Address claude-review round 5 on #922 (verdict: good to merge after the docstring):
- BatchPending docstring: the deferred job stays on its own `(ocr-upstream)` tier
queue, not the retired `(ocr)` — batch mode is the upstream Mistral path only.
- classifier.py: clarify that `recommended_tier == "ocr"` is the classifier's
COARSE vocabulary ("needs OCR"), resolved to a concrete rung
(ocr-incluster -> ocr-upstream) by the registry — NOT a TIER_LADDER tier name.
- Added test_evaluate_escalation_suppressed_targets_incluster_four_rung: with both
OCR rungs registered but both flags off, the suppressed what-if-OCR signal names
the cheapest ideal rung (ocr-incluster), not ocr-upstream.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Address claude-review round 3 on #922:
- Important: _process_batch emitted "no gateway backend (provider=mistral or
EMBEDDING_GATEWAY_URL unset)" for the gateway_only in-cluster rung, where the
gateway IS configured — sending operators chasing a non-existent config
problem. The real reason is "in-cluster GPU is synchronous-only; batch is the
upstream path". Guard the warning with `if not self._gateway_only`. Extended
test_gateway_only_processor_never_uses_batch_mode to drive _process_batch and
assert _batch_fallback_warned stays False.
- Nits: refresh stale ladder in escalation.py module docstring
(fast->structured->ocr-incluster->ocr-upstream); fix "minimum='ocr'" ->
"ocr-incluster" in a test docstring; rename stale tier="ocr" ->
"ocr-upstream" in test_process_tier_oversize_fails_fast.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Insert a configurable in-cluster OCR rung into the escalation ladder (Deck #353):
a tier2-eligible doc is OCR'd on the on-demand burst GPU before falling through to
paid upstream OCR. The in-cluster backend is reached ONLY via the embedding gateway
(model prefix routes to the GPU over the tailnet) and is a config value (default
surya/surya-ocr-2, swappable to e.g. lightonocr) — never hard-coded.
Ladder: fast -> structured -> ocr-incluster -> ocr-upstream
(queues ingest-ocr-incluster / ingest-ocr-upstream).
- escalation.py: 4-tier ladder; in-cluster flag folded into the dead-letter signature.
- ocr.py: OcrProcessor(name, tier, model_setting, gateway_only); build_ocr_backend(
..., model=, gateway_only=) — gateway_only forces the gateway backend (never the
direct Mistral fallback), disabling the tier with a warning if no gateway URL.
- registry.py: per-rung enable map; scanned docs target minimum="ocr-incluster";
inline path runs the cheapest available OCR rung.
- procrastinate.py: two OCR queues; legacy ingest-ocr kept as a drain target.
- config.py: DOCUMENT_OCR_INCLUSTER_ENABLED (off) + DOCUMENT_OCR_INCLUSTER_MODEL.
- __init__.py: register the two OCR instances; vector/processor.py: pages_ocr
metered for the upstream (paid) rung only; cli.py: new --tier choices + legacy drain.
- metrics.py: zero the legacy ingest-ocr queue gauge during rollout.
- tests: migrated to the split ladder + new tests (gateway-only forcing, per-tier
model incl. lightonocr override, no-hard-coded-surya guard). 1792 pass; ruff + ty green.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
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>
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>
- 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>
