebuild round-trips don't lose picture chunks."""
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from haiku.rag.client.processing import build_picture_chunks
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from tests.store.test_document_items import _docling_doc_with_picture
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doc = _docling_doc_with_picture()
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for picture in doc.pictures:
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picture.image = None
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chunks = build_picture_chunks(
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doc,
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document_id="doc-1",
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existing_picture_data={"#/pictures/0": b"snapshot-bytes"},
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)
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assert len(chunks) == 1
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assert chunks[0]._picture_data == b"snapshot-bytes"
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def test_build_picture_chunks_skips_pictures_without_bytes():
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from haiku.rag.client.processing import build_picture_chunks
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from tests.store.test_document_items import _docling_doc_with_picture
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doc = _docling_doc_with_picture()
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for picture in doc.pictures:
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picture.image = None
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chunks = build_picture_chunks(doc, document_id="doc-1")
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assert chunks == []
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@pytest.mark.asyncio
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async def test_chunk_interleaves_picture_in_structural_order(monkeypatch):
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"""``chunk()`` merges text and picture chunks by their first
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``doc_item_ref``'s position in ``iterate_items()``, so picture chunks
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sit where they appear in the document, not appended at the end.
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"""
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from haiku.rag.client.processing import chunk
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from haiku.rag.config import AppConfig
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from haiku.rag.embeddings import EmbedderWrapper
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from haiku.rag.store.models.chunk import Chunk
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class StubMultimodalEmbedder(EmbedderWrapper):
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supports_images = True
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def __init__(self):
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super().__init__(embedder=None, vector_dim=4)
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class StubChunker:
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async def chunk(self, document):
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# Two text chunks straddling the picture's structural position.
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# iterate_items order on the fixture below: texts/0, texts/1,
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# pictures/0, texts/2 — positions 0,1,2,3.
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return [
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Chunk(
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content="before",
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metadata={"doc_item_refs": ["#/texts/0", "#/texts/1"]},
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),
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Chunk(
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content="after",
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metadata={"doc_item_refs": ["#/texts/2"]},
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),
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]
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monkeypatch.setattr(
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"haiku.rag.embeddings.get_embedder",
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lambda *a, **kw: StubMultimodalEmbedder(),
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)
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monkeypatch.setattr(
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"haiku.rag.chunkers.get_chunker", lambda *a, **kw: StubChunker()
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)
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from docling_core.types.doc.document import DoclingDocument, ImageRef
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from docling_core.types.doc.labels import DocItemLabel
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from PIL import Image as PILImageModule
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img = PILImageModule.new("RGB", (8, 8), "blue")
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doc = DoclingDocument(name="ordered")
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doc.add_text(label=DocItemLabel.PARAGRAPH, text="A")
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doc.add_text(label=DocItemLabel.PARAGRAPH, text="B")
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doc.add_picture(image=ImageRef.from_pil(img, dpi=72))
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doc.add_text(label=DocItemLabel.PARAGRAPH, text="C")
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chunks = await chunk(AppConfig(), doc)
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contents = [c.content for c in chunks]
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assert contents == ["before", "", "after"], (
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f"expected [before, picture, after], got {contents}"
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)
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assert chunks[1].metadata["labels"] == ["picture"]
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assert chunks[1].metadata["doc_item_refs"] == ["#/pictures/0"]
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assert chunks[1]._picture_data is not None
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# chunk.order matches list index after the merge sort.
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for i, c in enumerate(chunks):
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assert c.order == i
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@pytest.mark.asyncio
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async def test_embed_chunks_dispatches_text_vs_picture(monkeypatch):
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"""embed_chunks routes text chunks through embed_documents (batched) and
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picture chunks through embed_image_query (one at a time), reassembling
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in original order."""
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from haiku.rag.embeddings import EmbedderWrapper, embed_chunks
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from haiku.rag.store.models.chunk import Chunk
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text_calls: list[list[str]] = []
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image_calls: list[bytes] = []
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class StubEmbedder(EmbedderWrapper):
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supports_images = True
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def __init__(self):
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super().__init__(embedder=None, vector_dim=4)
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async def embed_documents(self, texts):
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text_calls.append(list(texts))
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return [[0.1, 0.2, 0.3, 0.4] for _ in texts]
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async def embed_image_query(self, image):
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image_calls.append(image)
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return [0.9, 0.8, 0.7, 0.6]
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monkeypatch.setattr(
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"haiku.rag.embeddings.get_embedder", lambda *a, **kw: StubEmbedder()
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)
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text_chunk = Chunk(content="hello", order=0)
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pic_chunk = Chunk(
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content="figure 1",
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metadata={"labels": ["picture"], "doc_item_refs": ["#/pictures/0"]},
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order=1,
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)
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pic_chunk._picture_data = b"PNGBYTES"
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embedded = await embed_chunks([text_chunk, pic_chunk, text_chunk.model_copy()])
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assert len(embedded) == 3
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assert embedded[0].embedding == [0.1, 0.2, 0.3, 0.4]
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assert embedded[1].embedding == [0.9, 0.8, 0.7, 0.6]
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assert embedded[2].embedding == [0.1, 0.2, 0.3, 0.4]
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assert text_calls == [["hello", "hello"]]
|
|
assert image_calls == [b"PNGBYTES"]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_embed_chunks_raises_on_picture_chunks_with_text_only_embedder(
|
|
monkeypatch,
|
|
):
|
|
from haiku.rag.embeddings import EmbedderWrapper, embed_chunks
|
|
from haiku.rag.store.models.chunk import Chunk
|
|
|
|
class TextOnlyEmbedder(EmbedderWrapper):
|
|
def __init__(self):
|
|
super().__init__(embedder=None, vector_dim=4)
|
|
|
|
async def embed_documents(self, texts):
|
|
return [[0.0] * 4 for _ in texts]
|
|
|
|
monkeypatch.setattr(
|
|
"haiku.rag.embeddings.get_embedder", lambda *a, **kw: TextOnlyEmbedder()
|
|
)
|
|
|
|
pic_chunk = Chunk(content="x", metadata={"labels": ["picture"]}, order=0)
|
|
pic_chunk._picture_data = b"PNG"
|
|
with pytest.raises(ValueError, match="multimodal embedder"):
|
|
await embed_chunks([pic_chunk])
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_ingest_emits_picture_chunks_with_multimodal_embedder(
|
|
temp_db_path, monkeypatch
|
|
):
|
|
"""End-to-end: ingest a docling doc with one picture under a stub
|
|
multimodal embedder; chunks_table contains a picture-labelled chunk
|
|
pointing at the picture's self_ref."""
|
|
from haiku.rag.client.documents import _store_document_with_chunks
|
|
from haiku.rag.embeddings import EmbedderWrapper, embed_chunks
|
|
from haiku.rag.store.models.document import Document
|
|
from tests.store.test_document_items import _docling_doc_with_picture
|
|
|
|
class StubMultimodalEmbedder(EmbedderWrapper):
|
|
supports_images = True
|
|
|
|
def __init__(self):
|
|
super().__init__(embedder=None, vector_dim=4)
|
|
|
|
async def embed_documents(self, texts):
|
|
return [[0.1] * 4 for _ in texts]
|
|
|
|
async def embed_image_query(self, image):
|
|
return [0.9] * 4
|
|
|
|
monkeypatch.setattr(
|
|
"haiku.rag.embeddings.get_embedder",
|
|
lambda *a, **kw: StubMultimodalEmbedder(),
|
|
)
|
|
|
|
docling_doc = _docling_doc_with_picture()
|
|
|
|
from haiku.rag.config import AppConfig, EmbeddingModelConfig, EmbeddingsConfig
|
|
|
|
config = AppConfig(
|
|
embeddings=EmbeddingsConfig(
|
|
model=EmbeddingModelConfig(provider="ollama", name="stub", vector_dim=4)
|
|
)
|
|
)
|
|
config.processing.pictures = "image"
|
|
|
|
async with HaikuRAG(temp_db_path, config=config, create=True) as rag:
|
|
chunks = await rag.chunk(docling_doc)
|
|
embedded = await embed_chunks(chunks, rag._config)
|
|
|
|
document = Document(content="x", uri="test://doc")
|
|
document.set_docling(docling_doc)
|
|
await _store_document_with_chunks(rag, document, embedded, docling_doc)
|
|
|
|
all_db_chunks = await rag.chunk_repository.store.chunks_table.query().to_list()
|
|
picture_db_chunks = [
|
|
c for c in all_db_chunks if "picture" in (c.get("metadata") or "")
|
|
]
|
|
assert len(picture_db_chunks) >= 1
|
|
assert any(
|
|
"#/pictures/0" in (c.get("metadata") or "") for c in picture_db_chunks
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_search_tool_skips_binary_content_when_qa_model_is_text_only():
|
|
"""The agent search tool must NOT attach picture bytes when the QA model
|
|
is text-only (``qa.model.vision = False``, the default). Sending image
|
|
parts to a text-only model would cause it to hallucinate confidently —
|
|
Ollama silently accepts the bytes and the model guesses."""
|
|
from haiku.rag.config import AppConfig
|
|
|
|
picture_result = SearchResult(
|
|
content="A diagram of the layout",
|
|
score=1.0,
|
|
chunk_id="chunk-1",
|
|
document_id="doc-1",
|
|
doc_item_refs=["#/pictures/0"],
|
|
labels=["picture"],
|
|
image_data={"#/pictures/0": PICTURE_B64},
|
|
)
|
|
|
|
fake_client = AsyncMock()
|
|
fake_client.search = AsyncMock(return_value=[picture_result])
|
|
fake_client.expand_context = AsyncMock(return_value=[picture_result])
|
|
|
|
config = AppConfig()
|
|
# vision defaults to False; assert anyway so the test reads explicitly.
|
|
assert config.qa.model.vision is False
|
|
toolset = create_search_toolset(config, expand_context=False)
|
|
func = toolset.tools["search"].function
|
|
|
|
ctx = RunContext(
|
|
deps=_Deps(client=fake_client), # type: ignore[arg-type]
|
|
model=TestModel(),
|
|
usage=RunUsage(),
|
|
run_id="run-1",
|
|
)
|
|
result = await func(ctx, "anything")
|
|
|
|
assert isinstance(result, str)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_search_tool_returns_plain_string_when_no_pictures():
|
|
"""When no result carries image_data the tool returns a plain str (no
|
|
ToolReturn wrapper) so non-vision flows are unaffected."""
|
|
text_result = SearchResult(
|
|
content="Some text",
|
|
score=1.0,
|
|
chunk_id="chunk-1",
|
|
document_id="doc-1",
|
|
doc_item_refs=["#/texts/0"],
|
|
labels=["paragraph"],
|
|
)
|
|
|
|
fake_client = AsyncMock()
|
|
fake_client.search = AsyncMock(return_value=[text_result])
|
|
fake_client.expand_context = AsyncMock(return_value=[text_result])
|
|
|
|
toolset = create_search_toolset(Config, expand_context=False)
|
|
func = toolset.tools["search"].function
|
|
|
|
ctx = RunContext(
|
|
deps=_Deps(client=fake_client), # type: ignore[arg-type]
|
|
model=TestModel(),
|
|
usage=RunUsage(),
|
|
run_id="run-1",
|
|
)
|
|
result = await func(ctx, "anything")
|
|
|
|
assert isinstance(result, str)
|
|
assert "rank 1" in result
|