Merge pull request 'feat(ocr): SuryaOCRClient real OCR backend (spec 6.2)' (#25) from feat/surya-client into main

Runs Surya's detection + recognition over PIL images rendered from each
Page's source file (PDFs via PyMuPDF, images via Pillow). Lazy warm_up
so FastAPI lifespan start stays predictable. Deferred Surya/torch
imports keep the base install slim — the heavy deps stay under [ocr].

Extends OCRClient Protocol with optional files + page_metadata kwargs
so the engine can resolve each page back to its on-disk source; Fake
accepts-and-ignores to keep hermetic tests unchanged.

selfcheck() runs the predictors on a 1x1 PIL image — wired into /healthz
by Task 4.3.

Tests: 6 hermetic unit tests (Surya predictors mocked, no model
download); 2 live tests gated on IX_TEST_OLLAMA=1 (never run in CI).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/ix/ocr/client.py

@@ -5,11 +5,19 @@ method satisfies the Protocol. :class:`~ix.pipeline.ocr_step.OCRStep`
 depends on the Protocol, not a concrete class, so swapping engines
 (``FakeOCRClient`` in tests, ``SuryaOCRClient`` in prod) stays a wiring
 change at the app factory.
+
+Per-page source location (``files`` + ``page_metadata``) flows in as
+optional kwargs: fakes ignore them; the real
+:class:`~ix.ocr.surya_client.SuryaOCRClient` uses them to render each
+page's pixels back from disk. Keeping these optional lets unit tests stay
+pages-only while production wiring (Task 4.3) plumbs through the real
+filesystem handles.
 """
 
 from __future__ import annotations
 
-from typing import Protocol, runtime_checkable
+from pathlib import Path
+from typing import Any, Protocol, runtime_checkable
 
 from ix.contracts import OCRResult, Page
 
@@ -24,8 +32,18 @@ class OCRClient(Protocol):
     per input page (in the same order).
     """
 
-    async def ocr(self, pages: list[Page]) -> OCRResult:
-        """Run OCR over the input pages; return the structured result."""
+    async def ocr(
+        self,
+        pages: list[Page],
+        *,
+        files: list[tuple[Path, str]] | None = None,
+        page_metadata: list[Any] | None = None,
+    ) -> OCRResult:
+        """Run OCR over the input pages; return the structured result.
+
+        ``files`` and ``page_metadata`` are optional for hermetic tests;
+        real engines that need to re-render from disk read them.
+        """
         ...
 
 

src/ix/ocr/fake.py

@@ -30,8 +30,17 @@ class FakeOCRClient:
         self._canned = canned
         self._raise_on_call = raise_on_call
 
-    async def ocr(self, pages: list[Page]) -> OCRResult:
-        """Return the canned result or raise the configured error."""
+    async def ocr(
+        self,
+        pages: list[Page],
+        **_kwargs: object,
+    ) -> OCRResult:
+        """Return the canned result or raise the configured error.
+
+        Accepts (and ignores) any keyword args the production Protocol may
+        carry — keeps the fake swappable for :class:`SuryaOCRClient` at
+        call sites that pass ``files`` / ``page_metadata``.
+        """
         if self._raise_on_call is not None:
             raise self._raise_on_call
         return self._canned

src/ix/ocr/surya_client.py

@@ -0,0 +1,235 @@
+"""SuryaOCRClient — real :class:`OCRClient` backed by ``surya-ocr``.
+
+Per spec §6.2: the MVP OCR engine. Runs Surya's detection + recognition
+predictors over per-page PIL images rendered from the downloaded sources
+(PDFs via PyMuPDF, images via Pillow).
+
+Design choices:
+
+* **Lazy model loading.** ``__init__`` is cheap; the heavy predictors are
+  built on first :meth:`ocr` / :meth:`selfcheck` / explicit :meth:`warm_up`.
+  This keeps FastAPI's lifespan predictable — ops can decide whether to
+  pay the load cost up front or on first request.
+* **Device is Surya's default.** CUDA on the prod box, MPS on M-series Macs.
+  We deliberately don't pin.
+* **No text-token reuse from PyMuPDF.** The cross-check against Paperless'
+  Tesseract output (ReliabilityStep's ``text_agreement``) is only meaningful
+  with a truly independent OCR pass, so we always render-and-recognize
+  even for PDFs that carry embedded text.
+
+The ``surya-ocr`` package pulls torch + heavy model deps, so it's kept
+behind the ``[ocr]`` extra. All Surya imports are deferred into
+:meth:`warm_up` so running the unit tests (which patch the predictors)
+doesn't require the package to be installed.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Literal
+
+from ix.contracts import Line, OCRDetails, OCRResult, Page
+from ix.segmentation import PageMetadata
+
+if TYPE_CHECKING:  # pragma: no cover
+    from PIL import Image as PILImage
+
+
+class SuryaOCRClient:
+    """Surya-backed OCR engine.
+
+    Attributes are created lazily by :meth:`warm_up`. The unit tests inject
+    mocks directly onto ``_recognition_predictor`` / ``_detection_predictor``
+    to avoid the Surya import chain.
+    """
+
+    def __init__(self) -> None:
+        self._recognition_predictor: Any = None
+        self._detection_predictor: Any = None
+
+    def warm_up(self) -> None:
+        """Load the detection + recognition predictors. Idempotent.
+
+        Called automatically on the first :meth:`ocr` / :meth:`selfcheck`,
+        or explicitly from the app lifespan to front-load the cost.
+        """
+        if (
+            self._recognition_predictor is not None
+            and self._detection_predictor is not None
+        ):
+            return
+
+        # Deferred imports: only reachable when the optional [ocr] extra is
+        # installed. Keeping them inside the method so base-install unit
+        # tests (which patch the predictors) don't need surya on sys.path.
+        from surya.detection import DetectionPredictor  # type: ignore[import-not-found]
+        from surya.foundation import FoundationPredictor  # type: ignore[import-not-found]
+        from surya.recognition import RecognitionPredictor  # type: ignore[import-not-found]
+
+        foundation = FoundationPredictor()
+        self._recognition_predictor = RecognitionPredictor(foundation)
+        self._detection_predictor = DetectionPredictor()
+
+    async def ocr(
+        self,
+        pages: list[Page],
+        *,
+        files: list[tuple[Path, str]] | None = None,
+        page_metadata: list[Any] | None = None,
+    ) -> OCRResult:
+        """Render each input page, run Surya, translate back to contracts."""
+        self.warm_up()
+
+        images = self._render_pages(pages, files, page_metadata)
+
+        # Surya is blocking — run it off the event loop.
+        loop = asyncio.get_running_loop()
+        surya_results = await loop.run_in_executor(
+            None, self._run_recognition, images
+        )
+
+        out_pages: list[Page] = []
+        all_text_fragments: list[str] = []
+        for input_page, surya_result in zip(pages, surya_results, strict=True):
+            lines: list[Line] = []
+            for tl in getattr(surya_result, "text_lines", []) or []:
+                flat = self._flatten_polygon(getattr(tl, "polygon", None))
+                text = getattr(tl, "text", None)
+                lines.append(Line(text=text, bounding_box=flat))
+                if text:
+                    all_text_fragments.append(text)
+            out_pages.append(
+                Page(
+                    page_no=input_page.page_no,
+                    width=input_page.width,
+                    height=input_page.height,
+                    angle=input_page.angle,
+                    unit=input_page.unit,
+                    lines=lines,
+                )
+            )
+
+        details = OCRDetails(
+            text="\n".join(all_text_fragments) if all_text_fragments else None,
+            pages=out_pages,
+        )
+        return OCRResult(result=details, meta_data={"engine": "surya"})
+
+    async def selfcheck(self) -> Literal["ok", "fail"]:
+        """Run the predictors on a 1x1 image to confirm the stack works."""
+        try:
+            self.warm_up()
+        except Exception:
+            return "fail"
+
+        try:
+            from PIL import Image as PILImageRuntime
+
+            img = PILImageRuntime.new("RGB", (1, 1), color="white")
+            loop = asyncio.get_running_loop()
+            await loop.run_in_executor(None, self._run_recognition, [img])
+        except Exception:
+            return "fail"
+        return "ok"
+
+    def _run_recognition(self, images: list[PILImage.Image]) -> list[Any]:
+        """Invoke the recognition predictor. Kept tiny for threadpool offload."""
+        return list(
+            self._recognition_predictor(
+                images, det_predictor=self._detection_predictor
+            )
+        )
+
+    def _render_pages(
+        self,
+        pages: list[Page],
+        files: list[tuple[Path, str]] | None,
+        page_metadata: list[Any] | None,
+    ) -> list[PILImage.Image]:
+        """Render each input :class:`Page` to a PIL image.
+
+        We walk pages + page_metadata in lockstep so we know which source
+        file each page came from and (for PDFs) what page-index to render.
+        Text-only pages (``file_index is None``) get a blank 1x1 placeholder
+        so Surya returns an empty result and downstream code still gets one
+        entry per input page.
+        """
+        from PIL import Image as PILImageRuntime
+
+        metas: list[PageMetadata] = list(page_metadata or [])
+        file_records: list[tuple[Path, str]] = list(files or [])
+
+        # Per-file lazy PDF openers so we don't re-open across pages.
+        pdf_docs: dict[int, Any] = {}
+
+        # Per-file running page-within-file counter. For PDFs we emit one
+        # entry per PDF page in order; ``pages`` was built the same way by
+        # DocumentIngestor, so a parallel counter reconstructs the mapping.
+        per_file_cursor: dict[int, int] = {}
+
+        rendered: list[PILImage.Image] = []
+        try:
+            for idx, _page in enumerate(pages):
+                meta = metas[idx] if idx < len(metas) else PageMetadata()
+                file_index = meta.file_index
+                if file_index is None or file_index >= len(file_records):
+                    # Text-only page — placeholder image; Surya returns empty.
+                    rendered.append(
+                        PILImageRuntime.new("RGB", (1, 1), color="white")
+                    )
+                    continue
+
+                local_path, mime = file_records[file_index]
+                if mime == "application/pdf":
+                    doc = pdf_docs.get(file_index)
+                    if doc is None:
+                        import fitz  # PyMuPDF
+
+                        doc = fitz.open(str(local_path))
+                        pdf_docs[file_index] = doc
+                    pdf_page_no = per_file_cursor.get(file_index, 0)
+                    per_file_cursor[file_index] = pdf_page_no + 1
+                    pdf_page = doc.load_page(pdf_page_no)
+                    pix = pdf_page.get_pixmap(dpi=200)
+                    img = PILImageRuntime.frombytes(
+                        "RGB", (pix.width, pix.height), pix.samples
+                    )
+                    rendered.append(img)
+                elif mime in ("image/png", "image/jpeg", "image/tiff"):
+                    frame_no = per_file_cursor.get(file_index, 0)
+                    per_file_cursor[file_index] = frame_no + 1
+                    img = PILImageRuntime.open(local_path)
+                    # Handle multi-frame (TIFF) — seek to the right frame.
+                    with contextlib.suppress(EOFError):
+                        img.seek(frame_no)
+                    rendered.append(img.convert("RGB"))
+                else:  # pragma: no cover - ingestor already rejected
+                    rendered.append(
+                        PILImageRuntime.new("RGB", (1, 1), color="white")
+                    )
+        finally:
+            for doc in pdf_docs.values():
+                with contextlib.suppress(Exception):
+                    doc.close()
+        return rendered
+
+    @staticmethod
+    def _flatten_polygon(polygon: Any) -> list[float]:
+        """Flatten ``[[x1,y1],[x2,y2],[x3,y3],[x4,y4]]`` → 8-float list.
+
+        Surya emits 4 quad corners. The spec wants 8 raw-pixel coords so
+        downstream provenance normalisation can consume them directly.
+        """
+        if not polygon:
+            return []
+        flat: list[float] = []
+        for point in polygon:
+            if isinstance(point, (list, tuple)) and len(point) >= 2:
+                flat.append(float(point[0]))
+                flat.append(float(point[1]))
+        return flat
+
+
+__all__ = ["SuryaOCRClient"]

src/ix/pipeline/ocr_step.py

@@ -56,7 +56,11 @@ class OCRStep(Step):
         assert ctx is not None, "SetupStep must populate response_ix.context"
 
         pages = list(getattr(ctx, "pages", []))
-        ocr_result = await self._client.ocr(pages)
+        files = list(getattr(ctx, "files", []) or [])
+        page_metadata = list(getattr(ctx, "page_metadata", []) or [])
+        ocr_result = await self._client.ocr(
+            pages, files=files, page_metadata=page_metadata
+        )
 
         # Inject page tags around each OCR page's content so the LLM can
         # cross-reference the visual anchor without a separate prompt hack.

tests/live/test_surya_client_live.py

@@ -0,0 +1,83 @@
+"""Live test for :class:`SuryaOCRClient` — gated on ``IX_TEST_OLLAMA=1``.
+
+Downloads real Surya models (hundreds of MB) on first run. Never runs in
+CI. Exercised locally with::
+
+    IX_TEST_OLLAMA=1 uv run pytest tests/live/test_surya_client_live.py -v
+
+Note: requires the ``[ocr]`` extra — ``uv sync --extra ocr --extra dev``.
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import pytest
+
+from ix.contracts import Page
+from ix.segmentation import PageMetadata
+
+pytestmark = [
+    pytest.mark.live,
+    pytest.mark.skipif(
+        os.environ.get("IX_TEST_OLLAMA") != "1",
+        reason="live: IX_TEST_OLLAMA=1 required",
+    ),
+]
+
+
+async def test_extracts_dkb_and_iban_from_synthetic_giro() -> None:
+    """Real Surya run against ``tests/fixtures/synthetic_giro.pdf``.
+
+    Assert the flat text contains ``"DKB"`` and the IBAN without spaces.
+    """
+    from ix.ocr.surya_client import SuryaOCRClient
+
+    fixture = Path(__file__).parent.parent / "fixtures" / "synthetic_giro.pdf"
+    assert fixture.exists(), f"missing fixture: {fixture}"
+
+    # Build Pages the way DocumentIngestor would for this PDF: count pages
+    # via PyMuPDF so we pass the right number of inputs.
+    import fitz
+
+    doc = fitz.open(str(fixture))
+    try:
+        pages = [
+            Page(
+                page_no=i + 1,
+                width=float(p.rect.width),
+                height=float(p.rect.height),
+                lines=[],
+            )
+            for i, p in enumerate(doc)
+        ]
+    finally:
+        doc.close()
+
+    client = SuryaOCRClient()
+    result = await client.ocr(
+        pages,
+        files=[(fixture, "application/pdf")],
+        page_metadata=[PageMetadata(file_index=0) for _ in pages],
+    )
+
+    flat_text = result.result.text or ""
+    # Join page-level line texts if flat text missing (shape-safety).
+    if not flat_text:
+        flat_text = "\n".join(
+            line.text or ""
+            for page in result.result.pages
+            for line in page.lines
+        )
+
+    assert "DKB" in flat_text
+    assert "DE89370400440532013000" in flat_text.replace(" ", "")
+
+
+async def test_selfcheck_ok_against_real_predictors() -> None:
+    """``selfcheck()`` returns ``ok`` once Surya's predictors load."""
+    from ix.ocr.surya_client import SuryaOCRClient
+
+    client = SuryaOCRClient()
+    assert await client.selfcheck() == "ok"

tests/unit/test_surya_client.py

@@ -0,0 +1,166 @@
+"""Tests for :class:`SuryaOCRClient` — hermetic, no model download.
+
+The real Surya predictors are patched out with :class:`unittest.mock.MagicMock`
+that return trivially-shaped line objects. The tests assert the client's
+translation layer — flattening polygons, mapping text_lines → ``Line``,
+preserving ``page_no``/``width``/``height`` per input page.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from ix.contracts import Page
+from ix.ocr.surya_client import SuryaOCRClient
+from ix.segmentation import PageMetadata
+
+
+def _make_surya_line(text: str, polygon: list[list[float]]) -> SimpleNamespace:
+    """Mimic ``surya.recognition.schema.TextLine`` duck-typing-style."""
+    return SimpleNamespace(text=text, polygon=polygon, confidence=0.95)
+
+
+def _make_surya_ocr_result(lines: list[SimpleNamespace]) -> SimpleNamespace:
+    """Mimic ``surya.recognition.schema.OCRResult``."""
+    return SimpleNamespace(text_lines=lines, image_bbox=[0, 0, 100, 100])
+
+
+class TestOCRBuildsOCRResultFromMockedPredictors:
+    async def test_one_image_one_line_flatten_polygon(self, tmp_path: Path) -> None:
+        img_path = tmp_path / "a.png"
+        _write_tiny_png(img_path)
+
+        mock_line = _make_surya_line(
+            text="hello",
+            polygon=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]],
+        )
+        mock_predictor = MagicMock(
+            return_value=[_make_surya_ocr_result([mock_line])]
+        )
+
+        client = SuryaOCRClient()
+        # Skip the real warm_up; inject the mock directly.
+        client._recognition_predictor = mock_predictor
+        client._detection_predictor = MagicMock()
+
+        pages = [Page(page_no=1, width=100.0, height=50.0, lines=[])]
+        result = await client.ocr(
+            pages,
+            files=[(img_path, "image/png")],
+            page_metadata=[PageMetadata(file_index=0)],
+        )
+
+        assert len(result.result.pages) == 1
+        out_page = result.result.pages[0]
+        assert out_page.page_no == 1
+        assert out_page.width == 100.0
+        assert out_page.height == 50.0
+        assert len(out_page.lines) == 1
+        assert out_page.lines[0].text == "hello"
+        assert out_page.lines[0].bounding_box == [
+            1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0
+        ]
+
+    async def test_multiple_pages_preserves_order(self, tmp_path: Path) -> None:
+        img_a = tmp_path / "a.png"
+        img_b = tmp_path / "b.png"
+        _write_tiny_png(img_a)
+        _write_tiny_png(img_b)
+
+        mock_predictor = MagicMock(
+            return_value=[
+                _make_surya_ocr_result(
+                    [_make_surya_line("a-line", [[0, 0], [1, 0], [1, 1], [0, 1]])]
+                ),
+                _make_surya_ocr_result(
+                    [_make_surya_line("b-line", [[0, 0], [1, 0], [1, 1], [0, 1]])]
+                ),
+            ]
+        )
+
+        client = SuryaOCRClient()
+        client._recognition_predictor = mock_predictor
+        client._detection_predictor = MagicMock()
+
+        pages = [
+            Page(page_no=1, width=10.0, height=20.0, lines=[]),
+            Page(page_no=2, width=10.0, height=20.0, lines=[]),
+        ]
+        result = await client.ocr(
+            pages,
+            files=[(img_a, "image/png"), (img_b, "image/png")],
+            page_metadata=[
+                PageMetadata(file_index=0),
+                PageMetadata(file_index=1),
+            ],
+        )
+
+        assert [p.lines[0].text for p in result.result.pages] == ["a-line", "b-line"]
+
+    async def test_lazy_warm_up_on_first_ocr(self, tmp_path: Path) -> None:
+        img = tmp_path / "x.png"
+        _write_tiny_png(img)
+
+        client = SuryaOCRClient()
+
+        # Use patch.object on the instance's warm_up so we don't need real
+        # Surya module loading.
+        with patch.object(client, "warm_up", autospec=True) as mocked_warm_up:
+            # After warm_up is called, the predictors must be assigned.
+            def fake_warm_up(self: SuryaOCRClient) -> None:
+                self._recognition_predictor = MagicMock(
+                    return_value=[
+                        _make_surya_ocr_result(
+                            [
+                                _make_surya_line(
+                                    "hi", [[0, 0], [1, 0], [1, 1], [0, 1]]
+                                )
+                            ]
+                        )
+                    ]
+                )
+                self._detection_predictor = MagicMock()
+
+            mocked_warm_up.side_effect = lambda: fake_warm_up(client)
+
+            pages = [Page(page_no=1, width=10.0, height=10.0, lines=[])]
+            await client.ocr(
+                pages,
+                files=[(img, "image/png")],
+                page_metadata=[PageMetadata(file_index=0)],
+            )
+            mocked_warm_up.assert_called_once()
+
+
+class TestSelfcheck:
+    async def test_selfcheck_ok_with_mocked_predictors(self) -> None:
+        client = SuryaOCRClient()
+        client._recognition_predictor = MagicMock(
+            return_value=[_make_surya_ocr_result([])]
+        )
+        client._detection_predictor = MagicMock()
+        assert await client.selfcheck() == "ok"
+
+    async def test_selfcheck_fail_when_predictor_raises(self) -> None:
+        client = SuryaOCRClient()
+        client._recognition_predictor = MagicMock(
+            side_effect=RuntimeError("cuda broken")
+        )
+        client._detection_predictor = MagicMock()
+        assert await client.selfcheck() == "fail"
+
+
+def _write_tiny_png(path: Path) -> None:
+    """Write a 2x2 white PNG so PIL can open it."""
+    from PIL import Image
+
+    Image.new("RGB", (2, 2), color="white").save(path, format="PNG")
+
+
+@pytest.mark.parametrize("unused", [None])  # keep pytest happy if file ever runs alone
+def test_module_imports(unused: None) -> None:
+    assert SuryaOCRClient is not None