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Async on-prem LLM-powered structured information extraction microservice
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test(pipeline): end-to-end hermetic test with fakes + synthetic fixture 
Wires the five pipeline steps together with FakeOCRClient +
FakeGenAIClient, feeds the committed synthetic_giro.pdf fixture via
file:// URL, and asserts the full response shape.

- scripts/create_fixture_pdf.py: PyMuPDF-based builder. One-page A4 PDF
  with six known header strings (bank name, IBAN, period, balances,
  statement date). Re-runnable on demand; the committed PDF is what CI
  consumes.
- tests/fixtures/synthetic_giro.pdf: committed output.
- tests/unit/test_pipeline_end_to_end.py: 5 tests covering
  * ix_result.result fields populated from the fake LLM
  * provenance.fields["result.closing_balance"].provenance_verified True
  * text_agreement True when Paperless-style texts match the value
  * metadata.timings has one entry per step in the right order
  * response.error is None and context is not serialised

197 tests total; ruff clean. No integration tests, no real clients,
no network.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 11:24:29 +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
scripts test(pipeline): end-to-end hermetic test with fakes + synthetic fixture 2026-04-18 11:24:29 +02:00
src/ix feat(pipeline): ResponseHandlerStep — shape-up final payload (spec §8) 2026-04-18 11:21:36 +02:00
tests test(pipeline): end-to-end hermetic test with fakes + synthetic fixture 2026-04-18 11:24:29 +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.