- local_converter.py: remove redundant `_default_converter = None` in
except block of `_ensure_default_converter` (variable was already None,
re-raised immediately — dead store)
- test_analysis_service.py: replace bare `await task` with
`await asyncio.gather(task)` to satisfy static analysis
- Replace French mode strings (configurer/verifier/preparer) with English
equivalents (configure/verify/prepare) in StudioPage.vue and tests
- Extract _build_conversion_options, _run_conversion, _finalize_analysis
from _run_analysis_inner to respect Single Responsibility Principle
- Rename _get_default_converter to _ensure_default_converter to reflect
its lazy-init side effect
Closes#136, closes#137, closes#138
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
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
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)
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
Lightweight sliding-window per-IP rate limiter (100 req/min default)
with no external dependency. Health endpoint is excluded. Returns 429
with Retry-After header when exceeded. Sufficient for single-process
SQLite deployments; document the Redis upgrade path for scale.
Unbounded asyncio.create_task calls could exhaust CPU and memory on
modest hardware. Add a configurable semaphore (MAX_CONCURRENT_ANALYSES,
default 3) so excess jobs queue instead of running all at once.
UPLOAD_DIR and DB_PATH were read directly from os.environ, bypassing
the Settings dataclass. This caused an inconsistency where overriding
Settings had no effect on these values. Now all modules import from
infra.settings.settings.
domain/ must be pure with no external dependencies. bbox.py imports
docling_core and belongs in infra/. Also refactor ServeConverter to
use the canonical to_topleft_list via BoundingBox instead of
duplicated manual coordinate conversion. Move docling-core to base
requirements since it is now needed in both modes.
Replace hardcoded version strings with build-time injection:
- Frontend: Vite __APP_VERSION__ from env or package.json
- Backend: APP_VERSION env var exposed via /health endpoint
- Docker: build arg propagated through both stages
- CI: release workflow extracts version from git tag
Document branching strategy and release process in CONTRIBUTING.md.
Catch up CHANGELOG with v0.2.0 and Unreleased sections.
Sync package.json version to 0.3.0.
Extract bounding boxes from chunk doc_items provenance in the chunker,
propagate through domain/service/API layers, and render highlighted
bboxes on canvas when hovering a chunk card. Reset highlights on
mode and page changes to prevent stale visual state.
LocalChunker implements DocumentChunker port using docling-core chunkers.
LocalConverter now serializes DoclingDocument to JSON for re-chunking support.
Docling Serve expects array fields (to_formats) as repeated multipart
keys (to_formats=md&to_formats=html&to_formats=json), not a JSON
string. Changed _build_form_data to return list[tuple] so httpx sends
repeated keys correctly. Fixes 422 Unprocessable Entity on convert.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bug #1: _on_task_done now receives job_id via functools.partial and
calls _mark_failed when the background task raises or is cancelled,
preventing jobs from being stuck in RUNNING state forever.
Bug #5: _parse_response wraps json.loads in try/except JSONDecodeError
so malformed json_content strings fall back gracefully instead of crashing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Delete domain/parsing.py (broke hexagonal layering by importing infra)
- Migrate all tests to import directly from domain.value_objects and
infra.local_converter
- Rewrite ServeConverter to match real Docling Serve v1 API contract:
options sent as individual form fields (not JSON blob), response
parsed from document.json_content (DoclingDocument), proper bbox
coord_origin handling (TOPLEFT/BOTTOMLEFT)
- Transmit all conversion options including generate_picture_images
- Replace fragile lazy import circular dep with FastAPI Depends() +
app.state for AnalysisService injection
- Add frontend file size validation (50MB) before upload
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement the HTTP client adapter that delegates document conversion
to a remote Docling Serve instance via its /v1/convert/file endpoint.
Switchable via CONVERSION_ENGINE=remote env var. Includes health check,
API key auth, response parsing, and 30 new tests covering parsing,
type mapping, HTTP calls, and DI wiring.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extract domain value objects and ports from parsing.py, move Docling-specific
code to infra/local_converter.py, and convert analysis_service to a class
with injected DocumentConverter. This prepares the codebase for plugging in
alternative conversion backends (e.g. Docling Serve) via the Protocol pattern.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
