The data and domain layers for the chunks editor (#219-224 in 0.6.0).
Chunks were previously stored as a JSON blob in analysis_jobs.chunks_json;
this commit makes them first-class persisted entities with stable IDs,
soft-delete, and an immutable audit log.
Domain
- Chunk: persistent entity with id, document_id, sequence, text,
headings, source_page, bboxes, doc_items, token_count, timestamps,
deleted_at (soft delete)
- ChunkEdit: immutable audit row (action, actor, at, before, after,
parents, children, reason)
- ChunkPush: snapshot of which chunk_ids landed in which store at push
- ChunkEditAction enum: insert/update/delete/merge/split
- domain/chunk_editing.py: pure operations on a chunkset (insert,
update, delete, merge, split). Each returns a new chunkset and the
affected chunk(s); errors raise ChunkEditingError.
Persistence
- Three new tables: chunks, chunk_edits, chunk_pushes (FK + indexes)
- SqliteChunkRepository (insert, insert_many, update, soft_delete,
find_for_document, find_by_id; respects deleted_at)
- SqliteChunkEditRepository (append-only audit log; paginated reads
ordered newest-first; per-chunk history)
- SqliteChunkPushRepository (per-(doc, store) latest snapshot)
Ports
- ChunkRepository, ChunkEditRepository, ChunkPushRepository protocols
added to domain/ports.py
Tests
- 17 tests for the pure chunk-editing operations covering insert /
update / delete / merge / split, sequence shifts, lineage, error
paths (out-of-range, missing id, deleted target, cross-document)
- 11 tests for the three repositories: round-trips, soft-delete
filtering, history ordering, lineage round-trip, cascade-delete with
document, find_latest semantics
Service orchestration (ChunkEditingService — atomic chunk + audit
write) and the API endpoints land in a follow-up commit on the same
feature branch / next release. The data + domain foundation here is
what unblocks #219-224.
Refs #205
Introduces the data layer for multi-store ingestion. Documents can now
live in multiple stores, each with its own Ingested/Stale/Failed state.
The doc-level lifecycle (#202) becomes the aggregate over all per-store
links, computed by a pure domain function.
Domain
- Store entity (name, slug, kind, embedder, config, is_default)
- DocumentStoreLink entity with mark_ingested / mark_stale / mark_failed
helpers
- StoreKind and DocumentStoreLinkState enums
- aggregate_lifecycle(): pure function — Failed > Stale > Ingested
> fallback (the doc's pre-link Uploaded/Parsed/Chunked state)
Persistence
- New tables 'stores' and 'document_store_links' with the right indexes
(doc_id, store_id, state) and a UNIQUE (doc, store) on the link
- Default 'opensearch' store seeded idempotently in init_db, embedder
pulled from DEFAULT_EMBEDDER (fallback bge-m3)
- SqliteStoreRepository (find_by_slug, find_by_id, get_default, …)
- SqliteDocumentStoreLinkRepository with ON CONFLICT … DO UPDATE upsert
Ports
- StoreRepository and DocumentStoreLinkRepository protocols added
Tests
- 14 new tests: seed idempotency, insert/find round-trips, UNIQUE
constraint, cascade delete with the document, every link state
round-trips, aggregation rule with all branches
Refs #203
Adds a first-class lifecycle state to every document, distinct from
AnalysisJob.status. The lifecycle describes the document as a whole and
is the foundation for the doc-centric pivot in 0.6.0.
Domain
- DocumentLifecycleState enum (Uploaded/Parsed/Chunked/Ingested/Stale/Failed)
- Document.lifecycle_state and lifecycle_state_at fields
- Document.transition_to() validates against a transition table
(domain/lifecycle.py) and returns a DocumentLifecycleChanged event
- InvalidLifecycleTransitionError on disallowed transitions
Persistence
- ALTER TABLE documents to add the two columns (default 'Uploaded')
- New index idx_documents_lifecycle_state for filter perf
- _COLUMN_MIGRATIONS refactored to support multiple tables
- _POST_MIGRATION_DDL list for indexes on freshly-added columns
- SqliteDocumentRepository.update_lifecycle()
Services
- AnalysisService drives transitions on parse / chunk / re-chunk / fail
via _transition_document(); idempotent and resilient (logs WARN and
continues if a stale state is somehow encountered)
API
- DocumentResponse exposes lifecycleState + lifecycleStateAt
(additive — existing 'status' field kept for backwards compat)
Frontend
- Document type extended with lifecycleState and lifecycleStateAt
- DocumentLifecycleState union literal mirroring the backend enum
Tests
- 24 new tests in test_lifecycle.py covering transitions, idempotency,
invariant preservation, and event emission
- test_repos.py: round-trip + every-enum-value check + update_lifecycle
- test_chunking.py: rechunk path now mocks document_repo correctly
Refs #202
- 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
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
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>
