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Async on-prem LLM-powered structured information extraction microservice
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feat(contracts): RequestIX + Context + Options per spec §3 
Adds the incoming-request data contracts as Pydantic v2 models. Matches the
MVP spec §3 exactly — fields dropped from the reference spec (use_vision,
reasoning_effort, version, ...) stay out, and `extra="forbid"` catches any
caller that sends them so drift surfaces immediately instead of silently.

Context.files is `list[str | FileRef]`: plain URLs stay str, dict entries
parse as FileRef. This keeps the common case (public URL) one-liner while
still supporting Paperless-style auth headers and per-file size caps.

ix_id stays optional with a docstring warning that callers MUST NOT set it —
the transport layer assigns the 16-char hex handle on insert. The field is
present so `Job` round-trips out of the store.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 10:47:31 +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(contracts): RequestIX + Context + Options per spec §3 2026-04-18 10:47:31 +02:00
tests feat(contracts): RequestIX + Context + Options per spec §3 2026-04-18 10:47:31 +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.