Why:
- POST /v1/files was reporting chunks_indexed=0/points_created=0 unconditionally
— chunks were parsed and embedded but never written to Qdrant, so nothing
was actually searchable after upload.
Changes:
- upload_source_file() now calls index_chunks() after embedding, inside the
same INGESTION_TIMEOUT_SECONDS window, and marks the job failed
(error_code=index_failed, 502) if it raises.
- Job counters (points_created, points_soft_deleted) and the response's
chunks_indexed now reflect the real indexing result instead of a hardcoded
zero.
- Wired PointStorage through AppResources/lifespan/the files router.
Impact:
- A successful upload is now searchable in Qdrant by the time 201 returns.
Why:
- Ingested chunks need to become searchable Qdrant points before the upload
response returns, with tenant/domain isolation and a safe re-ingestion
story per ADR-0001/0017.
Changes:
- src/application/points/: index_chunks() is the sole entry point, owning
payload construction, batched/bounded-concurrency upserts
(upsert_concurrency semaphore), and a soft-delete sweep for points a
shorter re-ingestion leaves behind. The sweep runs only after every upsert
in the attempt succeeds, so a failed attempt can leave a stale prefix but
never removes content from a working index.
- PointStorage port (application/ports/) + QdrantPointStorage adapter
(infrastructure/qdrant/points.py), keeping the qdrant_client SDK out of
application code per ADR-0015.
- FakePointStorage test double for exercising the ordering/idempotency
guarantees without a real Qdrant.
Why:
- ADR-0001's Qdrant payload records embedding_model_version so a future model
swap can identify which chunks need re-embedding. The embedder is what
knows which model produced its vectors, so it reports this rather than the
call site reconstructing it from settings.
Changes:
- DenseEmbedder/SparseEmbedder protocols gain a model_version: str attribute.
- OpenAICompatibleEmbedder reports its configured model; Bm25SparseEmbedder
reports its analyzer (bm25-<analyzer>).
Why:
- Plan 001 Phase 4 needs batched, concurrency-bounded embedding wired into
the inline upload path, with process-wide capacity/timeout/chunk-limit
guards (ADR-0017).
- The BM25 analyzer and dense-model config are ported from the `emet`
evaluation lab, which benchmarked them against the real Farsi corpus
(bm25-fa-norm-stop; nomic-embed-text-v2-moe at 768-dim; text-embedding-3-large
at native 3072-dim), closing open items in ADR-0001/ADR-0005.
Changes:
- New: embedding ports, orchestration (embed_chunks), request-bounds
helpers, and dense/sparse adapters (analyzers.py, bm25.py,
openai_compatible.py).
- upload.py now parses/chunks/embeds inline behind INGESTION_MAX_CONCURRENCY
(503), INGESTION_TIMEOUT_SECONDS (504), and the chunk-count ceiling (413);
every failure path still writes a terminal job row.
- Lifespan builds and warms both dense embedders at startup (fail-soft) and
creates the sparse embedder and concurrency semaphore.
- httpx moves from dev to main dependencies (adapters use it directly).
Impact:
- Qdrant point upserts are still Phase 5 -- chunks_indexed stays 0.
- New EMBEDDING_* env vars documented in .env.example; safe defaults.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Why:
- Implements plan 001 Phase 3's upload orchestration.
Changes:
- ObjectStorage port and MinIO adapter, thread-offloaded per ADR-0017.
- Tenant-scoped repositories for api_keys, source_files, ingestion_jobs.
- upload_source_file implementing the two-transaction shape with
(tenant_id, domain, content_sha256) idempotency.
Impact:
- This phase stores bytes only -- chunks_indexed is always 0 until
Phase 4/5 add parsing/embedding.
Why:
- The package exposed 8 modules directly, pushing source-type dispatch and
the ADR-0017 thread-offload obligation onto every caller.
Changes:
- Add parse_and_chunk_document as the sole public entry point.
- Demote the individual parsers to internal/test-only.
Adds src/application/ingestion/ -- Persian normalization, DOCX body
walk with structural data/layout table classification, CSV/XLSX row
rendering, and fixed-size token chunking (cl100k_base, 400/60/512) --
as pure functions per ADR-0015, tested against real production
documents (asia_data_sample, kept out of the repo). ADR-0018 records
where this diverges from ADR-0004 (fixed-size default, no invented
headings/tree, structural table classification, header-provable
labeling only). Plan 001's scope line is corrected from CSV-only to
DOCX/XLSX/CSV, and CLAUDE.md's stale project-status paragraph is
updated to match current implementation state.
