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Merge pull request #914 from cbcoutinho/fix/glyph-corruption-structured-escalation

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fix(document-processors): escalate glyph-corrupt PDFs to the structured tier
This commit is contained in:
Chris Coutinho
2026-06-16 21:01:39 +02:00
committed by GitHub
parent d09f8b53ef 4af7c7104b
commit 9b39ec1f49
9 changed files with 639 additions and 68 deletions
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nextcloud_mcp_server/config.py
+12
View File
@@ -171,6 +171,12 @@ _DEFAULTS: dict[str, Any] = {
"document_ocr_page_fraction": 0.5,
"document_ocr_min_page_chars": 16,
"document_ocr_detect_scanned": True,
# Tier-0 glyph-corruption trigger. When the fast (pypdfium2) extraction's
# doc-level C0-control-char ratio exceeds this, the text layer is treated as
# glyph-corrupt (a broken /ToUnicode mapping leaking raw glyph codes) and the
# doc escalates fast->structured (pymupdf re-extracts it correctly -- no OCR).
# 0 disables. Clean docs sit ~0; affected PDFs measured ~1-11% in testing.
"document_glyph_corruption_ratio": 0.02,
# OCR backend request timeout (seconds). Slow scanned newspapers can take
# 20-60s; raise/lower per tenant. Configurable so a tenant isn't stuck with
# the 180s default when its gateway has its own shorter ceiling.
@@ -384,6 +390,7 @@ _dynaconf = Dynaconf(
Validator("DOCUMENT_OCR_MIN_TEXT_QUALITY", gte=0, lte=1),
Validator("DOCUMENT_OCR_PAGE_FRACTION", gte=0, lte=1),
Validator("DOCUMENT_OCR_MIN_PAGE_CHARS", gte=0),
Validator("DOCUMENT_GLYPH_CORRUPTION_RATIO", gte=0, lte=1),
# Non-negative
Validator("DOCUMENT_CHUNK_OVERLAP", gte=0),
# Non-empty strings
@@ -896,6 +903,10 @@ class Settings:
document_ocr_page_fraction: float = 0.5
document_ocr_min_page_chars: int = 16
document_ocr_detect_scanned: bool = True
# Tier-0 glyph-corruption trigger: doc-level C0-control-char ratio above which
# the fast (pypdfium2) text layer is treated as glyph-corrupt and escalated
# fast->structured (pymupdf). 0 disables. See classifier._control_char_ratio.
document_glyph_corruption_ratio: float = 0.02
# Observability settings
metrics_enabled: bool = True
@@ -1532,6 +1543,7 @@ def get_settings() -> Settings:
"document_ocr_page_fraction": "DOCUMENT_OCR_PAGE_FRACTION",
"document_ocr_min_page_chars": "DOCUMENT_OCR_MIN_PAGE_CHARS",
"document_ocr_detect_scanned": "DOCUMENT_OCR_DETECT_SCANNED",
"document_glyph_corruption_ratio": "DOCUMENT_GLYPH_CORRUPTION_RATIO",
# Observability settings
"metrics_enabled": "METRICS_ENABLED",
"metrics_port": "METRICS_PORT",
nextcloud_mcp_server/document_processors/classifier.py
+159 -43
View File
@@ -27,9 +27,10 @@ Two entry points:
Recommended tier:
* ``ocr`` -- scanned / no-usable-text-layer (route to tier 3, when enabled)
* ``structured`` -- a text layer that is present but glyph-corrupt (the fast
extractor leaked raw glyph codes; high C0-control-char ratio). A different
in-cluster extractor (the pymupdf ``structured`` tier) recovers it -- no OCR.
* ``fast`` -- a usable digital text layer (stay on tier 1)
``structured`` (tier 2 / docling) is a separate service, not produced here.
"""
import logging
@@ -56,9 +57,20 @@ MIN_TEXT_QUALITY = 0.5
OCR_PAGE_FRACTION = 0.5
# A page with fewer extracted chars than this has effectively no text layer.
MIN_PAGE_CHARS = 16
# Doc-level control-character ratio above which the text layer is treated as
# glyph-corrupt and routed to the ``structured`` (pymupdf) tier, which re-extracts
# such PDFs correctly. Kept in sync with the DOCUMENT_GLYPH_CORRUPTION_RATIO
# setting default (the registry passes the per-tenant value). See
# ``_control_char_ratio``.
GLYPH_CORRUPTION_RATIO = 0.02
_WORD_RE = re.compile(r"\S+")
# Whitespace control characters that legitimately appear in extracted text
# (tab / newline / carriage-return / form-feed / vertical-tab). Every OTHER C0
# control char is a corruption signal -- see ``_control_char_ratio``.
_TEXT_WHITESPACE_CONTROLS = frozenset("\t\n\r\f\v")
@dataclass
class PageSignals:
@@ -67,6 +79,7 @@ class PageSignals:
image_coverage: float # 0..1 of page area covered by images
text_quality: float # 0..1; low = mashed/space-less/garbage layer
needs_ocr: bool # scanned or unusable text layer
control_ratio: float = 0.0 # 0..1; high = corrupt/glyph-leak text layer
@dataclass
@@ -76,10 +89,11 @@ class DocClassification:
total_chars: int
mean_text_quality: float
ocr_page_fraction: float # fraction of sampled pages flagged needs_ocr
recommended_tier: str # "fast" | "ocr"
recommended_tier: str # "fast" | "structured" | "ocr"
mean_control_ratio: float = 0.0 # doc-level C0-control-char ratio (glyph-leak)
flags: set[str] = field(
default_factory=set
) # scanned | bad_text_layer | image_heavy
) # scanned | bad_text_layer | image_heavy | corrupt_glyphs
pages: list[PageSignals] = field(default_factory=list)
@@ -116,6 +130,25 @@ def _text_quality(text: str) -> float:
return round(ws_score * len_score * overlong_score * merge_score, 3)
def _control_char_ratio(text: str) -> float:
"""Fraction of C0 control characters (excluding whitespace controls) in ``text``.
Near 0 for clean text in ANY script; elevated when the extractor leaked raw
glyph codes instead of Unicode -- the broken-/ToUnicode failure mode where a
subset font's character codes are returned uniformly offset (e.g. "WKH" for
"THE"). This is the language-agnostic counterpart to :func:`_text_quality`: a
uniform glyph/Caesar offset preserves whitespace and token lengths (so every
``_text_quality`` factor scores it ~1.0), but it litters the text with C0
controls -- digits/punctuation map to bytes below 0x20 -- which clean prose
never contains. Unlike a dictionary or stop-word probe it makes no assumption
about the document's language.
"""
if not text:
return 0.0
bad = sum(1 for c in text if ord(c) < 0x20 and c not in _TEXT_WHITESPACE_CONTROLS)
return bad / len(text)
def _sample_indices(page_count: int) -> list[int]:
if page_count <= MAX_SAMPLED_PAGES:
return list(range(page_count))
@@ -143,6 +176,64 @@ def _page_image_coverage(page: Any) -> float:
return min(img_area / page_area, 1.0)
def _route_from_signals(
*,
total_chars: int,
ocr_frac: float,
mean_quality: float,
control_ratio: float,
image_heavy: bool,
page_fraction: float,
min_text_quality: float,
glyph_corruption_ratio: float,
) -> tuple[set[str], str]:
"""Shared flag-set + recommended-tier decision for both classifier paths.
Routing precedence (cheapest correct fix first):
1. scanned / no text layer (``ocr_frac >= fraction`` AND ``total_chars == 0``)
-> ``"ocr"``
2. glyph-corrupt text layer (``control_ratio > glyph_corruption_ratio``)
-> ``"structured"``. pypdfium2 leaked glyph codes; the pymupdf
``structured`` tier re-extracts these correctly, so no paid OCR is
needed. The registry re-classifies the structured output, so a doc that
is ALSO partly scanned can still escalate to OCR from there.
3. junk/mashed text layer (``ocr_frac >= fraction``) -> ``"ocr"``
4. otherwise -> ``"fast"``
Flags are diagnostic and independent of the verdict (e.g. ``image_heavy``
fires on ANY image-heavy page; the OCR route needs a page FRACTION).
"""
# glyph_corruption_ratio <= 0 disables the signal (a ratio of 0 would otherwise
# fire on any single C0 control byte). total_chars > 0 also guarantees this
# never overlaps the scanned branch (total_chars == 0), so a doc is never both
# glyph-corrupt and "scanned".
glyph_corrupt = (
glyph_corruption_ratio > 0
and total_chars > 0
and control_ratio > glyph_corruption_ratio
)
flags: set[str] = set()
if ocr_frac >= page_fraction and total_chars == 0:
flags.add("scanned")
elif ocr_frac >= page_fraction and mean_quality < min_text_quality:
flags.add("bad_text_layer")
if glyph_corrupt:
flags.add("corrupt_glyphs")
if image_heavy:
flags.add("image_heavy")
if ocr_frac >= page_fraction and total_chars == 0:
recommended = "ocr"
elif glyph_corrupt:
recommended = "structured"
elif ocr_frac >= page_fraction:
recommended = "ocr"
else:
recommended = "fast"
return flags, recommended
def classify_pdf(content: bytes) -> DocClassification:
"""Classify a PDF from its bytes.
@@ -171,7 +262,14 @@ def classify_pdf(content: bytes) -> DocClassification:
# raises the diagnostic image_heavy flag below.
needs_ocr = quality < MIN_TEXT_QUALITY or len(text.strip()) < MIN_PAGE_CHARS
pages.append(
PageSignals(n, len(text), round(coverage, 3), quality, needs_ocr)
PageSignals(
n,
len(text),
round(coverage, 3),
quality,
needs_ocr,
round(_control_char_ratio(text), 4),
)
)
sampled = len(pages)
@@ -180,25 +278,28 @@ def classify_pdf(content: bytes) -> DocClassification:
round(sum(p.text_quality for p in pages) / sampled, 3) if sampled else 0.0
)
ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 0.0
# Char-weighted doc-level control-char ratio (p.control_ratio * char_count is
# the per-page bad-char count). The glyph-leak signal -- see _control_char_ratio.
# NOTE: this is over the <=MAX_SAMPLED_PAGES sample, so unlike classify_from_text
# (which scans the whole full_text) this diagnostic path can under-detect
# corruption concentrated outside the sampled pages. Acceptable here: the hot
# path is classify_from_text; this standalone pass is for diagnostics.
control_ratio = (
sum(p.control_ratio * p.char_count for p in pages) / total_chars
if total_chars
else 0.0
)
# Flags are diagnostic signals, intentionally independent of the routing
# verdict: image_heavy fires if ANY page is image-heavy, while the OCR route
# needs a FRACTION of pages (OCR_PAGE_FRACTION). So a mostly-digital doc with
# one full-page photo is flagged image_heavy yet still routes "fast" -- the
# flag_total{image_heavy} count is expected to exceed classified{ocr}.
flags: set[str] = set()
if any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages):
flags.add("image_heavy")
if (
ocr_frac >= OCR_PAGE_FRACTION
and total_chars
and mean_quality < MIN_TEXT_QUALITY
):
flags.add("bad_text_layer")
if ocr_frac >= OCR_PAGE_FRACTION and total_chars == 0:
flags.add("scanned")
recommended = "ocr" if ocr_frac >= OCR_PAGE_FRACTION else "fast"
flags, recommended = _route_from_signals(
total_chars=total_chars,
ocr_frac=ocr_frac,
mean_quality=mean_quality,
control_ratio=control_ratio,
image_heavy=any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages),
page_fraction=OCR_PAGE_FRACTION,
min_text_quality=MIN_TEXT_QUALITY,
glyph_corruption_ratio=GLYPH_CORRUPTION_RATIO,
)
return DocClassification(
page_count=page_count,
@@ -207,6 +308,7 @@ def classify_pdf(content: bytes) -> DocClassification:
mean_text_quality=mean_quality,
ocr_page_fraction=round(ocr_frac, 3),
recommended_tier=recommended,
mean_control_ratio=round(control_ratio, 4),
flags=flags,
pages=pages,
)
@@ -239,6 +341,7 @@ def classify_from_text(
min_text_quality: float = MIN_TEXT_QUALITY,
min_page_chars: int = MIN_PAGE_CHARS,
page_fraction: float = OCR_PAGE_FRACTION,
glyph_corruption_ratio: float = GLYPH_CORRUPTION_RATIO,
image_coverage: list[float] | None = None,
) -> DocClassification:
"""Classify from text already extracted by tier-1 -- no PDF re-open by default.
@@ -246,9 +349,13 @@ def classify_from_text(
The hot-path classifier. A page is OCR-worthy when its text is near-empty
(``< min_page_chars``) or its text-quality is junk (``< min_text_quality`` --
the word-merging signal). The doc recommends ``ocr`` once
``ocr_frac >= page_fraction``. Thresholds are passed in by the registry from
per-tenant settings. ``image_coverage`` (when supplied) only feeds the
``image_heavy`` diagnostic flag -- it does NOT route (see module docstring).
``ocr_frac >= page_fraction``. A doc whose text layer is present but
glyph-corrupt (doc-level C0-control-char ratio ``> glyph_corruption_ratio``,
the broken-/ToUnicode failure mode) instead recommends ``structured`` -- the
pymupdf tier re-extracts it correctly, no OCR needed. Thresholds are passed in
by the registry from per-tenant settings. ``image_coverage`` (when supplied)
only feeds the ``image_heavy`` diagnostic flag -- it does NOT route (see
module docstring).
``page_boundaries`` are ``{page, start_offset, end_offset}`` indexing into
``full_text``; ``image_coverage[i]`` (if given) aligns with the i-th boundary.
@@ -293,7 +400,14 @@ def classify_from_text(
# ``cov`` still feeds the diagnostic ``image_heavy`` flag below.
needs_ocr = len(seg.strip()) < min_page_chars or quality < min_text_quality
pages.append(
PageSignals(b["page"], len(seg), round(cov, 3), quality, needs_ocr)
PageSignals(
b["page"],
len(seg),
round(cov, 3),
quality,
needs_ocr,
round(_control_char_ratio(seg), 4),
)
)
sampled = len(pages)
@@ -305,23 +419,24 @@ def classify_from_text(
# page_count guard also skips escalation; defaulting to 0.0 keeps the
# recorded classification metric accurate rather than a misleading "ocr").
ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 0.0
# Doc-level control-char ratio -- the glyph-leak signal that routes to the
# structured tier. Computed over the WHOLE full_text (all pages), unlike
# classify_pdf which char-weights the up-to-MAX_SAMPLED_PAGES sample; the two
# are therefore not numerically identical for a >24-page doc with corruption
# concentrated outside the sample. full_text is used here because it is exactly
# the text that gets chunked + indexed and is robust to boundary edge cases.
control_ratio = _control_char_ratio(full_text)
# Flags gated on ocr_frac >= page_fraction (matching classify_pdf): a doc that
# routes "fast" must not carry a junk-layer flag just because a few isolated
# pages are bad -- otherwise the metric diverges from classify_pdf.
flags: set[str] = set()
if sampled and ocr_frac >= page_fraction:
if total_chars == 0:
# "scanned" (not "no_text_layer"): same name + meaning as classify_pdf
# so astrolabe_document_classifier_flag_total isn't split across two
# labels for the empty-text-layer case.
flags.add("scanned")
elif mean_quality < min_text_quality:
flags.add("bad_text_layer")
if any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages):
flags.add("image_heavy")
recommended = "ocr" if ocr_frac >= page_fraction else "fast"
flags, recommended = _route_from_signals(
total_chars=total_chars,
ocr_frac=ocr_frac,
mean_quality=mean_quality,
control_ratio=control_ratio,
image_heavy=any(p.image_coverage >= IMAGE_HEAVY_THRESHOLD for p in pages),
page_fraction=page_fraction,
min_text_quality=min_text_quality,
glyph_corruption_ratio=glyph_corruption_ratio,
)
return DocClassification(
page_count=len(page_boundaries),
@@ -330,6 +445,7 @@ def classify_from_text(
mean_text_quality=mean_quality,
ocr_page_fraction=round(ocr_frac, 3),
recommended_tier=recommended,
mean_control_ratio=round(control_ratio, 4),
flags=flags,
pages=pages,
)
nextcloud_mcp_server/document_processors/escalation.py
+6 -5
View File
@@ -49,7 +49,7 @@ class EscalationDecision:
kind: Literal["hop", "suppressed"]
to_tier: str
reason: Literal["empty_text", "low_confidence"]
reason: Literal["empty_text", "low_confidence", "corrupt_glyphs"]
def next_tier(current: str) -> str | None:
@@ -80,10 +80,11 @@ class EscalateError(Exception):
the junk text is never indexed, and it must never be swallowed by a broad
``except Exception`` on the indexing path.
``reason`` uses the existing escalation label vocabulary. This PR raises
``empty_text`` (scanned / no text layer) and ``low_confidence`` (junk text
layer); ``unsupported`` and ``forced`` are reserved for future callers and
not raised yet.
``reason`` uses the existing escalation label vocabulary: ``empty_text``
(scanned / no text layer), ``low_confidence`` (junk text layer), and
``corrupt_glyphs`` (a usable-looking layer whose extractor leaked raw glyph
codes -- the broken-/ToUnicode case -- recovered by a different in-cluster
extractor); ``unsupported`` and ``forced`` are reserved for future callers.
"""
def __init__(self, *, from_tier: str, to_tier: str, reason: str) -> None:
nextcloud_mcp_server/document_processors/registry.py
+115 -14
View File
@@ -247,6 +247,86 @@ class ProcessorRegistry:
result, content, settings, record=True, filename=filename
)
# The tier whose output produced the current ``classification`` -- used as
# ``from_tier`` for a subsequent OCR hop so a fast->structured->ocr cascade
# is attributed correctly (the OCR hop is from ``structured``, not a second
# ``fast`` escalation).
from_tier = "fast"
# Set when the structured tier ran but failed to parse. The document is then
# terminal -- the external path does not escalate a parse FAILURE either --
# so the OCR gate below must not treat it as a fallback.
structured_failed = False
# Escalate a poor fast extraction up the ladder (fast -> structured -> ocr),
# mirroring the external per-tier path so both modes behave identically. A
# glyph-corrupt layer (the extractor leaked raw glyph codes -- the
# broken-/ToUnicode case) OR a low-quality-but-non-empty layer first tries
# the structured (pymupdf) tier: free, in-cluster, and able to recover both.
# Only a scanned / no-text-layer doc (total_chars == 0) skips structured --
# a text extractor cannot conjure text from a pure raster -- and drops
# straight to OCR via the gate below. Structured is therefore NOT gated on
# document_ocr_enabled. Its output is re-classified (record=False -- the doc
# was already counted at the fast tier) so a doc that is ALSO partly scanned
# still reaches the OCR gate.
if (
classification is not None
and classification.page_count > 0
and (
classification.recommended_tier == "structured"
or (
classification.recommended_tier == "ocr"
and classification.total_chars > 0
)
)
):
structured = self._pdf_processor_for_tier("structured")
if structured is None:
# Structured isn't registered: mirror the external
# next_available_tier, which skips the missing rung and lands on
# OCR. Leave the recommendation unchanged so the OCR gate below
# picks it up (incl. a glyph-corrupt "structured" recommendation).
logger.debug(
"No structured processor registered; %s falls through to the "
"OCR gate (recommended_tier=%s)",
filename or "<bytes>",
classification.recommended_tier,
)
else:
reason = (
"corrupt_glyphs"
if classification.recommended_tier == "structured"
else "low_confidence"
)
record_document_escalation("fast", "structured", reason)
logger.info(
"Escalating %s fast->structured (reason=%s)",
filename or "<bytes>",
reason,
)
structured_result = await self._run_processor(
structured,
content,
content_type,
filename,
options,
progress_callback,
escalated=True,
)
if structured_result.success:
result = structured_result
from_tier = "structured"
classification = self._classify_result(
result, content, settings, record=False, filename=filename
)
else:
structured_failed = True
logger.warning(
"structured escalation did not succeed for %s (%s); keeping "
"the tier-1 result (OCR not attempted)",
filename or "<bytes>",
structured_result.metadata.get("parse_failed_reason", "error"),
)
# NOTE: the suppressed-escalation metric (document_escalation_suppressed_total,
# the "what-if OCR" signal; Deck #324) is intentionally NOT emitted on this
# inline/memory path -- it is instrumented only on the per-tier external
@@ -254,30 +334,37 @@ class ProcessorRegistry:
# is off here the would-be escalation is simply not taken (the gate below);
# operators reading the suppressed counter are on the procrastinate fleet.
#
# Escalate scanned / no-text-layer PDFs to OCR (tier-3) when enabled and
# a provider is registered. The fast tier is terminal otherwise. Note: a
# fast FAILURE (encrypted/corrupt -- result.success False, no
# classification) is NOT escalated; a PDF pypdfium2 can't open is treated
# as a hard failure (OCR reads the same bytes and would usually fail
# too). The page_count guard skips a zero-page (empty/corrupt) PDF, which
# OCR can't help either.
# Escalate to OCR (tier-3) when enabled and a provider is registered. Fires
# for a scanned / no-text-layer doc (recommended "ocr"), and also for an
# unresolved "structured" recommendation -- a glyph-corrupt doc whose
# structured rung wasn't registered -- so the inline path falls through to
# OCR exactly like the external next_available_tier. ``structured_failed``
# excludes a doc whose structured parse FAILED (terminal, like the external
# path). Note: a fast FAILURE (result.success False, no classification) is
# NOT escalated; a PDF pypdfium2 can't open is a hard failure (OCR reads the
# same bytes and would usually fail too). The page_count guard skips a
# zero-page (empty/corrupt) PDF, which OCR can't help either.
if (
classification is not None
and classification.recommended_tier == "ocr"
and not structured_failed
and classification.recommended_tier in ("ocr", "structured")
and classification.page_count > 0
and settings.document_ocr_enabled
):
ocr = self._pdf_processor_for_tier("ocr")
if ocr is not None:
reason = (
"empty_text"
"corrupt_glyphs"
if classification.recommended_tier == "structured"
else "empty_text"
if classification.total_chars == 0
else "low_confidence"
)
record_document_escalation("fast", "ocr", reason)
record_document_escalation(from_tier, "ocr", reason)
logger.info(
"Escalating %s fast->ocr (reason=%s)",
"Escalating %s %s->ocr (reason=%s)",
filename or "<bytes>",
from_tier,
reason,
)
ocr_result = await self._run_processor(
@@ -379,6 +466,7 @@ class ProcessorRegistry:
min_text_quality=settings.document_ocr_min_text_quality,
min_page_chars=settings.document_ocr_min_page_chars,
page_fraction=settings.document_ocr_page_fraction,
glyph_corruption_ratio=settings.document_glyph_corruption_ratio,
image_coverage=image_coverage,
)
except Exception:
@@ -520,6 +608,9 @@ class ProcessorRegistry:
- ``total_chars == 0`` (scanned / no text layer) -> target the ``ocr``
tier directly. Text-extractor tiers (``structured``) cannot conjure
text from a pure raster scan, so a structured hop would just be wasted.
- glyph-corrupt text layer (``recommended_tier == "structured"``) -> target
the ``structured`` tier; pymupdf re-extracts a broken-/ToUnicode layer
correctly, so OCR is never the target for this case.
- low-confidence but non-empty layer -> escalate to the next rung, so a
different in-cluster extractor can try before paying for OCR.
@@ -538,13 +629,23 @@ class ProcessorRegistry:
record=(current_tier == TIER_LADDER[0]),
filename=filename,
)
if classification is None or classification.recommended_tier != "ocr":
if classification is None or classification.recommended_tier not in (
"structured",
"ocr",
):
return None
# A zero-page (empty/corrupt) PDF gains nothing from any tier.
if classification.page_count <= 0:
return None
if classification.total_chars == 0:
minimum: str | None = "ocr"
minimum: str | None
if classification.recommended_tier == "structured":
# Glyph-corrupt text layer (the extractor leaked glyph codes): a
# different in-cluster extractor (the structured/pymupdf tier) recovers
# it -- never pay for OCR here. Target the structured rung specifically.
minimum = "structured"
reason = "corrupt_glyphs"
elif classification.total_chars == 0:
minimum = "ocr"
reason = "empty_text"
else:
minimum = None
nextcloud_mcp_server/observability/metrics.py
+7 -4
View File
@@ -272,7 +272,7 @@ document_bytes_processed_total = Counter(
document_escalation_total = Counter(
"astrolabe_document_escalation_total",
"Total document parse escalations between tiers",
# reason: low_confidence | empty_text | unsupported | error | forced
# reason: low_confidence | empty_text | corrupt_glyphs | unsupported | error | forced
["from_tier", "to_tier", "reason"],
)
@@ -287,7 +287,10 @@ document_escalation_total = Counter(
document_escalation_suppressed_total = Counter(
"astrolabe_document_escalation_suppressed_total",
"Would-be tier escalations suppressed because the target tier is disabled",
# reason: low_confidence | empty_text
# reason: low_confidence | empty_text | corrupt_glyphs. (corrupt_glyphs lands
# here only in the narrow case where the structured tier is unregistered AND
# OCR is registered-but-disabled: evaluate_escalation follows minimum="structured"
# past the missing rung to OCR, which is gated off -> suppressed{to_tier="ocr"}.)
["from_tier", "to_tier", "reason"],
)
@@ -332,7 +335,7 @@ document_classifier_flag_total = Counter(
# so flag{image_heavy} is expected to exceed classified{recommended_tier=ocr}.
"astrolabe_document_classifier_flag_total",
"Tier-0 classifier flags raised on documents",
["flag"], # image_heavy | scanned | bad_text_layer
["flag"], # image_heavy | scanned | bad_text_layer | corrupt_glyphs
)
document_text_quality = Histogram(
@@ -754,7 +757,7 @@ def record_document_escalation(from_tier: str, to_tier: str, reason: str) -> Non
Args:
from_tier: Tier that could not satisfactorily parse the document
to_tier: Tier the document was escalated to
reason: low_confidence | empty_text | unsupported | error | forced
reason: low_confidence | empty_text | corrupt_glyphs | unsupported | error | forced
"""
document_escalation_total.labels(
from_tier=from_tier, to_tier=to_tier, reason=reason
tests/fixtures/glyph_corruption.py
Vendored
+11
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@@ -0,0 +1,11 @@
"""Shared test data for the tier-0 glyph-corruption signal.
A fast-tier text layer that looks like words -- normal spacing and token lengths,
so it scores HIGH on ``_text_quality`` -- but leaks C0 control characters: the
broken-/ToUnicode signature that ``classifier._control_char_ratio`` catches. The
alphabetic tokens decode to a pangram under a -3 (Caesar) shift.
Kept in one place so the classifier and registry tiering tests can't diverge.
"""
GLYPH_CORRUPT_TEXT = "WKH \x0f TXLFN \x10 EURZQ \x11 IRA MXPSV \x0f RYHU \x10 GRJ " * 6
tests/unit/test_config.py
+24
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@@ -266,6 +266,30 @@ class TestChunkConfigValidation:
_reload_config()
assert get_settings().document_max_pdf_size_mb == pytest.approx(12.5)
def test_glyph_corruption_ratio_default_and_env_override(self):
"""document_glyph_corruption_ratio defaults to 0.02 and reads its env var.
Guards the _DEFAULTS-key-must-match-env-var footgun.
"""
assert Settings().document_glyph_corruption_ratio == pytest.approx(0.02)
with patch.dict(
os.environ, {"DOCUMENT_GLYPH_CORRUPTION_RATIO": "0.05"}, clear=True
):
_reload_config()
assert get_settings().document_glyph_corruption_ratio == pytest.approx(0.05)
@patch.dict(
os.environ,
{"DOCUMENT_GLYPH_CORRUPTION_RATIO": "1.5"},
clear=True,
)
def test_glyph_corruption_ratio_out_of_range_raises_error(self):
"""The ratio must be within [0, 1]."""
from dynaconf import ValidationError
with pytest.raises(ValidationError, match="DOCUMENT_GLYPH_CORRUPTION_RATIO"):
_reload_config()
def test_valid_chunk_settings(self):
"""Test valid chunk size and overlap configuration."""
settings = Settings(
tests/unit/test_doc_classifier.py
+107
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@@ -14,6 +14,7 @@ import pymupdf
import pytest
from nextcloud_mcp_server.document_processors import classifier as clf
from tests.fixtures.glyph_corruption import GLYPH_CORRUPT_TEXT
pytestmark = pytest.mark.unit
@@ -30,6 +31,18 @@ def _digital_pdf(
return data
def _glyph_corrupt_pdf(pages: int = 2) -> bytes:
# A born-digital PDF whose text layer carries the glyph-leak control chars,
# for the classify_pdf (diagnostic) path. pymupdf round-trips the C0 controls.
doc = pymupdf.open()
for _ in range(pages):
page = doc.new_page(width=595, height=842)
page.insert_text((50, 60), GLYPH_CORRUPT_TEXT)
data: bytes = doc.tobytes()
doc.close()
return data
def _full_page_image_pdf(pages: int = 2) -> bytes:
# A page whose entire area is a raster image -> looks scanned.
doc = pymupdf.open()
@@ -320,3 +333,97 @@ def test_scan_coverage_shorter_than_pages_aligns_without_crash():
assert all(p.needs_ocr is False for p in c.pages) # coverage no longer routes
assert "image_heavy" in c.flags # but page 0 still flags image_heavy
assert c.recommended_tier == "fast"
# --- glyph-corruption signal (broken /ToUnicode -> structured escalation) -----
# A uniform glyph/Caesar offset turns clean prose into alphabetic-but-wrong tokens
# (normal spacing + token length => HIGH text_quality) while digits/punctuation map
# to C0 control bytes. The control-char ratio is the only signal that catches this;
# _text_quality scores it ~1.0. Shared with the registry tiering tests.
_GLYPH_CORRUPT = GLYPH_CORRUPT_TEXT
def test_control_char_ratio_clean_is_zero():
assert clf._control_char_ratio("the quick brown fox") == pytest.approx(0.0)
# legitimate whitespace controls (tab/newline/CR/form-feed/vtab) don't count
assert clf._control_char_ratio("a\tb\nc\r\nd\f\ve") == pytest.approx(0.0)
def test_control_char_ratio_detects_glyph_leak():
assert clf._control_char_ratio(_GLYPH_CORRUPT) > clf.GLYPH_CORRUPTION_RATIO
def test_clean_text_not_flagged_corrupt():
txt = "the quick brown fox jumps over the lazy dog " * 3
c = clf.classify_from_text(
txt, [{"page": 1, "start_offset": 0, "end_offset": len(txt)}]
)
assert "corrupt_glyphs" not in c.flags
assert c.mean_control_ratio == pytest.approx(0.0)
assert c.recommended_tier == "fast"
def test_glyph_corrupt_routes_structured_not_ocr():
full = _GLYPH_CORRUPT
c = clf.classify_from_text(
full, [{"page": 1, "start_offset": 0, "end_offset": len(full)}]
)
assert c.recommended_tier == "structured"
assert "corrupt_glyphs" in c.flags
# The point: it is NOT a low-quality signal -- the cipher scores high, so only
# the control-char ratio diverts it (to structured, the free pymupdf re-parse).
assert c.mean_text_quality >= clf.MIN_TEXT_QUALITY
assert c.mean_control_ratio > clf.GLYPH_CORRUPTION_RATIO
def test_glyph_corruption_ratio_override_disables_trigger():
full = _GLYPH_CORRUPT
bounds = [{"page": 1, "start_offset": 0, "end_offset": len(full)}]
# A threshold of 1.0 can never be exceeded => not treated as corrupt => the
# other (high-quality) signals win => fast.
c = clf.classify_from_text(full, bounds, glyph_corruption_ratio=1.0)
assert c.recommended_tier == "fast"
assert "corrupt_glyphs" not in c.flags
def test_glyph_corruption_ratio_zero_disables_trigger():
full = _GLYPH_CORRUPT
bounds = [{"page": 1, "start_offset": 0, "end_offset": len(full)}]
# 0 disables the signal (rather than firing on any single control byte).
c = clf.classify_from_text(full, bounds, glyph_corruption_ratio=0.0)
assert c.recommended_tier == "fast"
assert "corrupt_glyphs" not in c.flags
def test_empty_doc_routes_ocr_not_structured():
# Precedence: a scanned/empty doc (no text layer) has no control chars to leak,
# so it must stay an OCR case, never structured.
c = clf.classify_from_text("", [{"page": 1, "start_offset": 0, "end_offset": 0}])
assert c.recommended_tier == "ocr"
assert "corrupt_glyphs" not in c.flags
def test_glyph_corrupt_takes_precedence_over_junk_text_layer():
# A layer that is BOTH glyph-corrupt (high control ratio) AND junk-quality
# (mashed, no whitespace -> low text_quality): both flags fire, but
# glyph-corrupt wins the route (structured, not ocr) -- the structured
# re-extract is the cheaper correct fix, and re-classification catches any
# residual junk afterwards.
text = "WKHTXLFNEURZQIRAMXPSV\x0f\x10\x11\x0f\x10" * 3
c = clf.classify_from_text(
text, [{"page": 1, "start_offset": 0, "end_offset": len(text)}]
)
assert c.recommended_tier == "structured"
assert "corrupt_glyphs" in c.flags
assert "bad_text_layer" in c.flags
def test_classify_pdf_glyph_corrupt_routes_structured():
# Symmetry with the classify_from_text routing on the standalone/diagnostic
# classify_pdf path (which re-opens the PDF and samples pages).
c = clf.classify_pdf(_glyph_corrupt_pdf())
assert c.recommended_tier == "structured"
assert "corrupt_glyphs" in c.flags
assert c.mean_control_ratio > clf.GLYPH_CORRUPTION_RATIO
assert c.mean_text_quality >= clf.MIN_TEXT_QUALITY # control signal, not quality
tests/unit/test_registry_tiering.py
+198 -2
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@@ -4,7 +4,7 @@ Covers: default fast-tier routing, the pymupdf rollback toggle, classification
recording derived from the extraction, and OCR escalation (on/off).
"""
from unittest.mock import MagicMock
from unittest.mock import MagicMock, call
import pytest
@@ -15,6 +15,7 @@ from nextcloud_mcp_server.document_processors.base import (
)
from nextcloud_mcp_server.document_processors.escalation import EscalationDecision
from nextcloud_mcp_server.document_processors.registry import ProcessorRegistry
from tests.fixtures.glyph_corruption import GLYPH_CORRUPT_TEXT
pytestmark = pytest.mark.unit
@@ -24,7 +25,9 @@ class _Fake(DocumentProcessor):
self,
name: str,
tier: str,
text: str = "clean text here",
# >= MIN_PAGE_CHARS of clean, whitespace-separated prose so the default
# classifies "fast" (a shorter string trips the near-empty OCR signal).
text: str = "this is clean readable prose text",
success=True,
pages: int = 1,
):
@@ -75,6 +78,7 @@ class _Settings:
page_fraction=0.5,
min_page_chars=16,
detect_scanned=False,
glyph_corruption_ratio=0.02,
# Guard off by default so existing tiering tests are unaffected; tests
# that exercise the size guard pass an explicit cap.
max_pdf_size_mb=0.0,
@@ -86,6 +90,7 @@ class _Settings:
self.document_ocr_page_fraction = page_fraction
self.document_ocr_min_page_chars = min_page_chars
self.document_ocr_detect_scanned = detect_scanned
self.document_glyph_corruption_ratio = glyph_corruption_ratio
self.document_max_pdf_size_mb = max_pdf_size_mb
@@ -244,6 +249,197 @@ async def test_no_ocr_escalation_when_disabled(monkeypatch):
assert res.processor == "fast"
# --- glyph-corruption escalation + full-ladder parity ------------------------
# A fast-tier text layer that looks like words (HIGH text_quality) but leaks C0
# control chars -- the broken-/ToUnicode signature the control-char ratio catches.
# Shared with the classifier tests so the two can't diverge.
_GLYPH = GLYPH_CORRUPT_TEXT
async def test_glyph_corrupt_escalates_fast_to_structured(monkeypatch):
# Not gated on OCR: structured is free + in-cluster, so a glyph-corrupt layer
# escalates fast->structured even with OCR disabled.
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=False))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text=_GLYPH), 20),
(_Fake("structured", "structured", text="clean recovered prose text"), 10),
)
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "structured"
esc.assert_called_once_with("fast", "structured", "corrupt_glyphs")
async def test_glyph_corrupt_no_structured_stays_fast(monkeypatch):
# No structured processor registered AND OCR off -> nothing to escalate to;
# keep fast (the inline counterpart of the external "suppressed" outcome).
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=False))
r = _registry((_Fake("fast", "fast", text=_GLYPH), 20))
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "fast"
async def test_glyph_corrupt_no_structured_falls_through_to_ocr(monkeypatch):
# Parity with the external path: structured unregistered but OCR enabled ->
# the glyph-corrupt doc falls through to OCR (not silently kept at fast).
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=True))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text=_GLYPH), 20),
(_Fake("ocr", "ocr", text="ocr recovered text"), 5),
) # no structured registered
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "ocr"
esc.assert_called_once_with("fast", "ocr", "corrupt_glyphs")
async def test_inline_lowconf_tries_structured_before_ocr(monkeypatch):
# Full-ladder parity with the external path: a junk-but-non-empty fast layer
# tries structured (fast->structured) BEFORE any OCR, even with OCR enabled.
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=True))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text="x" * 40), 20), # one long token -> quality ~0
(_Fake("structured", "structured", text="clean recovered prose text here"), 10),
(_Fake("ocr", "ocr", text="ocr text"), 5),
)
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "structured"
esc.assert_called_once_with("fast", "structured", "low_confidence")
async def test_inline_empty_skips_structured_straight_to_ocr(monkeypatch):
# The one intended shortcut: a scanned/no-text-layer doc (total_chars == 0)
# skips structured (it cannot extract text from a raster) and goes to OCR.
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=True))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text=""), 20),
(_Fake("structured", "structured", text="should not run"), 10),
(_Fake("ocr", "ocr", text="ocr text"), 5),
)
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "ocr"
esc.assert_called_once_with("fast", "ocr", "empty_text")
async def test_inline_fast_structured_ocr_cascade(monkeypatch):
# Full cascade: a junk-but-non-empty fast layer hops to structured, the
# structured re-extract is empty (a doc that was ALSO scanned), so it then
# hops to OCR. The second hop must be attributed from_tier="structured",
# NOT a second "fast" escalation.
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=True))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text="x" * 40), 20), # quality ~0, non-empty
(_Fake("structured", "structured", text=""), 10), # re-extract empty
(_Fake("ocr", "ocr", text="ocr recovered text"), 5),
)
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "ocr"
assert esc.call_args_list == [
call("fast", "structured", "low_confidence"),
call("structured", "ocr", "empty_text"),
]
async def test_inline_structured_still_corrupt_escalates_to_ocr(monkeypatch):
# Edge: the structured re-extract is ALSO glyph-corrupt (pymupdf also failed to
# decode). Re-classification stays "structured", so the OCR gate fires --
# attributed from_tier="structured" with reason corrupt_glyphs.
monkeypatch.setattr(reg_mod, "get_settings", lambda: _Settings(ocr=True))
esc = MagicMock()
monkeypatch.setattr(reg_mod, "record_document_escalation", esc)
r = _registry(
(_Fake("fast", "fast", text=_GLYPH), 20),
(_Fake("structured", "structured", text=_GLYPH), 10), # still corrupt
(_Fake("ocr", "ocr", text="ocr recovered text"), 5),
)
res = await r.process(b"%PDF-1.7", "application/pdf")
assert res.processor == "ocr"
assert esc.call_args_list == [
call("fast", "structured", "corrupt_glyphs"),
call("structured", "ocr", "corrupt_glyphs"),
]
def test_evaluate_escalation_glyph_corrupt_goes_structured(monkeypatch):
# External path mirrors the inline path: glyph-corrupt -> structured, never OCR.
monkeypatch.setattr(reg_mod, "record_document_classification", MagicMock())
r = _registry(
(_Fake("fast", "fast"), 20),
(_Fake("structured", "structured"), 10),
(_Fake("ocr", "ocr"), 5),
)
res = ProcessingResult(
text=_GLYPH,
metadata={
"page_count": 1,
"page_boundaries": [
{"page": 1, "start_offset": 0, "end_offset": len(_GLYPH)}
],
},
processor="fast",
)
decision = r.evaluate_escalation(res, b"%PDF", "fast", _Settings(ocr=True))
assert decision == EscalationDecision("hop", "structured", "corrupt_glyphs")
def test_evaluate_escalation_glyph_corrupt_no_structured_falls_through_to_ocr(
monkeypatch,
):
# External path with structured unregistered: next_available_tier skips the
# missing rung and lands on OCR, keeping the corrupt_glyphs reason.
monkeypatch.setattr(reg_mod, "record_document_classification", MagicMock())
r = _registry(
(_Fake("fast", "fast"), 20),
(_Fake("ocr", "ocr"), 5),
) # no structured registered
res = ProcessingResult(
text=_GLYPH,
metadata={
"page_count": 1,
"page_boundaries": [
{"page": 1, "start_offset": 0, "end_offset": len(_GLYPH)}
],
},
processor="fast",
)
decision = r.evaluate_escalation(res, b"%PDF", "fast", _Settings(ocr=True))
assert decision == EscalationDecision("hop", "ocr", "corrupt_glyphs")
def test_evaluate_escalation_glyph_corrupt_no_structured_ocr_disabled_suppressed(
monkeypatch,
):
# Structured unregistered AND OCR registered-but-disabled: the would-be OCR
# fallthrough is suppressed, and it carries the corrupt_glyphs reason (so the
# "what-if OCR" counter can show latent glyph-corruption demand).
monkeypatch.setattr(reg_mod, "record_document_classification", MagicMock())
r = _registry(
(_Fake("fast", "fast"), 20),
(_Fake("ocr", "ocr"), 5),
) # structured not registered; ocr registered but disabled below
res = ProcessingResult(
text=_GLYPH,
metadata={
"page_count": 1,
"page_boundaries": [
{"page": 1, "start_offset": 0, "end_offset": len(_GLYPH)}
],
},
processor="fast",
)
decision = r.evaluate_escalation(res, b"%PDF", "fast", _Settings(ocr=False))
assert decision == EscalationDecision("suppressed", "ocr", "corrupt_glyphs")
# --- Per-tier external path (Deck #323) -------------------------------------