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feat(ui): queue position, elapsed time, filename, CPU-mode notice (#46 )

Co-authored-by: Dirk Riemann <ditori@gmail.com>
Co-committed-by: Dirk Riemann <ditori@gmail.com>

2026-04-18 20:06:35 +00:00

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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 (REST API + browser UI at /ui).

Web UI

A minimal browser UI lives at http://192.168.68.42:8994/ui: drop a PDF, pick a registered use case or define one inline, submit, see the pretty-printed result. HTMX polls the job status every 2 s until the pipeline finishes. LAN-only, no auth.

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).

Ad-hoc use cases

For one-offs where a registered use case doesn't exist yet, ship the schema inline:

{
  "use_case": "adhoc-invoice",        // free-form label (logs/metrics only)
  "use_case_inline": {
    "use_case_name": "Invoice totals",
    "system_prompt": "Extract vendor and total amount.",
    "fields": [
      {"name": "vendor", "type": "str", "required": true},
      {"name": "total",  "type": "decimal"},
      {"name": "currency", "type": "str", "choices": ["USD", "EUR", "CHF"]}
    ]
  },
  // ...ix_client_id, request_id, context...
}

When use_case_inline is set, the pipeline builds the response schema on the fly and skips the registry. Supported types: str, int, float, decimal, date, datetime, bool. choices is only allowed on str fields. Precedence: inline wins over use_case when both are present.

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

UI queue + progress UX

The /ui job page polls GET /ui/jobs/{id}/fragment every 2 s and surfaces:

Queue position while pending: "Queue position: N ahead — M jobs total in flight (single worker)" so it's obvious a new submission is waiting on an earlier job rather than stuck. "About to start" when the worker has just freed up.
Elapsed time while running ("Running for MM:SS") and on finish ("Finished in MM:SS").
Original filename — the UI stashes the client-provided upload name in FileRef.display_name so the browser shows your_statement.pdf instead of the on-disk UUID.
CPU-mode notice when /healthz reports ocr_gpu: false (the Surya OCR client observed torch.cuda.is_available() == False): a collapsed <details> pointing at the deployment runbook.

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.

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