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be4d43d6c9
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.
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""``ExecutionBlock`` — the ``execution`` section of the automation definition."""
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from __future__ import annotations
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field
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from .plan_step import PlanStep
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class ExecutionBlock(BaseModel):
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"""The ``execution`` block of an ``AutomationDefinition``.
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Carries automation-wide defaults that individual ``PlanStep``s
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can override. Every field has a sane default so an automation
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definition may omit the block entirely; in that case all defaults
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apply.
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``on_failure`` is a secondary plan that runs only when the main
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``plan`` fails after retries exhaust. It uses the same
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``PlanStep`` shape as the main plan and shares the same execution
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semantics.
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"""
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model_config = ConfigDict(extra="forbid")
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timeout_seconds: int = Field(
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default=600,
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gt=0,
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description=(
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"Hard wall-clock cap for the entire run. The executor "
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"transitions the run to ``timed_out`` when this is "
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"exceeded."
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),
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)
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max_retries: int = Field(
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default=2,
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ge=0,
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description=(
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"Per-step retry budget applied when a step raises a "
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"retryable error. Steps may override per-step."
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),
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)
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retry_backoff: Literal["exponential", "linear", "none"] = Field(
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default="exponential",
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description="Backoff policy between retries.",
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)
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concurrency: Literal[
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"drop_if_running", "queue", "always"
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] = Field(
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default="drop_if_running",
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description=(
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"Behaviour when a new fire arrives while a previous run "
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"is still in progress. ``drop_if_running`` skips the new "
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"fire, ``queue`` enqueues it, ``always`` runs it in "
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"parallel."
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),
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)
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budget_cap_usd: float | None = Field(
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default=None,
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gt=0,
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description=(
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"Optional mid-flight cost cap in USD. The executor kills "
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"the run when accumulated cost exceeds this value. v1 "
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"treats this as an advisory because cost tracking lands "
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"with the executor in a later step."
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),
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)
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on_failure: list[PlanStep] = Field(
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default_factory=list,
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description=(
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"Secondary plan executed only when the main plan fails "
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"after retries exhaust. Empty list means no fallback."
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),
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)
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