Chris CoutinhoandClaude Opus 4.8
f1272dfe84
Address PR #858 review round 2: - OcrProcessor backend resolution is now guarded by an anyio.Lock (lazy-init, double-checked) so a burst of concurrent first-OCR calls resolves the backend once instead of each fetching its own gateway M2M token. - The document_tier1_engine=pymupdf rollback now logs a warning when it falls back to the fast processor (no 'structured' registered) instead of silently using the very engine the operator opted out of. - classify_from_text defaults ocr_frac to 0.0 (not 1.0) for a zero-page PDF, so the recorded classification metric is "fast" (no OCR evidence) rather than a misleading "ocr"; the no_text_layer/bad_text_layer flags are gated on having sampled at least one page. New tests: zero-page classify routes fast, rollback-fallback warning. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
188 lines
6.3 KiB
Python
188 lines
6.3 KiB
Python
"""Unit tests for the tier-0 document classifier.
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Pins the routing decisions and the text-quality heuristic that drive which
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extraction tier a PDF starts in:
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* a clean born-digital PDF (text, no full-page images) -> ``fast`` (tier 1);
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* a full-page-image scan -> ``ocr`` (tier 3), since handwriting/stamps aren't
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in any text layer;
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* the text-quality score distinguishes clean prose from mashed/space-less junk.
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"""
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import pymupdf
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import pytest
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from nextcloud_mcp_server.document_processors import classifier as clf
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pytestmark = pytest.mark.unit
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def _digital_pdf(
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pages: int = 3, body: str = "Hello world this is clean text. "
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) -> bytes:
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doc = pymupdf.open()
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for _ in range(pages):
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page = doc.new_page(width=595, height=842)
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page.insert_text((50, 60), body * 8)
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data: bytes = doc.tobytes()
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doc.close()
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return data
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def _full_page_image_pdf(pages: int = 2) -> bytes:
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# A page whose entire area is a raster image -> looks scanned.
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doc = pymupdf.open()
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pix = pymupdf.Pixmap(pymupdf.csRGB, pymupdf.IRect(0, 0, 600, 850))
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pix.clear_with(255)
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img = pix.tobytes("png")
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del pix # Pixmap holds native memory; release it before the loop
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for _ in range(pages):
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page = doc.new_page(width=595, height=842)
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page.insert_image(page.rect, stream=img)
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data: bytes = doc.tobytes()
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doc.close()
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return data
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# --- text-quality heuristic --------------------------------------------------
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def test_text_quality_clean_prose_scores_high():
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assert clf._text_quality("the quick brown fox jumps over the lazy dog") > 0.8
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def test_text_quality_mashed_tokens_scores_low():
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# space-less / mashed layer (the "Student 147" failure mode)
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mashed = "01322234567mobileoutstandingresilienceacademicachievementhurdles"
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assert clf._text_quality(mashed) < clf.MIN_TEXT_QUALITY
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def test_text_quality_empty_is_zero():
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assert clf._text_quality("") == pytest.approx(0.0)
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# --- routing -----------------------------------------------------------------
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def test_digital_pdf_routes_fast():
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c = clf.classify_pdf(_digital_pdf())
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assert c.recommended_tier == "fast"
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assert c.ocr_page_fraction == pytest.approx(0.0)
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assert "image_heavy" not in c.flags
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assert c.mean_text_quality > 0.8
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def test_full_page_image_routes_ocr():
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c = clf.classify_pdf(_full_page_image_pdf())
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assert c.recommended_tier == "ocr"
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assert c.ocr_page_fraction == pytest.approx(1.0)
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assert "image_heavy" in c.flags
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assert "scanned" in c.flags # no text layer at all
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# --- sampling bounds large docs ----------------------------------------------
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def test_large_doc_is_sampled():
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c = clf.classify_pdf(_digital_pdf(pages=120))
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assert c.page_count == 120
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assert c.sampled_pages <= clf.MAX_SAMPLED_PAGES
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def test_sample_indices_includes_first_and_last_page():
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idx = clf._sample_indices(100)
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assert idx[0] == 0
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assert idx[-1] == 99 # last page must be sampled (scanned-tail case)
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assert len(idx) <= clf.MAX_SAMPLED_PAGES
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# --- flag paths --------------------------------------------------------------
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def _image_with_mashed_text_pdf(pages: int = 2) -> bytes:
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# Full-page image with a junk (mashed/space-less) text layer over it -- a
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# scan whose OCR'd text layer is unusable.
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doc = pymupdf.open()
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pix = pymupdf.Pixmap(pymupdf.csRGB, pymupdf.IRect(0, 0, 600, 850))
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pix.clear_with(255)
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img = pix.tobytes("png")
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del pix # Pixmap holds native memory; release it before the loop
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mashed = "01322234567mobileoutstandingresilienceacademicachievement " * 3
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for _ in range(pages):
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page = doc.new_page(width=595, height=842)
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page.insert_image(page.rect, stream=img)
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page.insert_text((50, 60), mashed)
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data: bytes = doc.tobytes()
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doc.close()
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return data
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def test_scanned_flag_when_no_text_layer():
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c = clf.classify_pdf(_full_page_image_pdf())
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assert c.total_chars == 0
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assert "scanned" in c.flags
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assert c.recommended_tier == "ocr"
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def test_bad_text_layer_flag_on_image_with_junk_text():
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c = clf.classify_pdf(_image_with_mashed_text_pdf())
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assert c.total_chars > 0
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assert c.mean_text_quality < clf.MIN_TEXT_QUALITY
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assert "bad_text_layer" in c.flags
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assert c.recommended_tier == "ocr"
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def _mostly_text_one_image_pdf() -> bytes:
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# 3 digital text pages + 1 full-page-image page: one image-heavy page, but
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# ocr_frac = 1/4 < OCR_PAGE_FRACTION, so the doc routes fast.
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doc = pymupdf.open()
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pix = pymupdf.Pixmap(pymupdf.csRGB, pymupdf.IRect(0, 0, 600, 850))
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pix.clear_with(255)
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img = pix.tobytes("png")
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del pix # Pixmap holds native memory; release it before the loop
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for _ in range(3):
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page = doc.new_page(width=595, height=842)
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page.insert_text((50, 60), "Hello world this is clean text. " * 8)
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page = doc.new_page(width=595, height=842)
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page.insert_image(page.rect, stream=img)
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data: bytes = doc.tobytes()
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doc.close()
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return data
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def test_image_heavy_flag_without_ocr_routing():
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# The documented asymmetry operators rely on: a mostly-digital doc with one
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# full-page image carries the image_heavy flag yet still routes fast.
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c = clf.classify_pdf(_mostly_text_one_image_pdf())
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assert "image_heavy" in c.flags
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assert c.recommended_tier == "fast"
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assert c.ocr_page_fraction < clf.OCR_PAGE_FRACTION
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# --- classify_from_text (hot-path, derived from tier-1 extraction) -----------
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def test_classify_from_text_clean_routes_fast():
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txt = "the quick brown fox jumps over the lazy dog " * 3
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c = clf.classify_from_text(
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txt, [{"page": 1, "start_offset": 0, "end_offset": len(txt)}]
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)
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assert c.recommended_tier == "fast"
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assert c.mean_text_quality > 0.8
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assert c.flags == set()
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def test_classify_from_text_empty_routes_ocr():
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c = clf.classify_from_text("", [{"page": 1, "start_offset": 0, "end_offset": 0}])
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assert c.recommended_tier == "ocr"
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assert "no_text_layer" in c.flags
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assert c.total_chars == 0
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def test_classify_from_text_no_pages_routes_fast():
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# An empty/corrupt PDF (no page boundaries) is not OCR evidence -> "fast",
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# so the recorded classification metric isn't a misleading "ocr".
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c = clf.classify_from_text("", [])
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assert c.recommended_tier == "fast"
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assert c.ocr_page_fraction == pytest.approx(0.0)
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assert c.flags == set()
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