mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-29 19:35:20 +02:00
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.
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
"""``TriggerSpec`` — one entry in the envelope's ``triggers`` array."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any
|
|
|
|
from pydantic import BaseModel, ConfigDict, Field
|
|
|
|
|
|
class TriggerSpec(BaseModel):
|
|
"""One trigger attached to an automation, as it appears in the definition.
|
|
|
|
The envelope keeps ``config`` as an untyped JSON object on purpose
|
|
— the per-type config schemas live in
|
|
``app.automations.schemas.triggers`` and are dispatched at
|
|
validation time by looking up ``type`` in the trigger registry.
|
|
|
|
This mirrors the design's "definitions are pure data" principle:
|
|
the envelope describes shape, the registry resolves names to
|
|
behaviour.
|
|
"""
|
|
|
|
model_config = ConfigDict(extra="forbid")
|
|
|
|
type: str = Field(
|
|
...,
|
|
description=(
|
|
"Trigger-type discriminator (e.g., ``schedule``, ``manual``). "
|
|
"Resolved against the trigger registry."
|
|
),
|
|
min_length=1,
|
|
)
|
|
config: dict[str, Any] = Field(
|
|
default_factory=dict,
|
|
description=(
|
|
"Trigger-type-specific config. Validated against the "
|
|
"registered ``TriggerDefinition.config_schema`` for "
|
|
"``type`` at definition-save time."
|
|
),
|
|
)
|