"""Picture-bearing search results: image_data attachment, expansion, multimodal ToolReturn.""" import base64 from dataclasses import dataclass from io import BytesIO from unittest.mock import AsyncMock import pytest from PIL import Image as PILImageModule from pydantic_ai import RunContext from pydantic_ai.messages import BinaryContent, ToolReturn from pydantic_ai.models.test import TestModel from pydantic_ai.usage import RunUsage from haiku.rag.client import HaikuRAG from haiku.rag.client.search import _populate_image_data from haiku.rag.config import AppConfig, Config from haiku.rag.context import expand_with_items from haiku.rag.store.models.chunk import SearchResult from haiku.rag.store.models.document_item import DocumentItem from haiku.rag.tools.search import create_search_toolset def _make_png(color: str = "red", size: tuple[int, int] = (4, 4)) -> bytes: buf = BytesIO() PILImageModule.new("RGB", size, color).save(buf, "PNG") return buf.getvalue() PICTURE_BYTES = _make_png("red") PICTURE_B64 = base64.b64encode(PICTURE_BYTES).decode("ascii") @pytest.mark.asyncio async def test_populate_image_data_attaches_base64(temp_db_path): async with HaikuRAG(temp_db_path, create=True) as rag: await rag.document_item_repository.create_items( "doc-1", [ DocumentItem( document_id="doc-1", position=0, self_ref="#/texts/0", label="paragraph", text="Some text", ), DocumentItem( document_id="doc-1", position=1, self_ref="#/pictures/0", label="picture", text="", picture_data=PICTURE_BYTES, ), ], ) text_only = SearchResult( content="Some text", score=1.0, document_id="doc-1", doc_item_refs=["#/texts/0"], labels=["paragraph"], ) with_picture = SearchResult( content="A figure caption", score=0.9, document_id="doc-1", doc_item_refs=["#/texts/0", "#/pictures/0"], labels=["paragraph", "picture"], ) await _populate_image_data(rag, [text_only, with_picture]) # Text-only result is unchanged assert text_only.image_data is None # Picture-bearing result has the bytes attached assert with_picture.image_data == {"#/pictures/0": PICTURE_B64} @pytest.mark.asyncio async def test_client_search_include_images_false_skips_lookup(temp_db_path): """include_images=False must short-circuit the picture-bytes lookup.""" async with HaikuRAG(temp_db_path, create=True) as rag: await rag.document_item_repository.create_items( "doc-1", [ DocumentItem( document_id="doc-1", position=0, self_ref="#/pictures/0", label="picture", picture_data=PICTURE_BYTES, ), ], ) # Spy that we never reach the picture-bytes accessor rag.document_item_repository.get_pictures_for_chunk = AsyncMock( # type: ignore[method-assign] wraps=rag.document_item_repository.get_pictures_for_chunk ) from haiku.rag.client.search import search # Stub the chunk-search results so we don't depend on embeddings/FTS async def fake_chunk_search(*args, **kwargs): return [] rag.chunk_repository.search = fake_chunk_search # type: ignore[method-assign] await search(rag, "anything", include_images=False) rag.document_item_repository.get_pictures_for_chunk.assert_not_called() @pytest.mark.asyncio async def test_expand_context_preserves_picture_refs_with_empty_text(temp_db_path): """A picture item with empty text must keep its self_ref through expansion.""" async with HaikuRAG(temp_db_path, create=True) as rag: # Build an items table with a section header + a paragraph match + an # adjacent picture row that has no text. The expansion must keep # picture self_refs even when item.text is empty so picture bytes # are still attached downstream. await rag.document_item_repository.create_items( "doc-1", [ DocumentItem( document_id="doc-1", position=0, self_ref="#/texts/0", label="section_header", text="Methods", ), DocumentItem( document_id="doc-1", position=1, self_ref="#/texts/1", label="paragraph", text="The figure below shows the architecture.", ), DocumentItem( document_id="doc-1", position=2, self_ref="#/pictures/0", label="picture", text="", picture_data=PICTURE_BYTES, ), DocumentItem( document_id="doc-1", position=3, self_ref="#/texts/2", label="paragraph", text="More commentary follows.", ), ], ) # Match on the paragraph that mentions the figure. seed = SearchResult( content="The figure below shows the architecture.", score=1.0, document_id="doc-1", doc_item_refs=["#/texts/1"], labels=["paragraph"], ) expanded = await expand_with_items( rag.document_item_repository, "doc-1", [seed], max_chars=10_000, ) assert len(expanded) == 1 out = expanded[0] assert "#/pictures/0" in out.doc_item_refs, ( "Picture self_ref should survive expansion even with empty text" ) assert "picture" in out.labels @pytest.mark.vcr() @pytest.mark.asyncio async def test_rechunk_preserves_picture_data(temp_db_path): """``rebuild --rechunk`` keeps ``picture_data`` for every picture row.""" from haiku.rag.client import RebuildMode from haiku.rag.client.documents import _store_document_with_chunks from haiku.rag.store.models.document import Document from tests.store.test_document_items import _docling_doc_with_picture docling_doc = _docling_doc_with_picture() async with HaikuRAG(temp_db_path, create=True) as rag: document = Document(content="x", uri="test://doc") document.set_docling(docling_doc) created = await _store_document_with_chunks(rag, document, [], docling_doc) assert created.id is not None before = await rag.document_item_repository.get_all_picture_data(created.id) assert before.get("#/pictures/0") is not None async for _ in rag.rebuild_database(mode=RebuildMode.RECHUNK): pass after = await rag.document_item_repository.get_all_picture_data(created.id) assert after.get("#/pictures/0") == before.get("#/pictures/0") @pytest.mark.asyncio async def test_expand_context_repopulates_image_data(temp_db_path): """expand_context rebuilds SearchResult objects via expand_with_items, so it must re-attach picture bytes — otherwise vision flows downstream see empty image_data after expansion.""" async with HaikuRAG(temp_db_path, create=True) as rag: await rag.document_item_repository.create_items( "doc-1", [ DocumentItem( document_id="doc-1", position=0, self_ref="#/texts/0", label="section_header", text="Methods", ), DocumentItem( document_id="doc-1", position=1, self_ref="#/texts/1", label="paragraph", text="The figure below shows the architecture.", ), DocumentItem( document_id="doc-1", position=2, self_ref="#/pictures/0", label="picture", text="", picture_data=PICTURE_BYTES, ), ], ) seed = SearchResult( content="The figure below shows the architecture.", score=1.0, document_id="doc-1", doc_item_refs=["#/texts/1"], labels=["paragraph"], image_data=None, ) expanded = await rag.expand_context([seed]) assert len(expanded) == 1 out = expanded[0] assert "#/pictures/0" in out.doc_item_refs assert out.image_data == {"#/pictures/0": PICTURE_B64} @dataclass class _Deps: client: object @pytest.mark.asyncio async def test_search_tool_returns_multimodal_when_picture_present(): """The agent-facing search tool must wrap text + BinaryContent in ToolReturn whenever a result carries picture image_data AND the QA model is vision-capable.""" 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() config.qa.model.vision = True 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, ToolReturn) assert isinstance(result.return_value, str) assert "Type: picture" in result.return_value or "rank 1" in result.return_value assert result.content is not None assert len(result.content) == 1 part = result.content[0] assert isinstance(part, BinaryContent) assert part.media_type == "image/png" assert part.identifier == "#/pictures/0" assert part.data == PICTURE_BYTES @pytest.mark.asyncio async def test_search_tool_attaches_same_self_ref_from_different_documents(): """Two different documents both have ``#/pictures/0`` — the dedup must key on ``(document_id, self_ref)`` so each document's figure reaches the model. Keying on ``self_ref`` alone silently drops the second document's bytes, leaving the model with text only for that result.""" other_bytes = _make_png("blue") other_b64 = base64.b64encode(other_bytes).decode("ascii") doc_a = SearchResult( content="Figure from doc A", score=1.0, chunk_id="chunk-a", document_id="doc-A", doc_item_refs=["#/pictures/0"], labels=["picture"], image_data={"#/pictures/0": PICTURE_B64}, ) doc_b = SearchResult( content="Figure from doc B (same self_ref, different bytes)", score=0.9, chunk_id="chunk-b", document_id="doc-B", doc_item_refs=["#/pictures/0"], labels=["picture"], image_data={"#/pictures/0": other_b64}, ) fake_client = AsyncMock() fake_client.search = AsyncMock(return_value=[doc_a, doc_b]) fake_client.expand_context = AsyncMock(return_value=[doc_a, doc_b]) config = AppConfig() config.qa.model.vision = True 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, ToolReturn) assert result.content is not None assert len(result.content) == 2, ( "Both documents' figures must reach the model — dedup keyed on " "self_ref alone would drop doc-B's bytes." ) payloads = {part.data for part in result.content} # type: ignore[attr-defined] assert PICTURE_BYTES in payloads assert other_bytes in payloads # Synthetic picture chunks at ingest def test_build_picture_chunks_uses_live_uri(): from haiku.rag.client.processing import build_picture_chunks from tests.store.test_document_items import _docling_doc_with_picture doc = _docling_doc_with_picture() chunks = build_picture_chunks(doc, document_id="doc-1") assert len(chunks) == 1 chunk = chunks[0] assert chunk.metadata["doc_item_refs"] == ["#/pictures/0"] assert chunk.metadata["labels"] == ["picture"] assert chunk._picture_data is not None assert chunk._picture_data.startswith(b"\x89PNG") assert chunk.document_id == "doc-1" def test_build_picture_chunks_falls_back_to_existing_picture_data(): """When the live docling has its picture URIs stripped, the snapshot fills the gap so rebuild round-trips don't lose picture chunks.""" from haiku.rag.client.processing import build_picture_chunks from tests.store.test_document_items import _docling_doc_with_picture doc = _docling_doc_with_picture() for picture in doc.pictures: picture.image = None chunks = build_picture_chunks( doc, document_id="doc-1", existing_picture_data={"#/pictures/0": b"snapshot-bytes"}, ) assert len(chunks) == 1 assert chunks[0]._picture_data == b"snapshot-bytes" def test_build_picture_chunks_skips_pictures_without_bytes(): from haiku.rag.client.processing import build_picture_chunks from tests.store.test_document_items import _docling_doc_with_picture doc = _docling_doc_with_picture() for picture in doc.pictures: picture.image = None chunks = build_picture_chunks(doc, document_id="doc-1") assert chunks == [] @pytest.mark.asyncio async def test_chunk_interleaves_picture_in_structural_order(monkeypatch): """``chunk()`` merges text and picture chunks by their first ``doc_item_ref``'s position in ``iterate_items()``, so picture chunks sit where they appear in the document, not appended at the end. """ from haiku.rag.client.processing import chunk from haiku.rag.embeddings import EmbedderWrapper from haiku.rag.store.models.chunk import Chunk class StubMultimodalEmbedder(EmbedderWrapper): supports_images = True def __init__(self): super().__init__(embedder=None, vector_dim=4) class StubChunker: async def chunk(self, document): # Two text chunks straddling the picture's structural position. # iterate_items order on the fixture below: texts/0, texts/1, # pictures/0, texts/2 — positions 0,1,2,3. return [ Chunk( content="before", metadata={"doc_item_refs": ["#/texts/0", "#/texts/1"]}, ), Chunk( content="after", metadata={"doc_item_refs": ["#/texts/2"]}, ), ] monkeypatch.setattr( "haiku.rag.embeddings.get_embedder", lambda *a, **kw: StubMultimodalEmbedder(), ) monkeypatch.setattr( "haiku.rag.chunkers.get_chunker", lambda *a, **kw: StubChunker() ) from docling_core.types.doc.document import DoclingDocument, ImageRef from docling_core.types.doc.labels import DocItemLabel from PIL import Image as PILImageModule img = PILImageModule.new("RGB", (8, 8), "blue") doc = DoclingDocument(name="ordered") doc.add_text(label=DocItemLabel.PARAGRAPH, text="A") doc.add_text(label=DocItemLabel.PARAGRAPH, text="B") doc.add_picture(image=ImageRef.from_pil(img, dpi=72)) doc.add_text(label=DocItemLabel.PARAGRAPH, text="C") chunks = await chunk(AppConfig(), doc) contents = [c.content for c in chunks] assert contents == ["before", "", "after"], ( f"expected [before, picture, after], got {contents}" ) assert chunks[1].metadata["labels"] == ["picture"] assert chunks[1].metadata["doc_item_refs"] == ["#/pictures/0"] assert chunks[1]._picture_data is not None # chunk.order matches list index after the merge sort. for i, c in enumerate(chunks): assert c.order == i @pytest.mark.asyncio async def test_embed_chunks_dispatches_text_vs_picture(monkeypatch): """embed_chunks routes text chunks through embed_documents (batched) and picture chunks through embed_image (one at a time), reassembling in original order.""" from haiku.rag.embeddings import EmbedderWrapper, embed_chunks from haiku.rag.store.models.chunk import Chunk text_calls: list[list[str]] = [] image_calls: list[bytes] = [] class StubEmbedder(EmbedderWrapper): supports_images = True def __init__(self): super().__init__(embedder=None, vector_dim=4) async def embed_documents(self, texts): text_calls.append(list(texts)) return [[0.1, 0.2, 0.3, 0.4] for _ in texts] async def embed_image(self, image): image_calls.append(image) return [0.9, 0.8, 0.7, 0.6] monkeypatch.setattr( "haiku.rag.embeddings.get_embedder", lambda *a, **kw: StubEmbedder() ) text_chunk = Chunk(content="hello", order=0) pic_chunk = Chunk( content="figure 1", metadata={"labels": ["picture"], "doc_item_refs": ["#/pictures/0"]}, order=1, ) pic_chunk._picture_data = b"PNGBYTES" embedded = await embed_chunks([text_chunk, pic_chunk, text_chunk.model_copy()]) assert len(embedded) == 3 assert embedded[0].embedding == [0.1, 0.2, 0.3, 0.4] assert embedded[1].embedding == [0.9, 0.8, 0.7, 0.6] assert embedded[2].embedding == [0.1, 0.2, 0.3, 0.4] 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(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 EmbeddingModelConfig, EmbeddingsConfig config = AppConfig( embeddings=EmbeddingsConfig( model=EmbeddingModelConfig(provider="ollama", name="stub", vector_dim=4) ) ) 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.""" 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_drops_invalid_image_bytes(): """A picture whose bytes cannot be decoded by PIL must not produce a BinaryContent part. Otherwise the model adapter emits a vision placeholder for an image the server can't decode, leaving Qwen3-VL's processor with an off-by-one count and a 400 from ``Qwen3VLProcessor``.""" from io import BytesIO from PIL import Image as PILImageModule buf = BytesIO() PILImageModule.new("RGB", (4, 4), "red").save(buf, "PNG") valid_png = buf.getvalue() valid_b64 = base64.b64encode(valid_png).decode("ascii") # Truthy bytes (passes ``if blob`` guards) but not a decodable PNG. invalid_bytes = b"\x89PNG\r\n\x1a\ngarbage" invalid_b64 = base64.b64encode(invalid_bytes).decode("ascii") picture_result = SearchResult( content="Two figures", score=1.0, chunk_id="chunk-1", document_id="doc-1", doc_item_refs=["#/pictures/0", "#/pictures/1"], labels=["picture"], image_data={"#/pictures/0": valid_b64, "#/pictures/1": invalid_b64}, ) fake_client = AsyncMock() fake_client.search = AsyncMock(return_value=[picture_result]) fake_client.expand_context = AsyncMock(return_value=[picture_result]) config = AppConfig() config.qa.model.vision = True 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, ToolReturn) assert result.content is not None identifiers = {p.identifier for p in result.content} # type: ignore[attr-defined] assert identifiers == {"#/pictures/0"}, ( "Only the decodable PNG should reach the model — the corrupt " "ref must be dropped so we don't emit a placeholder for an " "image the server can't decode." ) @pytest.mark.asyncio async def test_search_tool_drops_all_invalid_returns_plain_text(): """If every picture in the result set fails decode, fall back to a plain string return — there's nothing to attach, so wrapping in ``ToolReturn`` with an empty ``content`` list would surface an empty user message downstream.""" bad_b64 = base64.b64encode(b"\x89PNGnope").decode("ascii") picture_result = SearchResult( content="One broken figure", score=1.0, chunk_id="chunk-1", document_id="doc-1", doc_item_refs=["#/pictures/0"], labels=["picture"], image_data={"#/pictures/0": bad_b64}, ) fake_client = AsyncMock() fake_client.search = AsyncMock(return_value=[picture_result]) fake_client.expand_context = AsyncMock(return_value=[picture_result]) config = AppConfig() config.qa.model.vision = True 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 @pytest.mark.asyncio async def test_skill_search_tool_uses_skill_model_vision_flag(tmp_path): """The skill-level ``search`` tool gates picture attachment on the model passed to ``create_skill_tools`` (per-skill driving model), not on ``config.qa.model.vision``. Verifies the per-skill plumbing by setting qa.model.vision=False and analysis.model.vision=True simultaneously.""" from haiku.rag.client import HaikuRAG from haiku.rag.skills._deps import RAGRunDeps from haiku.rag.skills._tools import create_skill_tools from haiku.rag.skills.rag import RAGState picture_result = SearchResult( content="A figure", score=1.0, chunk_id="chunk-1", document_id="doc-1", doc_item_refs=["#/pictures/0"], labels=["picture"], image_data={"#/pictures/0": PICTURE_B64}, ) from haiku.rag.config.models import ModelConfig config = AppConfig() config.qa.model.vision = False # Explicitly set analysis.model (it defaults to None = inherit from qa). config.analysis.model = ModelConfig( provider="openai", name="vision-model", vision=True ) async with HaikuRAG(tmp_path / "db.lancedb", create=True) as rag: rag.search = AsyncMock(return_value=[picture_result]) # type: ignore[method-assign] rag.expand_context = AsyncMock(return_value=[picture_result]) # type: ignore[method-assign] # rag skill: should NOT attach binaries (qa.model.vision is False) rag_tools = create_skill_tools( tmp_path / "db.lancedb", config, RAGState, ["search"], model=config.qa.model, ) deps = RAGRunDeps(state=RAGState(), rag=rag, emit=lambda _e: None) ctx = RunContext( deps=deps, model=TestModel(), usage=RunUsage(), run_id="run-1", ) result_rag = await rag_tools["search"](ctx, "anything") assert isinstance(result_rag, str), ( "rag skill with qa.model.vision=False must return plain text" ) # analysis skill: SHOULD attach binaries (analysis.model.vision is True) analysis_tools = create_skill_tools( tmp_path / "db.lancedb", config, RAGState, # state shape doesn't matter for this assertion ["search"], model=config.analysis.model or config.qa.model, ) deps2 = RAGRunDeps(state=RAGState(), rag=rag, emit=lambda _e: None) ctx2 = RunContext( deps=deps2, model=TestModel(), usage=RunUsage(), run_id="run-2", ) result_analysis = await analysis_tools["search"](ctx2, "anything") assert isinstance(result_analysis, ToolReturn), ( "analysis skill with analysis.model.vision=True must wrap binaries" ) assert result_analysis.content is not None assert any(isinstance(p, BinaryContent) for p in result_analysis.content)