chore: MVP deployed (#42)

First deploy done 2026-04-18. E2E extraction of the bank_statement_header
use case completes in 35 s against the live service, with 7 of 9 header
fields provenance-verified + text-agreement-green. closing_balance
asserts from spec §12 all pass.

Updates:
- README.md: status -> "MVP deployed"; worked example curl snippet;
  pointers to deployment runbook + spec + plan.
- AGENTS.md: status line updated with the live URL + date.
- pyproject.toml: version comment referencing the first deploy.
- docs/deployment.md: "First deploy" section filled in with times,
  field-level extraction result, plus a log of every small Docker/ops
  follow-up PR that had to land to make the first deploy healthy.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

AGENTS.md

@@ -4,7 +4,7 @@ Async, on-prem, LLM-powered structured information extraction microservice. Give
 
 Designed to be used by other on-prem services (e.g. mammon) as a reliable fallback / second opinion for format-specific deterministic parsers.
 
-Status: design phase. Full reference spec at `docs/spec-core-pipeline.md`. MVP spec will live at `docs/superpowers/specs/`.
+Status: MVP deployed (2026-04-18) at `http://192.168.68.42:8994` — LAN only. Full reference spec at `docs/spec-core-pipeline.md`; MVP spec at `docs/superpowers/specs/2026-04-18-ix-mvp-design.md`; deploy runbook at `docs/deployment.md`.
 
 ## Guiding Principles
 

README.md

@@ -4,10 +4,12 @@ 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.
+**Status:** MVP deployed. Live on the home LAN at `http://192.168.68.42:8994`.
 
 - Full reference spec: [`docs/spec-core-pipeline.md`](docs/spec-core-pipeline.md) (aspirational; MVP is a strict subset)
 - **MVP design:** [`docs/superpowers/specs/2026-04-18-ix-mvp-design.md`](docs/superpowers/specs/2026-04-18-ix-mvp-design.md)
+- **Implementation plan:** [`docs/superpowers/plans/2026-04-18-ix-mvp-implementation.md`](docs/superpowers/plans/2026-04-18-ix-mvp-implementation.md)
+- **Deployment runbook:** [`docs/deployment.md`](docs/deployment.md)
 - Agent / development notes: [`AGENTS.md`](AGENTS.md)
 
 ## Principles
@@ -15,3 +17,44 @@ Given a document (PDF, image, text) and a named *use case*, ix returns a structu
 - **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
+
+```bash
+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
+
+```bash
+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
+
+```bash
+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`](docs/deployment.md) for full runbook + rollback.

docs/deployment.md

@@ -71,13 +71,30 @@ git push server main
 
 ## First deploy
 
-_(fill in after running — timestamps, commit sha, e2e_smoke output)_
+- **Date:** 2026-04-18
+- **Commit:** `fix/ollama-extract-json` (#36, the last of several Docker/ops follow-ups after PR #27 shipped the initial Dockerfile)
+- **`/healthz`:** all three probes (`postgres`, `ollama`, `ocr`) green. First-pass took ~7 min for the fresh container because Surya's recognition (1.34 GB) + detection (73 MB) models download from HuggingFace on first run; subsequent rebuilds reuse the named volumes declared in `docker-compose.yml` and come up in <30 s.
+- **E2E extraction:** `bank_statement_header` against `tests/fixtures/synthetic_giro.pdf` with Paperless-style texts:
+  - Pipeline completes in **35 s**.
+  - Extracted: `bank_name=DKB`, `account_iban=DE89370400440532013000`, `currency=EUR`, `opening_balance=1234.56`, `closing_balance=1450.22`, `statement_date=2026-03-31`, `statement_period_end=2026-03-31`, `statement_period_start=2026-03-01`, `account_type=null`.
+  - Provenance: 8 / 9 leaf fields have sources; 7 / 8 `provenance_verified` and `text_agreement` are True. `statement_period_start` shows up in the OCR but normalisation fails (dateutil picks a different interpretation of the cited day); to be chased in a follow-up.
 
-- **Date:** TBD
-- **Commit:** TBD
-- **`/healthz` first-ok time:** TBD
-- **`e2e_smoke.py` status:** TBD
-- **Notes:** —
+### Docker-ops follow-ups that landed during the first deploy
+
+All small, each merged as its own PR. In commit order after the scaffold (#27):
+
+- **#31** `fix(docker): uv via standalone installer` — Python 3.12 on Ubuntu 22.04 drops `distutils`; Ubuntu's pip needed it. Switched to the `uv` standalone installer, which has no pip dependency.
+- **#32** `fix(docker): include README.md in the uv sync COPY` — `hatchling` validates the readme file exists when resolving the editable project install.
+- **#33** `fix(compose): drop runtime: nvidia` — the deploy host's Docker daemon doesn't register a named `nvidia` runtime; `deploy.resources.devices` is sufficient and matches immich-ml.
+- **#34** `fix(deploy): network_mode: host` — `postgis` is bound to `127.0.0.1` on the host (security hardening T12). `host.docker.internal` points at the bridge gateway, not loopback, so the container couldn't reach postgis. Goldstein uses the same pattern.
+- **#35** `fix(deps): pin surya-ocr ^0.17` — earlier cu124 torch pin had forced surya to 0.14.1, which breaks our `surya.foundation` import and needs a transformers version that lacks `QuantizedCacheConfig`.
+- **#36** `fix(genai): drop Ollama format flag; extract trailing JSON` — Ollama 0.11.8 segfaults on Pydantic JSON Schemas (`$ref`, `anyOf`, `pattern`), and `format="json"` terminates reasoning models (qwen3) at `{}` because their `<think>…</think>` chain-of-thought isn't valid JSON. Omit the flag, inject the schema into the system prompt, extract the outermost `{…}` balanced block from the response.
+- **volumes** — named `ix_surya_cache` + `ix_hf_cache` mount `/root/.cache/datalab` + `/root/.cache/huggingface` so rebuilds don't re-download ~1.5 GB of model weights.
+
+Production notes:
+
+- `IX_DEFAULT_MODEL=qwen3:14b` (already pulled on the host). Spec listed `gpt-oss:20b` as a concrete example; swapped to keep the deploy on-prem without an extra `ollama pull`.
+- Torch 2.11 default cu13 wheels fall back to CPU against the host's CUDA 12.4 driver — Surya runs on CPU. Expected inference times: seconds per page. Upgrading the NVIDIA driver (or pinning a cu12-compatible torch wheel newer than 2.7) will unlock GPU with no code changes.
 
 ## E2E smoke test (`scripts/e2e_smoke.py`)
 

pyproject.toml

@@ -1,6 +1,8 @@
 [project]
 name = "infoxtractor"
 version = "0.1.0"
+# Released 2026-04-18 with the first live deploy of the MVP. See
+# docs/deployment.md §"First deploy" for the commit + /healthz times.
 description = "Async on-prem LLM-powered structured information extraction microservice"
 readme = "README.md"
 requires-python = ">=3.12"