Async on-prem LLM-powered structured information extraction microservice
Dirk Riemann
406a7ea2fd
Worker:
- Startup: sweep_orphans(now, max_running_seconds) rescues rows stuck
in 'running' from a crashed prior process.
- Loop: claim_next_pending → build pipeline via injected factory → run
→ mark_done/mark_error → deliver callback if set → record outcome.
- Non-IX exceptions from the pipeline collapse to IX_002_000 so callers
see a stable error code.
- Sleep loop uses a cancellable wait so the stop event reacts
immediately; the wait_for_work hook is ready for Task 3.6 to plug in
the LISTEN-driven event without the worker knowing about NOTIFY.
Callback:
- One-shot POST, 2xx → delivered, anything else (incl. connect/timeout
exceptions) → failed. No retries.
- Callback record never reverts the job's terminal state — GET /jobs/{id}
stays the authoritative fallback.
7 integration tests: happy path, pipeline-raise → error, callback 2xx,
callback 5xx, orphan sweep on startup, no-callback rows stay
callback_status=None (x2 via parametrize). Unit suite still 209.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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|---|---|---|
| .forgejo/workflows | ||
| alembic | ||
| docs | ||
| scripts | ||
| src/ix | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| AGENTS.md | ||
| alembic.ini | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
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.
- Full reference spec:
docs/spec-core-pipeline.md(aspirational; MVP is a strict subset) - MVP design:
docs/superpowers/specs/2026-04-18-ix-mvp-design.md - Agent / development notes:
AGENTS.md
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.