Backend — live runner
- New `POST /api/documents/:id/rag` endpoint. Loads `document_json` from
SQLite, reconstructs the DoclingDocument, wraps the model id in
`ModelIdentifier(ollama_name=...)`, and calls `agent._rag_loop`
off-thread (blocking sync call). Returns a `RAGResult` in the shape
the existing v1 import path already consumes, so the frontend overlay
is fully reused.
- `_rag_loop` is private upstream; we call it because `run()` wraps the
answer in a synthetic DoclingDocument and drops the iteration trace.
- Settings: `RAG_ENABLED`, `OLLAMA_HOST`, `RAG_MODEL_ID`. Router mounts
unconditionally; handler 503s when the flag is off or deps aren't
installed. `rag_available` surfaced in `/api/health`.
- Maps known docling-agent bugs to readable HTTP errors: 502 with
"the model couldn't produce a parseable answer" when `_rag_loop`
raises `IndexError` from `find_json_dicts([])[0]` after 3 + 3
rejection-sampling retries (model-dependent).
- Tests: 11 cases (flag off, query empty, no analysis, happy path,
model_id wrap, Ollama env, IndexError → 502, other errors → 500,
deps missing → 503).
Backend — bug fix
- Default `BATCH_PAGE_SIZE` flipped from `10` to `0` to match the
dataclass default. The old default silently dropped `document_json`
(see `domain/services.merge_results`) for any doc > 10 pages, which
broke the reasoning tunnel. Set `BATCH_PAGE_SIZE>0` explicitly on
memory-constrained deploys if batching is wanted.
Frontend — runner UX
- `features/reasoning/api.ts:runReasoning()` — POST wrapper.
- `RunReasoningDialog.vue` — query textarea + optional model_id
override. Blocks close while running, 20-40s loading state,
synthesises a sidecar-shaped envelope so the panel surfaces query +
model the same way an imported trace would.
- `ReasoningWorkspace.vue` — primary "Run reasoning" button; "Import
trace" relegated to ghost secondary.
- Store: `runDialogOpen`, `running`, `setRunning`.
Frontend — answer polish
- Answer rendered through `marked` + DOMPurify (models emit markdown
lists; `pre-wrap` rendered them as plain "1. …" strings).
- Dedicated answer block with orange border, "ANSWER" label, "Copy"
button (clipboard + "Copied ✓" feedback).
- IterationCard: drop the duplicate `response` block (the main answer
is authoritative); style reasons equal to `"fallback"` (docling-agent
`select_from_failure` placeholder) as italic muted "— no structured
rationale".
Frontend — node details contents
- Clicking a SectionHeader (or any node with compound children) lists
its contained elements in `NodeDetailsPanel` under a new "Contents"
block. Children come from the same `parentMap` used for Cytoscape
compound parenting (explicit PARENT_OF + synthetic section scope),
inverted once and cached as a computed.
- Click a child row → pan the viewport to it + swap the selection.
Housekeeping
- `cytoscape-navigator` removed from `package-lock.json` (follow-up
from the minimap removal in the previous commit).
Add Neo4j as an optional graph-native storage layer (ingestion profile).
Introduces infra/neo4j with a singleton async driver wrapper and an
idempotent bootstrap of constraints + indexes, wired into the FastAPI
lifespan. Integration tests skip when no live Neo4j is reachable.
Refs #186
- Move DEFAULT_PAGE_WIDTH/HEIGHT to domain/value_objects.py and import in both converters
- Add opensearch_default_limit to Settings (configurable via OPENSEARCH_DEFAULT_LIMIT env var)
- Pass settings.conversion_timeout to ServeConverter, removing independent _DEFAULT_TIMEOUT
- Update OpenSearchStore to accept default_limit from Settings via constructor
Default value of 5 is now in the application code (settings.py) instead
of only in the Docker image ENV. Consistent across all deployment modes
(dev local, Docker, tests). Aligned docker-compose files and docs.
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
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)
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
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.
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 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>
