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Async on-prem LLM-powered structured information extraction microservice
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feat(pipeline): ReliabilityStep — writes reliability flags (spec §6) 
Thin wrapper around ix.provenance.apply_reliability_flags. Validate
skips entirely when include_provenance is off OR when no provenance
data was built (text-only request, etc.). Process reads
context.texts + context.use_case_response and lets the verifier mutate
the FieldProvenance entries + fill quality_metrics counters in place.

11 unit tests in tests/unit/test_reliability_step.py cover: validate
skips on flag off / missing provenance, runs otherwise; per-type
flag behaviour (string verified + text_agreement, Literal -> None,
None value -> None, short numeric -> text_agreement None, date with
both sides parsed, IBAN whitespace-insensitive, disagreement -> False);
quality_metrics verified_fields / text_agreement_fields counters.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 11:20:18 +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
docs Implementation plan for ix MVP 2026-04-18 10:34:30 +02:00
src/ix feat(pipeline): ReliabilityStep — writes reliability flags (spec §6) 2026-04-18 11:20:18 +02:00
tests feat(pipeline): ReliabilityStep — writes reliability flags (spec §6) 2026-04-18 11:20:18 +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
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.