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Async on-prem LLM-powered structured information extraction microservice
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feat(ocr): SuryaOCRClient — real OCR backend (spec §6.2) 
Runs Surya's detection + recognition over PIL images rendered from each
Page's source file (PDFs via PyMuPDF, images via Pillow). Lazy warm_up
so FastAPI lifespan start stays predictable. Deferred Surya/torch
imports keep the base install slim — the heavy deps stay under [ocr].

Extends OCRClient Protocol with optional files + page_metadata kwargs
so the engine can resolve each page back to its on-disk source; Fake
accepts-and-ignores to keep hermetic tests unchanged.

selfcheck() runs the predictors on a 1x1 PIL image — wired into /healthz
by Task 4.3.

Tests: 6 hermetic unit tests (Surya predictors mocked, no model
download); 2 live tests gated on IX_TEST_OLLAMA=1 (never run in CI).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 12:04:19 +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(ocr): SuryaOCRClient — real OCR backend (spec §6.2) 2026-04-18 12:04:19 +02:00
tests feat(ocr): SuryaOCRClient — real OCR backend (spec §6.2) 2026-04-18 12:04:19 +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.