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Async on-prem LLM-powered structured information extraction microservice
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fix(genai): inject JSON schema into Ollama system prompt 
format=json loose mode gives valid JSON but no shape — models default
to emitting {} when the system prompt doesn't list fields. Prepend a
schema-guidance system message with the full Pydantic schema (after
the existing null-branch sanitiser) so the model sees exactly what
shape to produce. Pydantic still validates on parse.

Unit tests updated to check the schema message is prepended without
disturbing the caller's own messages.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 14:02:25 +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 fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
scripts chore(model): switch default IX_DEFAULT_MODEL to qwen3:14b (already on host) 2026-04-18 12:20:23 +02:00
src/ix fix(genai): inject JSON schema into Ollama system prompt 2026-04-18 14:02:25 +02:00
tests fix(genai): inject JSON schema into Ollama system prompt 2026-04-18 14:02:25 +02:00
.env.example fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +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 chore(model): switch default IX_DEFAULT_MODEL to qwen3:14b (already on host) 2026-04-18 12:20:23 +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 fix(compose): persist Surya + HF caches so rebuilds don't redownload models 2026-04-18 13:49:09 +02:00
Dockerfile fix(docker): include README.md in the uv sync COPY so hatchling finds it 2026-04-18 12:42:29 +02:00
pyproject.toml fix(deps): pin surya-ocr ^0.17 and drop cu124 index 2026-04-18 13:21:40 +02:00
README.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
uv.lock fix(deps): pin surya-ocr ^0.17 and drop cu124 index 2026-04-18 13:21:40 +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.