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Async on-prem LLM-powered structured information extraction microservice
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feat(deploy): setup_server.sh + deployment runbook (#28) 
Lands Task 5.2.
2026-04-18 10:17:14 +00:00
.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 feat(deploy): setup_server.sh + deployment runbook 2026-04-18 12:16:58 +02:00
scripts feat(deploy): setup_server.sh + deployment runbook 2026-04-18 12:16:58 +02:00
src/ix feat(app): production wiring — factories, pipeline, /healthz real probes 2026-04-18 12:09:11 +02:00
tests feat(app): production wiring — factories, pipeline, /healthz real probes 2026-04-18 12:09:11 +02:00
.env.example feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
.gitignore feat(docker): Dockerfile (CUDA+python3.12) + compose with GPU reservation 2026-04-18 12:15:26 +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
docker-compose.yml feat(docker): Dockerfile (CUDA+python3.12) + compose with GPU reservation 2026-04-18 12:15:26 +02:00
Dockerfile feat(docker): Dockerfile (CUDA+python3.12) + compose with GPU reservation 2026-04-18 12:15:26 +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.