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The SliceModal forces the user to pick four slots every time (printer / process / filament(s) / bed type). For fleet production that's tedious and error-prone. Pipelines let an operator save a named bundle and apply it with one click on the next file. PR A is bundle-and-management only. PR B adds single-target dispatch, PR C adds multi-copy batch with capability-matched fanout. Future-PR columns (target_kind / target_printer_id / target_model_class / fanout_strategy) ship in this migration so PR B+ is code-only, not a schema bump. Backend - New model SlicerPipeline + slicer_pipelines table; soft-delete via is_deleted so PR B+ run history can still resolve metadata. - Pydantic schemas reuse the existing PresetRef shape from schemas/slicer.py. - CRUD routes at /api/v1/slicer-pipelines/ — list (newest first by id DESC), create (201), get-by-id, partial PUT, soft-delete (204). - Three new permissions: PIPELINES_READ / PIPELINES_WRITE / PIPELINES_RUN. Administrators + Operators get all three; Viewers get READ. Backfill in seed_default_groups() so existing installs upgrade cleanly. All three denied to API keys for now. Frontend - Settings → Workflow splits into two horizontal sub-tabs mirroring the Authentication tab pattern: "Queue & Dispatch" (existing Workflow content) and "Pipelines" (new). URL deep-link via ?tab=queue&sub=pipelines. - SlicerPipelinesPanel — list, inline rename, delete, stale-preset warning when a referenced preset no longer resolves. - SliceModal gets "Apply pipeline ▾" + "Save as pipeline". Apply fills all four slot states; the filament list right-pads from current state so a pipeline with fewer entries than the current source's slot count keeps the existing tail.
62 lines
3.1 KiB
Python
62 lines
3.1 KiB
Python
"""Model for a Slicing/Printing Pipeline definition (#1425).
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A pipeline bundles the four slot picks a user normally makes in the SliceModal
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(printer / process / filament(s) / bed type) under a named, reusable preset.
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This is PR A — bundle definitions only. Run state and dispatch live in
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``pipeline_runs`` / ``pipeline_jobs`` (PR B + PR C).
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The target_* and fanout_strategy columns are materialised now to avoid a
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second migration when PR B / PR C land; PR A's API accepts the defaults and
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the UI doesn't expose them yet.
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"""
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from datetime import datetime
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from sqlalchemy import Boolean, DateTime, ForeignKey, Integer, String, Text, func
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from sqlalchemy.orm import Mapped, mapped_column
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from backend.app.core.database import Base
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class SlicerPipeline(Base):
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"""A named slicer preset bundle (printer + process + filament[s] + bed)."""
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__tablename__ = "slicer_pipelines"
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id: Mapped[int] = mapped_column(primary_key=True)
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name: Mapped[str] = mapped_column(String(200))
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description: Mapped[str | None] = mapped_column(String(1000))
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# Preset slots. ``*_source`` mirrors PresetRef.source semantics
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# (orca_cloud / cloud / local / standard); ``*_id`` is the opaque
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# source-specific id the slicer pipeline uses to resolve content.
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printer_preset_source: Mapped[str] = mapped_column(String(20))
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printer_preset_id: Mapped[str] = mapped_column(String(200))
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process_preset_source: Mapped[str] = mapped_column(String(20))
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process_preset_id: Mapped[str] = mapped_column(String(200))
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# JSON array of {"source": ..., "id": ...} entries — one per AMS slot the
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# source plate is expected to use. Stored as JSON text per Bambuddy's
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# convention (see LocalPreset.compatible_printers).
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filament_presets_json: Mapped[str] = mapped_column(Text)
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bed_type: Mapped[str | None] = mapped_column(String(64))
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# Target — PR B+ wiring; PR A treats every pipeline as a bundle without
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# an active target. Kept materialised so PR B is code-only, not a
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# migration. ``target_kind`` ∈ {"specific_printer", "printer_class"}.
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target_kind: Mapped[str] = mapped_column(String(20), default="printer_class")
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target_printer_id: Mapped[int | None] = mapped_column(Integer, ForeignKey("printers.id", ondelete="SET NULL"))
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target_model_class: Mapped[str | None] = mapped_column(String(20))
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# Fanout strategy for PR C multi-copy runs. PR A defaults it; the UI
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# doesn't expose it yet. Values: max_parallel / fill_one_first / round_robin.
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fanout_strategy: Mapped[str] = mapped_column(String(20), default="max_parallel")
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# Audit fields. created_by is nullable so pipelines survive user deletes
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# and so installs without auth enabled (current_user is None) still work.
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created_by: Mapped[int | None] = mapped_column(Integer, ForeignKey("users.id", ondelete="SET NULL"))
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is_deleted: Mapped[bool] = mapped_column(Boolean, default=False, server_default="0")
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created_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())
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updated_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now(), onupdate=func.now())
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