Async on-prem LLM-powered structured information extraction microservice
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| .forgejo/workflows | ||
| alembic | ||
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| scripts | ||
| src/ix | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| AGENTS.md | ||
| alembic.ini | ||
| docker-compose.yml | ||
| Dockerfile | ||
| pyproject.toml | ||
| README.md | ||
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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: MVP deployed. Live on the home LAN at http://192.168.68.42:8994.
- Full reference spec:
docs/spec-core-pipeline.md(aspirational; MVP is a strict subset) - MVP design:
docs/superpowers/specs/2026-04-18-ix-mvp-design.md - Implementation plan:
docs/superpowers/plans/2026-04-18-ix-mvp-implementation.md - Deployment runbook:
docs/deployment.md - Agent / development notes:
AGENTS.md
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.
Submitting a job
curl -X POST http://192.168.68.42:8994/jobs \
-H "Content-Type: application/json" \
-d '{
"use_case": "bank_statement_header",
"ix_client_id": "mammon",
"request_id": "some-correlation-id",
"context": {
"files": [{
"url": "http://paperless.local/api/documents/42/download/",
"headers": {"Authorization": "Token …"}
}],
"texts": ["<Paperless Tesseract OCR content>"]
}
}'
# → {"job_id":"…","ix_id":"…","status":"pending"}
Poll GET /jobs/{job_id} until status is done or error. Optionally pass callback_url to receive a webhook on completion (one-shot, no retry; polling stays authoritative).
Full REST surface + provenance response shape documented in the MVP design spec.
Running locally
uv sync --extra dev
uv run pytest tests/unit -v # hermetic unit + integration suite
IX_TEST_OLLAMA=1 uv run pytest tests/live -v # needs LAN access to Ollama + GPU
Deploying
git push server main # rebuilds Docker image, restarts container, /healthz deploy gate
python scripts/e2e_smoke.py # E2E acceptance against the live service
See docs/deployment.md for full runbook + rollback.