Round-3 review nits on PR #920 (none blocking):
- escalation_tiers_signature: TODO noting future settings that can rescue a
previously-terminal document (a toggleable llm tier, a raised oversize cap)
should be folded into the signature so raising them auto-retries dead-letters.
- Terminal-path placeholder cleanup: a delete failure here is real Qdrant I/O,
not control-flow -- log at warning (was debug) for observability. Non-fatal
(the durable marker is already written).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A pathological PDF (a 206-page ChronoScan scan with ~3400 JBIG2/JPX images)
jammed a tenant's structured ingest worker in an infinite reprocess loop,
re-burning a 120s pymupdf4llm parse (and occasionally OOM-racing the 2Gi pod)
every few minutes.
Root cause: the per-user placeholder "failed" mark could not stop the loop. The
placeholder point ID is user-agnostic (uuid5("file:<doc_id>:placeholder")) but
the scanner's freshness gate, query, and status update all filter by user_id.
For a file visible to several users the single shared placeholder's user_id is
overwritten by whoever scanned last, so every other user's scan sees "no record"
and re-queues -- an N-user ping-pong that never honours the failed status.
Fix: when a parse fails terminally (no higher escalation tier available, e.g.
structured with OCR off) record a durable, content-addressed, user-agnostic
dead-letter marker (mirrors vector/sharing_state.py). The scanner consults it
tenant-wide for every user and skips re-queuing until the content (etag) OR the
escalation-tier set (tiers_sig -- e.g. OCR enabled) changes, so the document is
attempted once per content-version instead of forever.
- new vector/dead_letter.py: mark/is/clear, content-addressed marker carrying
is_placeholder=True (inherits search exclusion) + dead_letter=True
- escalation.escalation_tiers_signature(settings): retry-on-tier-change key
- processor: dead-letter terminal failures, clear on successful (re-)index
- scanner: user-agnostic is_dead_lettered skip beside claim_existing_index
- placeholder: exempt dead_letter markers from the orphan sweep (durability)
- metrics: astrolabe_document_dead_lettered_total{reason}
Deck #349.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address round-4 review on PR #914:
- glyph_corruption_ratio <= 0 now disables the signal (previously `control_ratio
> 0` fired on any single C0 control byte), matching the "0 disables" convention
used elsewhere (document_max_pdf_size_mb) and the config comment. Add a
zero-disables test.
- Correct the document_escalation_suppressed_total comment: corrupt_glyphs CAN
appear there in the narrow case where structured is unregistered and OCR is
registered-but-disabled (evaluate_escalation follows minimum="structured" past
the missing rung to a gated-off OCR). Add a test for that suppressed decision.
- Add a test for the double-corruption edge: a structured re-extract that is also
glyph-corrupt escalates structured->ocr with reason corrupt_glyphs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address round-3 review on PR #914:
- When a glyph-corrupt doc's structured rung is NOT registered, the inline path
now falls through to OCR (with reason corrupt_glyphs), mirroring the external
next_available_tier instead of silently keeping the fast result. A structured
parse FAILURE remains terminal (tracked via structured_failed), matching the
external path which does not escalate a failure. Added a debug log for the
unregistered case and "(OCR not attempted)" to the failure warning.
- Tests: inline + external glyph-corrupt fallthrough to OCR when structured is
unregistered; glyph-corrupt + junk-quality both-flags precedence (structured
wins over the bad_text_layer/ocr route).
- Note the total_chars>0 mutual-exclusion with the scanned branch in
_route_from_signals.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address round-2 review on PR #914:
- Add corrupt_glyphs to the document_classifier_flag_total label comment (it is
a live flag value emitted by record_document_classification).
- Mirror the full_text-vs-sampled control-ratio NOTE into classify_pdf so the
diagnostic path's under-detection trade-off is documented in place.
- Add test_classify_pdf_glyph_corrupt_routes_structured for routing symmetry on
the standalone classify_pdf path.
(SonarCloud quality gate is green — the prior S1244 finding was fixed last round.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address round-1 review on PR #914:
- Attribute the OCR hop in a fast->structured->ocr inline cascade to
from_tier="structured" (not a second "fast" escalation), so
astrolabe_document_escalation_total per-tier counts stay accurate.
- Add test_inline_fast_structured_ocr_cascade pinning that two-hop path and the
metric attribution.
- Note in classify_from_text that its doc-level control ratio is over full_text
(all pages), not the sampled subset classify_pdf uses.
- Clarify that corrupt_glyphs never lands in the suppressed-escalation counter.
- Dedupe the glyph-corrupt test string into tests/fixtures/glyph_corruption.py.
- Use pytest.approx for the control-char-ratio zero checks (SonarCloud S1244).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The fast (pypdfium2) extractor can leak raw glyph codes on subset fonts with a
broken /ToUnicode CMap. The result scores high on the existing text-quality
heuristic -- a uniform glyph/Caesar offset preserves whitespace and token
lengths -- yet is unsearchable. The structured (pymupdf) tier extracts the same
pages correctly.
Add a language-agnostic C0-control-character-ratio signal to the tier-0
classifier that detects this corruption and routes the document to a new
`structured` recommended_tier. Wire the fast->structured hop on the inline path
and generalise it so a low-quality-but-non-empty layer also tries structured
before OCR -- the inline and external ingest modes now follow the full
fast->structured->ocr ladder identically. A scanned / no-text-layer document
(total_chars == 0) still shortcuts straight to OCR, since a text extractor
cannot recover a pure raster.
New per-tenant tunable DOCUMENT_GLYPH_CORRUPTION_RATIO (default 0.02); escalation
metrics gain a `corrupt_glyphs` reason label.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 5 review (PR #910), all nits, no blockers:
- _result_from_success: a succeeded job with `pages=[]` (empty list, not just a
missing key) is now a per-document failure ("no pages returned") instead of a
silent 0-chunk success. Test added.
- Comment the deadline-expiry path: the gateway-side job isn't cancelled (no
cancel endpoint at this layer) — it's reaped by the gateway file purge; we just
stop polling it.
- Drop the vestigial status="pending" from the BatchOcrJob test fakes (the column
was removed in round 4; BatchPollResult.status fakes are untouched).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 3 review (PR #910):
- Guard an unexpected terminal batch status in _process_batch: anything that
isn't succeeded/failed (gateway version skew, a new lifecycle state) now marks
the document parse-failed instead of falling through to _pages_to_text([]) — a
0-chunk "success" that silently indexed empty text and re-submitted forever.
Test added.
- gateway_batch_client.submit: raise an actionable ValueError on a 2xx response
with no job_id (was a bare KeyError deep in the caller).
- Document that a _process_batch transport error intentionally propagates to
procrastinate for retry rather than falling back to sync (opt-in batch wants
the retry).
- Annotate _batch_client as GatewayBatchOcrClient | None (TYPE_CHECKING import
already present); clarify the delete_stale_for_doc first-submit no-op comment.
- Add a parametrized build_gateway_batch_client test (the gateway-only invariant:
mistral/none/no-URL -> None; gateway|auto + URL -> client).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 2 review (PR #910):
- BLOCKING: BatchOcrJobStore._shared_lock is now lazy-init (anyio.Lock | None,
created on first shared() call) instead of at class-definition time — matches
the CLAUDE.md "no anyio primitives at import time" rule and OcrProcessor's
pattern. The None-check->assign has no await between, so it's race-free.
- document_ocr_mode now normalizes via _enum_fields (case-insensitive, like
document_ocr_provider) instead of a strict dynaconf is_in Validator, so
DOCUMENT_OCR_MODE=Batch normalizes to "batch" rather than erroring. Tests for
case-normalization + invalid-value rejection.
- TYPE_CHECKING-gated GatewayBatchOcrClient import so build_gateway_batch_client
/ _get_batch_client are typed `GatewayBatchOcrClient | None` instead of Any
(runtime import stays lazy to avoid the import cycle).
- Rename ocr_options -> doc_identity_options (it's threaded to all tiers; only
OCR reads it) + clarify the comment.
- Drop the redundant forward-ref quotes on _shared_instance.
- Add direct _batch_identity unit tests (partial/empty options branches).
Left as follow-up: reusing one httpx.AsyncClient across submit/poll (same
per-call pattern as the existing sync _GatewayOcrBackend; no clean aclose hook
on the cached client today).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round 1 review (PR #910):
- BLOCKING: add document_ocr_mode / _batch_poll_seconds / _batch_max_wait_seconds
to config._field_map — without it dynaconf silently ignored the env vars and
DOCUMENT_OCR_MODE=batch could never be enabled in production. Add a regression
test asserting the three round-trip from env.
- migration 008: give batch_ocr_jobs a composite PRIMARY KEY on
(user_id, doc_id, doc_type, etag) instead of a bare UniqueConstraint (N1).
- OcrProcessor: use a dedicated _batch_client_lock instead of sharing the sync
backend lock (N3).
- tests: use https:// gateway URLs in the new fixtures to clear SonarCloud's
"insecure http" security hotspots (all 14 were test-only http://gw literals).
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add DOCUMENT_OCR_MODE=sync|batch (default sync). In batch mode the tier-3 OCR
processor submits documents to the embedding gateway's async Batch OCR routes
(POST /v1/ocr/batch + GET /v1/ocr/batch/{job_id}, astrolabe-cloud-website#372)
for ~50% cheaper large-corpus backfill. The direct Mistral OCR path is left
untouched. Tracked on Deck #332.
Batch jobs run minutes-hours, so the OCR tier cannot block (the procrastinate
worker reclaims jobs in `doing` after INGEST_STALLED_JOB_SECONDS). Instead it
submits, records the gateway job id in a new per-tenant `batch_ocr_jobs` table
(procrastinate args are immutable across retries), and raises a BatchPending
signal that TieredEscalationStrategy turns into a same-queue deferred re-poll —
releasing the worker slot between polls. On completion the per-page markdown is
indexed like the sync path; a failure or a job past
DOCUMENT_OCR_BATCH_MAX_WAIT_SECONDS marks the document parse-failed.
Batch is opt-in and gateway-only: with the direct mistral backend, no gateway
URL, or the inline/memory pipeline (which can't defer), it falls back to sync.
One batch job per document (coalescing N docs/job is a follow-up).
- embedding/gateway_batch_client.py: submit/poll client (reuses GatewayTokenProvider).
- vector/batch_ocr_store.py + migration 008: job tracking (portable SQLite+PG).
- document_processors/escalation.py: BatchPending control-flow signal.
- document_processors/ocr.py: batch state machine + sync fallback.
- vector/processor.py: thread doc identity to the OCR tier; raise BatchPending
from the pending sentinel; propagate it as control flow (not a failure).
- vector/queue/procrastinate.py: BatchPending -> same-queue retry_in, exempt
from the transient cap (bounded by the processor's deadline).
- config + docs; tests across client/store/processor/strategy/parse-tier.
1653 unit tests pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Review round 4 (both nits):
- Rename the flag to ignore_ocr_enabled so its OCR-specific scope is explicit at
the call sites (the gate only bypasses the OCR-enabled check).
- Add test_evaluate_escalation_empty_suppressed_even_when_structured_registered:
empty_text (minimum='ocr') skips a registered structured tier and suppresses to
ocr when OCR is off, never hopping to structured.
Deck #324.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The inline _process_pdf path does not emit document_escalation_suppressed_total;
the "what-if OCR" counter is instrumented only on the per-tier external path
(evaluate_escalation / _parse_pdf_tier). Comment the inline OCR gate so a reader
doesn't mistake the omission for a bug. Deferred the assert_never nit (typing
.assert_never is 3.11+; Literal+frozen already guard construction) and the
pre-existing minimum-ValueError pass (only "ocr"/None are ever passed).
Deck #324.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- escalation: EscalationDecision.reason is now Literal["empty_text",
"low_confidence"] (parity with kind; ty catches a bad label at call sites).
- processor: nest the decision handling so the hop branch is reached via an
explicit else under `if decision is not None` — exhaustive over the Literal
kind, no None-attribute risk.
- tests: add the "OCR processor unregistered (not just disabled) → None"
quadrant, locking in absent != suppressed.
Deck #324.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- escalation: EscalationDecision.kind is now Literal["hop","suppressed"] so ty
catches a bad kind statically (and the processor branch is exhaustive).
- processor: simplify the suppressed-escalation log line (no longer repeats
to_tier / tier).
- registry: clarify _tier_available's ignore_enabled drops the OCR-enabled gate
specifically (a future per-tier gate would extend the condition).
Deck #324.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
OCR is the paid, opt-in tier (DOCUMENT_OCR_ENABLED, default off). The per-tier
escalation gate already declines to hop to OCR when it's disabled (the pre-OCR
tier is terminal — no surprise cost), but that left operators blind to how much
OCR demand exists.
evaluate_escalation now returns a structured EscalationDecision:
- "hop" — a higher tier can run; the caller raises EscalateError (queue-hop).
- "suppressed" — the ideal next tier (e.g. ocr) exists but is DISABLED; the caller
indexes the current tier's output as terminal and records the
would-be hop on the new astrolabe_document_escalation_suppressed_total
{from_tier,to_tier,reason} counter instead of hopping.
- None — index as-is (good text, or no such tier at all).
So with OCR off, escalation_suppressed_total{to_tier="ocr"} is the latent OCR
demand an operator weighs before enabling OCR; enabling it converts these into
real document_escalation_total{to_tier="ocr"} hops. next_available_tier gains an
ignore_enabled flag to compute the *ideal* (enabled-gate-ignored) target.
Tests: registry suppressed vs hop vs terminal (incl. structured-hop-not-suppressed
when OCR off but structured available); _parse_pdf_tier records suppressed +
indexes without raising.
Deck #324 (parent #323).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- metrics: update_ingest_queue_depth now pre-zeroes every managed ingest queue
before applying live counts, so a queue that drains to empty (and drops out of
procrastinate's list_queues_async) reads 0 instead of sticking at its last
non-zero value (ghost backlog in Grafana/alerts). Adds a regression test.
- procrastinate: comment that _is_transient_infra_error treats all qdrant errors
as transient deliberately (bounded same-tier retry; over-broad is acceptable).
- escalation: note next_tier is the building block; production routing uses
ProcessorRegistry.next_available_tier.
- tests: add evaluate_escalation fast+ocr-only low-confidence -> ocr case.
Deck #323.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Register the periodic stalled-job reclaim on a dedicated ingest-maintenance
queue that every worker drains (any --tier), so reclaim still fires when the
fast fleet is scaled to zero and only ocr workers run. procrastinate's
periodic-defer dedup keeps it single-run across drainers.
- escalation: mark `unsupported`/`forced` reason labels as reserved (not raised).
- processor: note that options/progress_callback are intentionally not threaded
through _parse_pdf_tier yet (symmetric with the inline path).
- tests: assert TieredEscalationStrategy backoff progression (4/8/16/…/300s);
cover get_ingest_pending per-queue aggregation + the legacy job_counts
fallback; add an external-path zero-page no-escalation case; use the canonical
INGEST_QUEUE_FAST instead of the back-compat alias.
Deck #323.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Split external (procrastinate) document processing into per-tier queues so a
document is attempted at most once per tier and requeued to the next tier's
queue on a low-quality parse, using procrastinate's native retry.
- escalation.py: TIER_LADDER (fast->structured->ocr) + EscalateError signal
- registry: process_tier (one tier) + evaluate_escalation post-parse gate
(reuses classify_from_text) + next_available_tier; shared _classify_result
and _oversize_result with the inline pipeline
- processor: process_document(tier=...) runs one tier and raises EscalateError
before embed (junk text never indexed); inline memory path unchanged
- queue/procrastinate: ingest-fast|structured|ocr queues; TieredEscalationStrategy
(queue-hop on EscalateError, bounded same-tier transient retry); queue-aware
task; producer defers to ingest-fast; per-queue counts + all-queue reclaim
- cli: worker --tier {fast,structured,ocr}
- billing: pages_ocr usage event + pipeline_tier metadata (paid OCR billed apart)
- observability: astrolabe_ingest_queue_depth{queue,status} gauge + per-queue
counts in nc_get_vector_sync_status / management status endpoint
- config: INGEST_ESCALATION_ENABLED (default true), INGEST_TRANSIENT_MAX_ATTEMPTS
INGEST_ESCALATION_ENABLED=false and INGEST_QUEUE=memory preserve prior behaviour.
Deck #323.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-1 review nits. The constant now only gates the diagnostic `image_heavy`
flag (not routing), so the old name was misleading. Rename + reword its comment
to state the diagnostic-only intent. Also add a classify_pdf symmetry test
(`test_classify_pdf_image_heavy_clean_text_stays_fast`) pinning that a full-page
raster image with a clean text layer routes fast on the classify_pdf path too.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The tier-0 classifier escalated any page with raster-image coverage >=0.80 to
the OCR tier regardless of its text layer. On OHR-Bench this drove ~45% of all
OCR escalations: clean born-digital pages dominated by a figure, and scanned
pages that already carry a usable OCR text layer -- re-OCR adds nothing for
either, but each one was routed to the paid tier-3 OCR.
Route on the text signals only (near-empty or junk-quality layer). Image
coverage is still computed and still raises the `image_heavy` diagnostic flag,
but no longer routes. True scans with no/garbage text continue to escalate via
the empty-text and quality signals, so genuine OCR needs are unaffected.
Trade-off: image-only content on an otherwise-clean page (handwriting, stamps,
text inside figures) is no longer force-routed to OCR. This was previously
intentional; the escalation cost outweighed the benefit for RAG indexing.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-3 review on PR #892 found a real bug: the gateway backend's httpx.Timeout
raises httpx.ReadTimeout (a httpx.TimeoutException, NOT a builtin TimeoutError),
so the `except TimeoutError` added in r2 only covered the Mistral
(anyio.fail_after) path — gateway timeouts still fell through to
reason="error". Catch both (TimeoutError, httpx.TimeoutException) so either
backend's timeout lands in the dedicated parse_failed_reason="timeout" bucket.
Add an end-to-end test driving a gateway httpx.ReadTimeout through the
processor.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-2 review on PR #892:
- OcrProcessor.process now catches TimeoutError separately and returns
parse_failed_reason="timeout" with a populated message ("OCR timed out after
Ns"), instead of conflating timeouts with API errors under "error" and logging
an empty suffix. Lets dashboards tell a too-low timeout from a failing
provider. Test added.
- Add validator-rejection tests for DOCUMENT_OCR_TIMEOUT_SECONDS=0 (gte=1) and
DOCUMENT_MAX_PDF_SIZE_MB=-1 (gte=0), matching the existing validator-test
pattern.
- Comment the _Settings test fixture's max_pdf_size_mb=0.0 default.
SonarCloud: quality gate was failing on new_security_hotspots_reviewed (S5332
"use https") from an http:// URL in the new gateway-timeout test — switched to
https:// (mirrors commit 98c9d58e).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Round-1 review on PR #892:
- Wire DOCUMENT_OCR_TIMEOUT_SECONDS into _MistralOcrBackend too (was
gateway-only): wrap process_async in anyio.fail_after so the SDK-managed
client honours the setting; on expiry it fails fast as a clean parse error.
Test added.
- Tighten the misleading "honoured without a restart" comment — per-call
get_settings() is for test monkeypatching; a live change still needs a
restart since the backend is cached for the pod lifetime.
- Comment the size guard's two intentional gaps: an explicit processor_name
override bypasses it, and the early return skips the parse-duration histogram.
SonarCloud (new-code smells in the added tests):
- S1244 float-equality asserts → pytest.approx (test_config.py, test_ocr_processor.py).
- S1186/S7503: rewrite the gateway-timeout test with mocker AsyncMock/MagicMock
instead of a hand-rolled fake client (no empty method, no async-without-await).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two ingest-robustness fixes from card 309 (OHR-Bench smoke-test triage).
The OCR backend timeout was a hardcoded 180s module constant, so a tenant
whose gateway has its own shorter ceiling couldn't tune it. Promote it to
DOCUMENT_OCR_TIMEOUT_SECONDS (default 180), resolved per call via get_settings
so an override applies without a restart.
Large, awkward PDFs (e.g. a 42 MB scanned DUDE) were handed straight to the
fast/OCR tiers, where they burned the full OCR timeout for zero recovered
text. Add a pre-parse size guard in the tiered PDF pipeline: a PDF over
DOCUMENT_MAX_PDF_SIZE_MB (default 50, 0 disables) fails fast with
parse_failed_reason="oversize" before any tier runs, so the existing
permanent-failure path marks the placeholder failed and records
astrolabe_document_parse_failed_total{reason="oversize"} instead of retrying.
Both knobs go through Settings + dynaconf validators (env-var keys verified by
regression tests) and are documented under Background Indexing Configuration.
Refs: Deck board 12 card 309 (AC #3 OCR timeout + size guard).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Addresses round-1 review on #878:
- Move the eager `document_processors` imports out of the API startup graph:
`app.py` (get_registry now imported inside initialize_document_processors,
after the disabled early-return) and `vector/processor.py` (get_registry now
imported at its single use site). Importing `app` + `cli` no longer loads
`document_processors` / `_isolation` at all -- the #877 stack is fully out of
startup (pymupdf still loads via search/pdf_highlighter, a Windows-compatible
and separately-tracked concern).
- Make `tests/unit/test_pdf_parse_isolation.py` importable on Windows: guard the
top-level `import resource` with try/except and skip the three rlimit
computation tests via a `requires_resource` marker when the module is absent.
The Windows no-op / import-guard tests don't use the real module and still run.
- Fix the `# pragma: no cover` comment on the win32 branch to be accurate.
- Add `enable-cache: true` to the package-smoke setup-uv step.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
`document_processors/_isolation.py` did an unconditional module-level
`import resource`, a POSIX-only stdlib module absent on Windows. It was
pulled into the API startup path via
`server/webdav.py -> utils/document_parser -> document_processors`, so
the MCP server failed to start on Windows since 0.101.2 with
`ModuleNotFoundError: No module named 'resource'`.
- Guard the import behind `sys.platform`; bind `resource = None` on
win32. `_apply_mem_limit()` degrades to a logged no-op when the module
is unavailable (the RLIMIT_AS cap is a Linux-pod safety measure, not a
correctness requirement).
- Make the document-parser import in `server/webdav.py` lazy so server
startup never loads the ingest document stack
(document_processors -> pymupdf -> _isolation) at all -- it is only
needed when a file is actually read and parsed. This both fixes#877
and decouples the API layer from ingest-only deps.
- Add unit regressions for the no-op path and the win32 import guard.
- Add a cross-platform `package-smoke` CI job (ubuntu + windows) that
installs the package isolated and runs the CLI, exercising the
cli -> server -> webdav import chain that crashed in #877.
Fixes#877
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #863 round 4: classify_from_text's docstring now states that the
image_heavy flag (and the image-coverage trigger) are only set when
image_coverage is supplied, so the flag reads zero for tenants with
DOCUMENT_OCR_DETECT_SCANNED=false -- self-documenting the metric semantics.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #863 round 3:
- classify_from_text emits the "scanned" flag (was "no_text_layer") for the
empty-text-layer case -- same name + meaning as classify_pdf, so
astrolabe_document_classifier_flag_total isn't split across two labels for the
same concept (and matches the metric's documented vocab).
- classify_from_text logs at DEBUG when image_coverage length != the expected
min(pages, MAX_SAMPLED_PAGES), so a 1:1-alignment contract break (extractor
reorders/skips pages) surfaces instead of silently misattributing coverage.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #863 round 2:
- classify_pdf now flags a page needs_ocr on the SAME three signals as
classify_from_text (image scan OR low text-quality OR near-empty), not image
coverage alone. Previously a word-merged digital doc with no images routed
"fast" via classify_pdf but "ocr" via the pipeline -- so an operator
reproducing routing offline got a different answer. They now match.
- Add a test that when image_coverage is shorter than the page boundaries (the
MAX_SAMPLED_PAGES cap on large scans), the leading page uses the scan signal
and later pages fall back to text-quality.
Left as-is: overlong_score (>20) partially overlaps merge_score (>12) -- the
double-penalty on very-long tokens is intentional, not a bug (per review).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #863 review:
- MIN_TEXT_QUALITY 0.45 -> 0.5 so the module/diagnostic default matches the
DOCUMENT_OCR_MIN_TEXT_QUALITY setting (registry always passes the setting; this
keeps classify_pdf and the test/default path on the production threshold).
- image_coverage_per_page is bounded to MAX_SAMPLED_PAGES (the image pass is the
costly part, so a 200-page scan isn't fully rasterised on the hot path); pages
beyond the cap fall back to the text-quality signal, and page_fraction still
gates over every page.
- Extracted _page_image_coverage(page) helper, shared by classify_pdf and
image_coverage_per_page (DRY + keeps the tiling-double-count note in one place).
- Scan-detection failure logs at WARNING (not DEBUG) so a systematic failure on
an OCR-enabled tenant is visible at LOG_LEVEL=INFO.
- Add the missing DOCUMENT_OCR_MIN_PAGE_CHARS range-validator test.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The hot-path classifier escalated to OCR purely on character count, so a
scanned/handwritten PDF with a low-quality embedded text layer (>16 chars/page
but garbled) routed `fast` and indexed the junk -- e.g. Student 147.pdf's
"Little Acoms Primary"/"0110912020", which pollutes the vector and demotes the
doc in search (Deck #207).
- classifier: recalibrate `_text_quality` with a long-token-fraction term that
detects word-merging (dropped inter-word spaces) -- the dominant junk-layer
failure the old whitespace/overlong(>20) terms missed. Measured: the Student
147 scan ~0.42 (60% pages junk) vs >=0.94 for clean digital docs.
- classify_from_text now routes on quality + scan: a page is OCR-worthy if
near-empty OR low text-quality OR (when OCR + scan detection are enabled) it's
mostly a raster image. New `image_coverage_per_page` re-opens the PDF for the
scan signal, so that cost is paid only by OCR-opted-in tenants. Thresholds are
passed in from per-tenant settings (keyword-only).
- config: 4 per-tenant settings -- DOCUMENT_OCR_MIN_TEXT_QUALITY (0.5),
DOCUMENT_OCR_PAGE_FRACTION (0.5), DOCUMENT_OCR_MIN_PAGE_CHARS (16),
DOCUMENT_OCR_DETECT_SCANNED (true) -- with range validators.
- metrics: new astrolabe_document_ocr_page_fraction histogram (the value the
page-fraction threshold acts on) alongside document_text_quality, so operators
can tune the OCR escalation per tenant (quality vs cost).
Escalation gate, OCR backends, and off-by-default behavior unchanged (#858).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Follow-up to the tiered document processor (#858), landing the round-4 review
nits the reviewer approved without:
- pypdfium2_fast: free the page handle in an outer finally so a corrupt page
that makes get_textpage() raise can't orphan it.
- test: classify_from_text junk-text-layer path (non-zero chars, low quality,
high ocr_frac) flags bad_text_layer -- the hot-path coverage gap.
- test: build_ocr_backend raises ValueError when the gateway M2M client_id is
set without its token_url/secret.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #858 review round 3:
- document_tier1_engine / document_ocr_provider now validate + normalize via
Settings.__post_init__ _enum_fields (the repo's canonical opt-in-enum pattern;
case-insensitive) instead of dynaconf Validators. A typo now raises ValueError
at load and "Gateway" normalizes to "gateway".
- classify_from_text gates no_text_layer/bad_text_layer on ocr_frac >=
OCR_PAGE_FRACTION, matching classify_pdf -- a "fast"-routed doc with a few junk
pages no longer emits a misleading flag (keeps the shadow vs hot-path
classification metrics consistent).
- build_ocr_backend warns when an EXPLICIT provider is misconfigured
(gateway without EMBEDDING_GATEWAY_URL, mistral without MISTRAL_API_KEY)
instead of silently returning None.
- Pypdfium2FastProcessor.health_check probes the import; documented why
OcrProcessor.health_check is unconditionally True (lazy per-tenant backends).
- Removed the leftover per-boundary / per-chunk debug logging loops.
Tests: enum normalization + rejection for the two new settings.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
Address PR #858 review:
- 🔴 OcrProcessor now resolves its backend once and reuses it. Rebuilding per
call created a fresh GatewayTokenProvider each time -- discarding its M2M-token
cache, so every OCR'd document fetched a new token -- and a new Mistral client.
- 🔴 build_ocr_backend uses explicit ValueError (not assert, which is stripped
under `python -O`) for the gateway M2M triple.
- PIPELINE_TIER in the Qdrant payload now reflects the tier that actually
produced the doc: the registry stamps result.metadata["pipeline_tier"] and the
processor reads it (was hardcoded "fast", wrong for OCR/structured).
- Escalation now requires classification.page_count > 0, so a zero-page
(empty/corrupt) PDF isn't pointlessly sent to OCR; documented that a fast
FAILURE (encrypted/unopenable) is a hard failure and is not OCR-escalated.
- Documented the OCR page_boundaries separator-attribution choice.
- Downgraded the per-document page-boundary / page-assignment INFO logs to debug.
New tests: zero-page no-escalation, pipeline_tier stamping.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
OCR is an enhancement, not a gate. Previously, escalating a scanned doc to the
OCR tier returned the OCR result unconditionally -- so with DOCUMENT_OCR_ENABLED
=true but no backend configured (no gateway URL / no MISTRAL_API_KEY) the OCR
processor returned success=False and the whole document was marked failed and
skipped: strictly worse than leaving OCR off (where it would at least index the
tier-1 text).
Now the registry keeps the tier-1 fast result when the OCR escalation doesn't
succeed (no backend, API down, empty output), logging a warning. A
misconfiguration degrades gracefully instead of dropping scanned docs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds the OCR escalation target the tiered registry already routes to. Scanned /
no-text-layer PDFs (the tier-0 "ocr" verdict) escalate here when
document_ocr_enabled (default off).
Two interchangeable backends, selected by document_ocr_provider
(auto | gateway | mistral | none):
- gateway: POST to the Astrolabe Cloud model gateway's /v1/ocr -- the same
M2M-authenticated gateway as embeddings, so NO provider keys live in the pod
(the platform default; reuses EMBEDDING_GATEWAY_URL + the M2M creds).
- mistral: call the Mistral OCR API directly from the pod (MISTRAL_API_KEY), for
self-hosters / deployments without the gateway.
"auto" prefers the gateway, then direct Mistral.
Both return per-page markdown joined into text + exact page_boundaries (the
pdf_highlighter contract; bbox re-derived from the PDF bytes as for other tiers).
Validated end-to-end via direct Mistral on the scanned Student 147.pdf:
success, 15 pages, 22k chars, offsets exact, ~4s.
Settings: document_ocr_provider (enum-validated), document_ocr_model
("mistral/mistral-ocr-latest" -- gateway routes on the prefix, the direct mistral
backend strips it). OcrProcessor registered at lowest priority so it is never the
non-tiered default.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replaces single-engine pymupdf4llm extraction with a tiered pipeline (Deck #205,
follows the tier-0 classifier #855). pypdfium2 becomes the default and only
hot-path PDF extractor; pymupdf4llm is deprecated to a rollback toggle.
Why: pymupdf4llm's O(n^2) find_tables drove the OOM (#852) and the form-PDF
parse timeouts (#856), carries AGPL/commercial licensing liability, and -- per
the benchmarks -- recovers near-zero usable tables on the real corpus. pypdfium2
(Apache/BSD) extracts the same text far faster (Student 1a.pdf: 120s timeout ->
0.2s) with no table-detection bomb.
- document_processors/pypdfium2_fast.py: tier-1 "fast" processor emitting text +
exact page_boundaries (the pdf_highlighter contract). pymupdf processor is now
tier "structured" (the rollback engine), registered but not default.
- registry: tiered routing in ProcessorRegistry. tier-1 fast extracts, then
classification is DERIVED from that text (classifier.classify_from_text -- no
PDF re-open), records the classification metrics, and escalates scanned /
no-text-layer docs to the "ocr" tier when document_ocr_enabled (default off;
no provider yet, so fast is terminal). Wires record_document_escalation + the
real "escalated" span attribute (was hardcoded False).
- Removes the separate _shadow_classify pass from vector/processor.py -- it
re-opened every PDF and re-extracted text (~0.5-1.3s/doc of pure duplicated
CPU that lowered throughput); classification now rides the tier-1 extraction.
- Settings: document_tier1_engine ("pypdfium2" default | "pymupdf" rollback,
enum-validated), document_ocr_enabled (default false).
Tests: pypdfium2 extractor, registry tiering (fast routing, rollback, classify
recording, OCR escalation on/off), classify_from_text. Full unit suite green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #855 round 2:
- 🔴 _shadow_classify swallowed all exceptions at DEBUG, so a systematic
failure (pymupdf bug, memory pressure) is invisible at LOG_LEVEL=INFO and
trips SonarQube S2221/S5754. Log at WARNING instead (still best-effort --
indexing is unaffected).
- classifier: use `with pymupdf.open(...) as doc` instead of manual try/finally.
- tests: release the Pixmap's native memory (del pix) in the image fixtures.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #855 review (all non-blocking):
- classifier: _sample_indices now always includes the first AND last page (the
old evenly-spaced sample missed the tail, e.g. last sampled index 95 on a
100-page doc -- a scanned tail could be missed).
- classifier + metrics: document that flags are diagnostic and fire
independently of routing (image_heavy on ANY page vs the ocr route needing a
page FRACTION), so flag{image_heavy} is expected to exceed classified{ocr}.
- classifier: clarify the text-quality whitespace comment (caps at 12%) and note
the image double-count approximation (min() caps coverage).
- tests: add the scanned (no text layer) and bad_text_layer (junk text over an
image) flag paths, and a test pinning first/last-page sampling.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
First step of the tiered document-processor effort (Deck #203): a cheap, local
pre-pass that recommends which extraction tier a PDF should start in, emitting
metrics WITHOUT changing routing yet -- so we gather per-tenant doc-mix data
before turning escalation on.
document_processors/classifier.py: classify_pdf(content) -> DocClassification.
Page-sampled (bounded on large docs), <~1s. Cheap signals only -- text-layer
chars, a text-quality score (catches the "Student 147" failure where a text
layer exists but is mashed/space-less junk), and image coverage. A page that is
mostly a raster image routes to OCR: its content (handwriting, stamps) isn't in
any text layer. Deliberately no get_drawings/graphics-density signal -- it's
slow on the exact pages it'd flag, the hotfix's graphics_limit already makes the
parse safe, and the (future) tier-1 quality gate catches lost tables.
Validated on the sample corpus: born-digital 2-col arxiv and a digital student
record -> fast (tier 1); a scanned+handwritten form -> ocr (tier 3).
Wiring (vector/processor.py): _shadow_classify runs the classifier on PDFs in a
worker thread, best-effort (never blocks/fails indexing), gated by the new
DOCUMENT_CLASSIFY_ENABLED setting. Metrics: astrolabe_document_classified_total
{recommended_tier}, astrolabe_document_classifier_flag_total{flag},
astrolabe_document_text_quality histogram.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #852 round 3 (all 🟡, no blockers):
- config: DOCUMENT_PARSE_TIMEOUT_SECONDS is now float (default 120.0) so a
fractional value is honoured rather than silently stored in an int field;
matches anyio.move_on_after's float seconds.
- _isolation: comment that a clean rlimit MemoryError leaves the worker alive
in anyio's pool (vs the SIGKILL/BrokenWorkerProcess path that respawns) --
acceptable since RLIMIT_AS caps virtual address space, not RSS.
- processor: note the `if indexed is False` is a deliberate identity check --
a successful index (incl. dedup hit) returns None and must not be mistaken
for a parse failure.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address PR #852 review:
- pymupdf.py: the metadata `doc` was only closed on the PdfParseFailed and
success paths, so a failure in `_extract_metadata`/`mkdir`/`get_settings`
leaked it. `doc` is only needed for metadata + page_count (the heavy parse
works from `content` bytes in the worker), so open it, read metadata, and
close it immediately under try/finally; drop the two later doc.close() calls.
- processor.py: a permanent parse failure early-returned from `_index_document`,
after which `process_document` still recorded record_qdrant_operation("upsert",
"success") + record_vector_sync_processing(success) -- counting an OOM/timeout
bomb as astrolabe_documents_indexed_total{status="success"}. `_index_document`
now returns False on that path and the caller skips the success metrics (the
failure is already recorded via document_parse_failed_total + the registry's
document_parse_total{error}).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The document processor crash-looped on one pathological PDF: pymupdf4llm's
table/graphics detection over a page with ~1M vector path items ballooned past
the 2 GiB pod limit. The parse ran in a thread, so nothing could interrupt or
memory-bound it -- a single bad file OOM-killed the whole pod.
Run the parse in an isolated worker subprocess (anyio.to_process, cancellable)
with an RLIMIT_AS memory cap and a wall-clock timeout, so a pathological file
fails THAT document instead of the pod (new document_processors/_isolation.py).
Also pass graphics_limit (default 5000) to to_markdown -- validated to cut the
known trigger page from 112 s to 23 s with bounded memory.
On a permanent parse failure the processor returns success=False (instead of
raising, which would retry 3x); vector/processor.py marks the placeholder
"failed" and skips indexing, and the scanner stops re-queuing failed placeholders
until the file changes -- so a doomed file no longer churns.
New per-tenant (per-pod env) settings: DOCUMENT_PDF_GRAPHICS_LIMIT,
DOCUMENT_PARSE_TIMEOUT_SECONDS, DOCUMENT_PARSE_MEM_LIMIT_MB. New metric
astrolabe_document_parse_failed_total{reason=timeout|oom|error} surfaces hard
failures that previously killed the process before any except ran.
First PR of the tiered document-processor effort (Deck #199); tier 0/1/3
pipeline tracked separately.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Remaining items from the PR #831 Claude review:
- processor span symmetry: add "vector_sync.total_chars" to the sparse
embedding span (already on the dense span) and drop the redundant
"embedding.batch_size" attribute from both spans — it always equalled
vector_sync.chunk_count and would mislead once batching is split.
- metrics: document the deliberate "throughput counts only on full success"
contract in record_document_parse (partial extractions flagged
success=False are counted as a parse-error but never inflate
pages/chars/bytes throughput).
- config: extract _detect_base_provider() -> (family, model) as the single
source of truth for the provider-detection priority chain, shared by
get_embedding_model_name() and get_embedding_provider_family(). Preserves
the intentional gateway asymmetry (only the family method short-circuits).
- base.py: Optional[...] -> PEP 604 `... | None`; drop now-unused import.
Behavior unchanged (get_embedding_* outputs covered by test_config.py).
Refs Deck #175, PR #831.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Failed deletes no longer bump astrolabe_documents_indexed_total: the outer
except in process_document now gates doc_type on operation != "delete", so a
delete error is counted as processed-error but not as an indexing event.
Added test_failed_delete_is_processed_but_not_indexed.
- registry parse span: pass record_exception=True explicitly (matches
instrument_tool) and add a structured logger.warning on the parse-error path
(processor/tier/byte_size/duration_ms) for a Loki-aggregatable failed-parse
signal.
- test_error_does_not_increment_throughput: snapshot-before/delta pattern
instead of absolute 0.0 (counters are global singletons).
- config: document the deliberate gateway asymmetry between
get_embedding_model_name() (no gateway branch) and
get_embedding_provider_family() (short-circuits on gateway).
- Cleanup in touched scope: narrow `except (HTTPStatusError, Exception)` to
`except Exception` (drop now-unused import); convert registry signatures from
Optional[...] to `... | None`.
Refs Deck #175, PR #831.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Make per-tier bottlenecks in the document-processing pipeline
(scan -> fetch -> parse -> chunk -> embed -> Qdrant upsert) visible via
metrics, traces, and structured logs. Today the document_processors layer
emits only a logger.info line: no metric, no span, and page counts live only
inside a log string. The single processing-duration histogram is unlabeled and
whole-document, so it cannot isolate parse vs embed vs upsert.
New astrolabe_* metric family (distinct from the mcp_* protocol metrics):
- astrolabe_document_parse_{duration_seconds,total} + pages/chars/bytes counters
recorded at the ProcessorRegistry.process() boundary (covers all current and
future processors uniformly)
- astrolabe_document_escalation_total (dormant; tiered-pipeline readiness)
- astrolabe_embedding_{duration_seconds,requests_total,chunks_total,chars_total}
- astrolabe_document_chunks_total, astrolabe_documents_indexed_total{source,status}
Tracing: new document_processor.parse child span + enriched embed/chunk span
attributes (provider/model/batch_size/chunk_count). Structured logs gain a
consistent field vocabulary (doc_id, doc_type, processor, tier, pages, chars,
byte_size, chunks, duration_ms, status) so Loki can aggregate without regex.
Tier-readiness: processor/tier are labels from day one and a tier property is
added to DocumentProcessor, so adding docling/OCR/LLM tiers later is additive
(new label values, never new metrics). Tenant comes from the kube namespace
label; mime_type/model are span attributes only (cardinality). Existing
mcp_vector_sync_*/mcp_qdrant_* are left untouched.
Refs Deck #175 (superset of #173 Phase 2). Dashboard/recording-rules follow-up
tracked on #175 for homelab-argocd.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
