Async on-prem LLM-powered structured information extraction microservice
Dirk Riemann
ae595c937a
Adds the single exception type used throughout the pipeline. Every failure maps to one of the ten IX_* codes from the MVP spec §8 with a stable machine-readable code and an optional free-form detail. The `str()` form is log-scrapable with a single regex (`IX_xxx_xxx: <msg> (detail=...)`), so mammon-side reliability UX can classify failures without brittle string parsing. Enum values equal names so callers can serialise either. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
|---|---|---|
| .forgejo/workflows | ||
| docs | ||
| src/ix | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| AGENTS.md | ||
| 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.