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Async on-prem LLM-powered structured information extraction microservice
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feat(use_cases): registry + bank_statement_header (spec §7) 
First use case lands. The schema is intentionally flat — nine scalar fields,
no nested arrays — because Ollama's structured-output guidance stays most
reliable when the top level has only scalars, and every field we care about
(bank_name, IBAN, period, opening/closing balance) can be rendered as one.

Registration is explicit in `use_cases/__init__.py`, not a side effect of
importing the use-case module. That keeps load order obvious and lets tests
patch the registry without having to reload modules.

`get_use_case(name)` is the one-liner adapters use; it raises
`IX_001_001` with the offending name in `detail` when the lookup misses,
which keeps log-scrape simple.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 10:51:43 +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(use_cases): registry + bank_statement_header (spec §7) 2026-04-18 10:51:43 +02:00
tests feat(use_cases): registry + bank_statement_header (spec §7) 2026-04-18 10:51:43 +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.