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Async on-prem LLM-powered structured information extraction microservice
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Dirk Riemann 86538ee8de Implementation plan for ix MVP 
Detailed, TDD-structured plan with 5 chunks covering ~30 feature-branch
tasks from foundation scaffolding through first live deploy + E2E smoke.
Each task is one PR; pipeline core comes hermetic-first, real Surya/Ollama
clients in Chunk 4, containerization + first deploy in Chunk 5.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 10:34:30 +02:00
docs Implementation plan for ix MVP 2026-04-18 10:34:30 +02:00
.gitignore Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
AGENTS.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
README.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +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.