Read version from the backend /api/health endpoint instead of
hardcoding from package.json at build time. Add About section
in Settings with link to the DZone design article.
Replace the basic progress bar with a ring percentage indicator,
segmented batch bar with pulse animation, and an inline mini
progress bar in the top banner visible from any tab.
Re-read the job from DB before mark_completed so that
progress_current/progress_total written during batched conversion
are not overwritten by the stale in-memory object.
Add regression unit test and e2e assertion on final progress values.
Force Node.js 24 in all GitHub Actions workflows to suppress the
upcoming Node.js 20 deprecation warnings. Disable vue/html-indent and
vue/html-closing-bracket-newline ESLint rules that conflict with
Prettier formatting.
Closes#122
These env vars were set in the CI workflow step but never passed through
docker-compose to the container, so the backend always used defaults
(100 RPM), causing 429 errors in E2E tests.
The backend container only exposes port 8000 internally (expose, not
ports). The health check and Karate tests must go through the nginx
proxy on port 3000, the only port published to the host.
Set up a full E2E test suite (39 scenarios) using Karate against
the real API stack. Hybrid architecture: domain-based features +
cross-domain workflows, with data-driven testing and callable helpers.
Structure:
- e2e/pom.xml: Maven + karate-core 1.5
- 3 helpers (upload, analyze+poll, cleanup)
- 3 JSON schemas (health, document, analysis)
- 12 feature files across health, documents, analyses, workflows
- Tags: @smoke (2), @regression (35), @e2e (2)
- generate-test-data.py: fpdf2-based PDF generation (no binaries)
Also adds:
- RATE_LIMIT_RPM env var to make rate limiter configurable (0=disabled)
- CI job e2e with needs: [backend, frontend]
- e2e/ in .dockerignore
Closes#119
Replace hardcoded 5 MB upload limit with a configurable setting.
Backend exposes the value via /api/health, frontend reads it
dynamically for validation and UI messages.
Closes#48
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
