GitHub only auto-closes issues when PRs target the default branch.
This workflow scans commit messages for Closes/Fixes patterns on
push to release/** and closes the referenced issues via gh CLI.
Closes#115
pypdfium2 is imported in analysis_service for PDF page counting
(batch feature #56) but was missing from requirements.txt, causing
CI collection errors on all 4 test modules.
Use Docling's native page_range parameter to split large PDFs into
sequential batches, preventing memory exhaustion and timeouts.
Progress is reported via existing polling mechanism.
Closes#56
Use `import infra.local_converter as lc_mod` consistently instead of
mixing `import` and `from ... import` for the same module.
Addresses CodeQL review comment on PR #58.
Raw PyTorch/Docling stack traces are no longer shown to the user.
Common failures (missing compiler, OOM, lock contention, corrupted
document) are mapped to actionable messages. Unknown errors are
truncated to 200 chars.
Ref #57 (M1)
If the lazy-init of the default converter fails (e.g. model download
error), the singleton was left as None but subsequent calls would not
retry. Now the failed state is cleared so the next request retries.
Ref #57 (H5)
A single transient network error (502, timeout) no longer kills the
polling loop. The frontend now tolerates up to 3 consecutive errors
before abandoning. Successful fetches reset the counter.
Also aligns frontend polling timeout (15 min) with backend timeout.
Ref #57 (H3, M5)
TableFormerMode.ACCURATE is very expensive on CPU (~3-5x slower).
The default can now be set to "fast" on resource-constrained
environments (HF Spaces) via the DEFAULT_TABLE_MODE env var.
User-specified table_mode in the request still takes precedence.
Ref #57 (H1)
Defense-in-depth: even if upload validation passes, Docling itself
now enforces page count and file size limits. Configurable via
MAX_PAGE_COUNT and MAX_FILE_SIZE env vars (0 = unlimited).
Ref #57 (C3)
A frozen conversion holding the lock indefinitely blocks all subsequent
jobs. Using lock.acquire(timeout=300) fails fast with a clear error
instead of waiting forever.
Ref #57 (C2)
Docling's native document_timeout is the only mechanism that can
interrupt processing inside a blocked thread (OCR, table extraction).
Without it, asyncio.wait_for cannot stop a frozen conversion.
Configurable via DOCUMENT_TIMEOUT env var (default: 120s).
Closes#57 (C1)
Prevents PyTorch/Docling pipeline crashes on HF Spaces CPU by:
- Reducing max file size from 50 MB to 5 MB
- Adding configurable MAX_PAGE_COUNT setting (env var, default unlimited)
- Increasing conversion timeout from 600s to 900s
- Adding frontend upload validation with explicit error messages
- Exposing maxPageCount via /api/health for dynamic UI hints
local_converter imports docling at module level, so any test that
touches it crashes when docling is not installed. Skip those tests
with importorskip / skipif so the CI (which only has docling-core)
passes cleanly.
The docling library (torch, transformers) is too heavy for lightweight
CI environments that only install docling-core. Use importorskip so
these tests are cleanly skipped instead of crashing collection.
Adapt test expectations to external changes: upload returns 200,
ValueError yields 400, and schemas now accept both snake_case and
camelCase via AliasChoices.
