SurfSense/surfsense_backend/app/automations/schemas/definition/metadata.py

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feat(automation): add Pydantic schemas for the automation definition Three layers of Pydantic models under app/automations/schemas/, one file per concern (SRP), matching the envelope in automation-design-plan.md §5. definition/ — the editable envelope persisted in automations.definition: - envelope.py AutomationDefinition (top-level shape) - plan_step.py PlanStep (one step in the sequential plan) - inputs.py InputsBlock (the inputs JSON Schema wrapper) - execution.py ExecutionBlock (timeouts, retries, concurrency, budget cap, on_failure plan) - metadata.py MetadataBlock (tags + created_from_nl + extras) - trigger_spec.py TriggerSpec (one entry in triggers[]) triggers/ — per-trigger config schemas, dispatched by registry on the TriggerSpec.type discriminator: - schedule.py ScheduleTriggerConfig(cron, timezone) - manual.py ManualTriggerConfig() — empty in v1 actions/ — per-action config schemas, dispatched by registry on the PlanStep.action discriminator: - agent_task.py AgentTaskActionConfig(prompt, tools, model, output_schema) Design properties verified by an inline smoke test: - The §5 worked example round-trips through model_validate_json / model_dump_json byte-for-byte (InputsBlock uses serialize_by_alias so the JSON key stays "schema" not "schema_"). - Envelope rejects unknown top-level keys (extra="forbid"). - MetadataBlock tolerates unknown keys (extra="allow"). - ExecutionBlock defaults apply when the block is omitted. - retry_backoff and concurrency are typed as Literal — bogus values rejected at validation time. - Per-type configs enforce their required fields (cron + timezone on schedule; non-empty prompt on agent_task). The envelope keeps trigger and action configs as untyped dicts on purpose — per-type validation is a registry-driven dispatch (commit 10), keeping the envelope free of every-type-knows-every-type coupling.
2026-05-26 22:50:52 +02:00
"""``MetadataBlock`` — the ``metadata`` section of the automation definition."""
from __future__ import annotations
from pydantic import BaseModel, ConfigDict, Field
class MetadataBlock(BaseModel):
"""Free-form metadata attached to the automation definition.
Unlike the rest of the envelope this block tolerates unknown keys
(``extra='allow'``) it's a deliberate extension point for
UI annotations, NL-generator breadcrumbs, custom tags, etc.
Two fields are first-class so the rest of the system can rely on
them without reaching into the loose extras:
``tags`` used by the UI for filtering and grouping.
``created_from_nl`` set by the NL generator so we can later
measure how many runs came from natural-language authoring.
"""
model_config = ConfigDict(extra="allow")
tags: list[str] = Field(
default_factory=list,
description="UI-facing tags. No semantic meaning to the engine.",
)
created_from_nl: bool = Field(
default=False,
description=(
"True when the definition was produced by the NL "
"generator (set automatically by the generator path; "
"human-authored definitions keep this false)."
),
)