Hybrid approach: reuse LocalChunker to chunk the DoclingDocument JSON
returned by Serve, so chunking works identically in both local and
remote modes without calling Serve's chunk endpoint.
Backend:
- _build_chunker() always returns LocalChunker (remove engine guard)
- Use docling-core[chunking] extra for required dependencies
- Skip client-side batching in remote mode (Serve manages its own
resources, and batching discards document_json needed for chunking)
- Fix Serve form fields: remove generate_page_images (not a Serve
field), use repeated form keys for to_formats and page_range
- Log Serve error response body on 4xx/5xx for diagnosis
- Fix FastAPI 204 DELETE routes missing response_model=None
Frontend:
- Update chunking feature flag to enable Prepare UI in remote mode
Closes#51
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
All endpoint functions, lifespan context manager, and get_connection now
have explicit return types. Upload endpoint returns 201 Created instead
of 200 to follow REST conventions.
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.
AnalysisService gains rechunk() and inline chunking during conversion.
ChunkingOptionsRequest/ChunkResponse schemas, POST rechunk endpoint,
and conditional chunker injection in main.py (local engine only).
- 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>
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>
