chore(spec): sync generated client from upstream openapi.yaml#15
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May 21, 2026
) Two spec-sync PRs (#12 on 2026-05-14, #15 on 2026-05-16) landed regenerated _generated/ on main but neither bumped pyproject, so the new control-plane surface has been sitting in the source tree unreleased on PyPI for a week. This PR closes the gap: bumps to 0.1.6, ships matching ergonomic wrappers + typed fields on the hand-curated resource layer, and adds tests so the new surface is covered, not just compiled. New high-level surface on client.custom_models (sync + async): - update(model_id, *, name=..., min_replicas=..., max_replicas=..., cooldown_seconds=..., extra=...) issues PATCH /v1/{ws}/custom-models/{id}. In-place mutation of the live deployment - no rolling restart, no downtime. Empty PATCH is refused client-side with ValidationError(code="empty_update") one round-trip earlier than the server's 422; an `extra` mapping lets callers PATCH future fields without an SDK release. - supported_architectures() returns a typed SupportedArchitectures catalog from GET /v1/{ws}/custom-models/supported-architectures. Each ArchitectureInfo carries the capability tags (tool_calling, vision, image_input, video_input, streaming, json_mode) the architecture exposes. Intended for driving UI architecture/ capability filters before calling validate(). - create(..., base_model_id=...) wires up the LoRA-import hint. Required on weight_source=s3_* to classify the bundle as an adapter at create-time; optional on weight_source=huggingface where it overrides adapter_config.json::base_model_name_or_path from the upstream repo (useful when the recorded base id isn't a valid HF id, e.g. a local filesystem path used during training). - validate(..., model_size_gb=...) lets callers skip the HF head-bytes probe by supplying a weight-size hint, useful for very large models (405B-class) where the probe stalls validate. Typed LoRA fields on the existing Pydantic types: - CustomModel: artifact_type ("base"|"lora"|None), base_model_id, lora_adapter_name, lora_rank. artifact_type is None on responses from control planes that predate the LoRA work - treat that as "base" for compatibility. - ValidateModelResponse: artifact_type (defaults to "base" on fresh responses, None on legacy), detected_base_model_id, lora_rank. When artifact_type == "lora", the architectures / num_params / estimated_memory_gb / max_context_length fields describe the base model resolved from adapter_config.json, not the adapter itself. New public exports: ArchitectureInfo, SupportedArchitectures, ArtifactType from graphn (and graphn.custom_models). CustomModelCreate.huggingface_model_id is now required on the generated attrs dataclass (was str | Unset). The server has returned 422 for omitted huggingface_model_id on every weight source since 0.1.3 (voltagepark/takao#1997) and the hand-curated client.custom_models.create resource raises ValidationError client-side for S3 imports, so this is the generated type catching up - callers using the keyword-only ergonomic API are unaffected. Tests: 57 pass (43 existing + 14 new) covering both transports. ruff check clean. mypy is clean on every file this PR touches (pre-existing no-any-return errors in _transport.py and tts.py are on main and not regressions). The auto-tag job's CHANGELOG check matches "## [0.1.6] - 2026-05-21" so PyPI publish fires automatically on merge.
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Automated nightly spec sync.
src/graphn/_generated/fromhttps://cp.graphn.ai/openapi.yaml.Review the diff for any breaking field renames before merging.