* docs: rename Clean Architecture → Hexagonal Architecture (ports & adapters)
Le backend suit le pattern ports & adapters (ports dans domain/ports.py,
adaptateurs dans infra/), pas Clean Architecture au sens Uncle Bob.
Aligne la terminologie dans README, docs/architecture.md, ADR guide,
audit master, fiche audit 01, et la nav mkdocs.
Les noms de fichiers et la commande /audit:clean-architecture restent
stables pour preserver les liens croises et les skills existants.
* feat(settings): add paste-image size/type limits surfaced via /api/health
Introduces MAX_PASTE_IMAGE_SIZE_MB (default 10) and
PASTE_ALLOWED_IMAGE_TYPES (default image/png,image/jpeg,image/webp)
env vars so the upcoming Verify-mode clipboard-paste handler can
validate client-side against the same limits the backend enforces.
Follows the existing MAX_FILE_SIZE_MB pattern. Ships the accepted
design doc at docs/design/195-copy-paste-image-verify-mode.md.
Refs #195
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).
Adds the `docling-agent` reasoning-trace viewer as a Studio tunnel, per
`docs/design/reasoning-trace.md`. Users pick an analyzed document, import
a RAGResult JSON, and the iterations are overlaid on the document graph.
Graph source is decoupled from Neo4j: a new pure builder
(`infra/docling_graph.build_graph_payload`) reads `document_json` from
SQLite and emits the same Cytoscape-shaped payload that `fetch_graph`
returns from Neo4j. Neo4j stays exclusive to the Maintain ingestion
pipeline. Shared DoclingDocument helpers live in `infra/docling_tree.py`
so TreeWriter and the builder can't drift on label taxonomy or tree walks.
Also removes the Cytoscape minimap (cytoscape-navigator) from GraphView:
second render instance hurt perf on large documents for no UX win.
Backend
- new `GET /api/documents/:id/reasoning-graph` (SQLite-only)
- new `infra/docling_tree.py`, `infra/docling_graph.py`
- `analysis_repo.find_latest_completed_by_document`
- tests: `test_docling_graph.py` (builder), `test_graph_api.py` (endpoint)
Frontend
- `features/reasoning/` — store, overlay, types, panel, import dialog,
workspace, doc picker
- new `ReasoningPage` + `/reasoning` and `/reasoning/:docId` routes
- `GraphView` gains a `fetcher` prop so reasoning can inject the
SQLite-backed fetcher while Maintain keeps using the Neo4j one
- drops minimap (nav container, dep, CSS)
- legend filters + section parenting extracted for reuse
- i18n base strings (FR + EN)
ChunkWriter mirrors chunks into Neo4j after OpenSearch indexing, creating
HAS_CHUNK edges and DERIVED_FROM back-references to the source Elements
(via doc_items propagated from the local chunker).
Graph API: GET /api/documents/{id}/graph returns a cytoscape-shaped
payload with nodes + edges for Document / Element / Page / Chunk.
Hard cap at 200 pages returns HTTP 413 per design §8.4.
Frontend: new Graph tab in Studio results, rendered with Cytoscape.js +
dagre layout (lazy-loaded, ~175 KB gz). Legend, node styling per element
label, directional edges styled per edge type.
README gains a Neo4j section with the schema, three demo Cypher
queries, and env vars. Backend tests skip cleanly when the neo4j python
package is not installed locally.
Refs #186
Serialize a DoclingDocument to a Neo4j graph: Document + Page + Element
nodes with dynamic specific labels (SectionHeader, Paragraph, Table,
Figure, …), plus HAS_ROOT / PARENT_OF / NEXT / ON_PAGE edges. Replace-on-
write for idempotent re-ingestion.
The reader returns the verbatim document_json stored on the Document
node — reconstruction from graph nodes is deferred to v0.6.
Wired into AnalysisService._finalize_analysis: runs after conversion,
degrades gracefully by default, fails fast when neo4j_required is set.
Refs #186
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
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
- 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
- Conditionally mount ingestion router only when OpenSearch + embedding are configured
- Add `ingestionAvailable` field to /api/health response
- Add `ingestion` feature flag to frontend (hides Search nav, Ingest button,
OpenSearch badge, indexed badges/filters when disabled)
- Skip ingestion polling when flag is off
- Make OpenSearch + embedding optional in docker-compose via profiles
- Add docker-compose.ingestion.yml override for full-stack ingestion
- Set BATCH_PAGE_SIZE=5 default in Docker local image
- Lead Quick Start with one-liner docker run command
- Document ingestion as opt-in with dedicated section
- Add BATCH_PAGE_SIZE, MAX_FILE_SIZE_MB, MAX_PAGE_COUNT, RATE_LIMIT_RPM to config tables
- Update test counts (380 backend, 159 frontend)
- Date CHANGELOG 0.4.0, bump frontend version to 0.4.0
- Sync CONTRIBUTING.md with E2E Karate test sections
Closes#180
Frontend decoupling:
- Create shared/appConfig.ts as reactive bridge (locale, maxFileSizeMb,
maxPageCount) eliminating shared→features and feature→feature imports
- Give history feature its own Pinia store and API layer (was re-export
of analysis store)
- Give chunking feature its own Pinia store and API layer (was importing
from analysis)
- ChunkPanel receives analysis data via props instead of cross-feature
store import
- document/store reads maxFileSizeMb from shared config instead of
importing feature-flags store
- shared/i18n reads locale from shared config instead of importing
settings store
Backend:
- Add HealthResponse Pydantic schema for /api/health endpoint
Closes#140, closes#141, closes#142, closes#143
- Add DocumentRepository and AnalysisRepository protocols in domain/ports.py (#128)
- Refactor persistence repos from module functions to SqliteDocumentRepository
and SqliteAnalysisRepository classes
- Inject repos into AnalysisService and new DocumentService class via
constructor, removing direct imports of persistence and infra.settings (#129)
- Move _merge_results, _classify_error, _extract_html_body to domain/services.py (#130)
- Update main.py composition root to build and wire all dependencies
- Switch api/documents.py to Depends pattern matching api/analyses.py
- Update all tests to use injected mocks instead of module-level patches
Closes#128, closes#129, closes#130
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
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.
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.
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.
Move /health to /api/health on backend and update the frontend
feature flag store to match. Without this, the combined Docker
image nginx proxy could not reach the endpoint and feature flags
(chunking/prepare mode) failed to load.
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.
AnalysisService gains rechunk() and inline chunking during conversion.
ChunkingOptionsRequest/ChunkResponse schemas, POST rechunk endpoint,
and conditional chunker injection in main.py (local engine only).
- Remove dead get_analysis_service() function in main.py
- Scope file deletion to UPLOAD_DIR to prevent path traversal
- Parse datetime strings back to datetime objects in repos
- Add 10-minute polling timeout in frontend analysis store
- Accept .pdf extension (not just MIME type) on drag-and-drop
- Guard localStorage access for private browsing compatibility
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>
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>
