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Async on-prem LLM-powered structured information extraction microservice
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feat(rest): FastAPI adapter + /jobs, /healthz, /metrics routes (spec §5) 
Routes:
- POST /jobs: 201 on first insert, 200 on idempotent re-submit.
- GET /jobs/{id}: full Job envelope or 404.
- GET /jobs?client_id=&request_id=: correlation lookup or 404.
- GET /healthz: {postgres, ollama, ocr}; 200 iff all ok (degraded counts
  as non-200 per spec). Postgres probe guarded by a 2 s wait_for.
- GET /metrics: pending/running counts + 24h done/error counters +
  per-use-case avg seconds. Plain JSON, no Prometheus.

create_app(spawn_worker=bool) parameterises worker spawning so tests that
only need REST pass False. Worker spawn is tolerant of the loop module not
being importable yet (Task 3.5 fills it in).

Probes are a DI bundle — production wiring swaps them in at startup
(Chunk 4); tests inject canned ok/fail callables. Session factory is also
DI'd so tests can point at a per-loop engine and sidestep the async-pg
cross-loop future issue that bit the jobs_repo fixture.

9 new integration tests; unit suite unchanged. Forgejo Actions trigger is
flaky; local verification is the gate (unit + integration green locally).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 11:47:04 +02:00
.forgejo/workflows ci: run on every push (not just main) so feat branches also get CI 2026-04-18 10:40:44 +02:00
alembic feat(store): Alembic scaffolding + initial ix_jobs migration (spec §4) 2026-04-18 11:37:21 +02:00
docs Implementation plan for ix MVP 2026-04-18 10:34:30 +02:00
scripts test(pipeline): end-to-end hermetic test with fakes + synthetic fixture 2026-04-18 11:24:29 +02:00
src/ix feat(rest): FastAPI adapter + /jobs, /healthz, /metrics routes (spec §5) 2026-04-18 11:47:04 +02:00
tests feat(rest): FastAPI adapter + /jobs, /healthz, /metrics routes (spec §5) 2026-04-18 11:47:04 +02:00
.env.example feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
.gitignore feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
.python-version feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
AGENTS.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
alembic.ini feat(store): Alembic scaffolding + initial ix_jobs migration (spec §4) 2026-04-18 11:37:21 +02:00
pyproject.toml feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
README.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
uv.lock feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00

README.md

InfoXtractor (ix)

Async, on-prem, LLM-powered structured information extraction microservice.

Given a document (PDF, image, text) and a named use case, ix returns a structured JSON result whose shape matches the use-case schema — together with per-field provenance (OCR segment IDs, bounding boxes, cross-OCR agreement flags) that let the caller decide how much to trust each extracted value.

Status: design phase. Implementation about to start.

Principles

  • On-prem always. LLM = Ollama, OCR = local engines (Surya first). No OpenAI / Anthropic / Azure / AWS / cloud.
  • Grounded extraction, not DB truth. ix returns best-effort fields + provenance; the caller decides what to trust.
  • Transport-agnostic pipeline core. REST + Postgres-queue adapters in parallel on one job store.