mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-29 19:35:20 +02:00
chore(automation): trim docstrings to intent only
Cut the docstrings and Field(description=...) text across the entire automations/ tree down to single-line intent statements, matching the multi_agent_chat conciseness style: - Module docstrings: one line stating what the file is. - Class docstrings: deleted when the class name + module docstring already cover intent; kept only where they add a constraint or rationale not visible in the signature. - Pydantic Field descriptions: short noun phrases / clauses, not full sentences. Reasoning that belonged in the design plan moved out of the code. - Enum values: per-value docstrings replaced with terse inline comments where the meaning isn't obvious from the name. Behaviour is unchanged. The same 33 files, same public surface, same imports — verified by re-running the 10-point registry smoke test and the 8-point schema round-trip / constraint suite from commits 9 and 10. LOC: 1180 → 691 (-42%).
This commit is contained in:
parent
7a96c0e29c
commit
f0e00bd3ee
33 changed files with 80 additions and 568 deletions
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@ -1,4 +1,4 @@
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"""Automations: scheduled / triggered runs of capabilities — see automation-design-plan.md."""
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"""Automations engine — see automation-design-plan.md."""
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from __future__ import annotations
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@ -1,4 +1,4 @@
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"""Persistence layer: SQLAlchemy enums under ``enums/`` and models under ``models/``."""
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"""SQLAlchemy models and enums for the automation tables."""
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from __future__ import annotations
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"""SQLAlchemy / Python enums backing the three automation tables."""
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"""Enums for the automation tables."""
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from __future__ import annotations
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@ -1,4 +1,4 @@
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"""``AutomationStatus`` — lifecycle of a stored automation definition."""
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"""Automation lifecycle status."""
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from __future__ import annotations
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@ -6,13 +6,6 @@ from enum import StrEnum
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class AutomationStatus(StrEnum):
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"""Status of an automation in the registry.
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``active`` — eligible to fire from its triggers.
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``paused`` — definition retained, triggers do not fire.
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``archived`` — kept for run history only; no edits, no fires.
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"""
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ACTIVE = "active"
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PAUSED = "paused"
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ARCHIVED = "archived"
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ACTIVE = "active" # eligible to fire
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PAUSED = "paused" # kept, but triggers don't fire
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ARCHIVED = "archived" # read-only history
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@ -1,4 +1,4 @@
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"""``RunStatus`` — the state machine of a single ``AutomationRun``."""
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"""AutomationRun state machine: pending → running → (succeeded|failed|cancelled|timed_out)."""
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from __future__ import annotations
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@ -6,20 +6,6 @@ from enum import StrEnum
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class RunStatus(StrEnum):
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"""Lifecycle states of an ``AutomationRun`` row.
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Transitions are linear with three terminal branches:
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pending → running → (succeeded | failed | cancelled | timed_out)
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``pending`` — row created, executor task enqueued, work not started.
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``running`` — executor has picked up the run.
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``succeeded`` — terminal: plan completed without error.
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``failed`` — terminal: at least one step raised an unrecoverable error.
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``cancelled`` — terminal: caller asked for cancellation.
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``timed_out`` — terminal: run exceeded its configured timeout.
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"""
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PENDING = "pending"
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RUNNING = "running"
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SUCCEEDED = "succeeded"
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"""``TriggerType`` — the trigger-kind discriminator (v1 = schedule, manual)."""
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"""Trigger-kind discriminator. v1: schedule | manual; webhook/event in Phase 2/3."""
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from __future__ import annotations
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@ -6,16 +6,5 @@ from enum import StrEnum
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class TriggerType(StrEnum):
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"""Kind of trigger an ``AutomationTrigger`` row represents.
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v1 ships two kinds:
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``schedule`` — fires on a cron expression managed by Celery Beat.
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``manual`` — fires on demand from the UI's "Run now" affordance.
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``webhook`` and ``event`` are deferred to Phase 2 and Phase 3
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respectively; adding them is an enum-value extension only.
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"""
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SCHEDULE = "schedule"
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MANUAL = "manual"
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@ -1,4 +1,4 @@
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"""SQLAlchemy models: one file per table (``automation.py``, ``trigger.py``, ``run.py``)."""
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"""SQLAlchemy models, one per table."""
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from __future__ import annotations
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"""``Automation`` table — the editable, versioned automation definition."""
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"""``automations`` table — editable, versioned automation definition."""
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from __future__ import annotations
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@ -21,15 +21,6 @@ from ..enums.automation_status import AutomationStatus
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class Automation(BaseModel, TimestampMixin):
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"""The editable, versioned spec a user authors.
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The ``definition`` JSON is what the user (or the NL generator) writes
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and edits. Each save bumps ``version`` by one; the previous JSON is
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not kept in this row — version history is reconstructed from the
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``definition_snapshot`` column on every ``AutomationRun`` that fired
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against a given version.
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"""
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__tablename__ = "automations"
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search_space_id = Column(
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@ -59,12 +50,7 @@ class Automation(BaseModel, TimestampMixin):
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definition = Column(JSONB, nullable=False)
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version = Column(
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Integer,
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nullable=False,
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default=1,
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server_default="1",
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)
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version = Column(Integer, nullable=False, default=1, server_default="1")
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updated_at = Column(
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TIMESTAMP(timezone=True),
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"""``AutomationRun`` table — the immutable per-fire execution record."""
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"""``automation_runs`` table — immutable per-fire execution record."""
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from __future__ import annotations
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@ -18,16 +18,6 @@ from ..enums.run_status import RunStatus
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class AutomationRun(BaseModel, TimestampMixin):
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"""One execution of an automation.
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Every fire of any trigger inserts exactly one row here. The row is
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immutable from the user's perspective — the executor only updates
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``status``, ``step_results``, ``output``, ``artifacts``, ``error``,
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``started_at``, ``finished_at`` as the run progresses; the
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``definition_snapshot`` is locked at fire time so the user can always
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see exactly what code path executed for any historical run.
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"""
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__tablename__ = "automation_runs"
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automation_id = Column(
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@ -52,18 +42,14 @@ class AutomationRun(BaseModel, TimestampMixin):
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index=True,
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)
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# locked at fire time so historical runs always show the exact code path
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definition_snapshot = Column(JSONB, nullable=False)
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trigger_payload = Column(JSONB, nullable=True)
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resolved_inputs = Column(JSONB, nullable=False, server_default="{}")
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step_results = Column(JSONB, nullable=False, server_default="[]")
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output = Column(JSONB, nullable=True)
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artifacts = Column(JSONB, nullable=False, server_default="[]")
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error = Column(JSONB, nullable=True)
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started_at = Column(TIMESTAMP(timezone=True), nullable=True)
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"""``AutomationTrigger`` table — one row per (automation, trigger-instance) pair."""
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"""``automation_triggers`` table — one row per (automation, trigger-instance) pair."""
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from __future__ import annotations
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@ -18,14 +18,6 @@ from ..enums.trigger_type import TriggerType
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class AutomationTrigger(BaseModel, TimestampMixin):
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"""One trigger attached to an automation.
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An automation may have multiple triggers — e.g. a ``schedule`` trigger
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for the autonomous path and a ``manual`` trigger backing the UI's
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"Run now" affordance. Each trigger's ``config`` is validated against
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the registered ``TriggerDefinition.config_schema`` for its ``type``.
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"""
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__tablename__ = "automation_triggers"
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automation_id = Column(
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@ -51,7 +43,4 @@ class AutomationTrigger(BaseModel, TimestampMixin):
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index=True,
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)
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last_fired_at = Column(
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TIMESTAMP(timezone=True),
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nullable=True,
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)
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last_fired_at = Column(TIMESTAMP(timezone=True), nullable=True)
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"""Three registries — ``capabilities/``, ``actions/``, ``triggers/`` — populated at import time."""
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"""Capability, action, and trigger registries — populated at process startup."""
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from __future__ import annotations
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"""Action registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Action registry."""
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from __future__ import annotations
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"""Action registry: in-memory dict + ``register_action`` API."""
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"""In-memory action registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,26 +8,16 @@ _REGISTRY: dict[str, ActionDefinition] = {}
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def register_action(action: ActionDefinition) -> None:
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"""Add an action to the in-memory registry.
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Raises ``ValueError`` on duplicate ``type`` — registration runs
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once per process, so a duplicate is always a bug.
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"""
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"""Register an action. Raises on duplicate type."""
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if action.type in _REGISTRY:
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raise ValueError(
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f"Action already registered: {action.type!r}"
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)
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raise ValueError(f"Action already registered: {action.type!r}")
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_REGISTRY[action.type] = action
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def get_action(action_type: str) -> ActionDefinition | None:
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"""Look up one action by type. Returns ``None`` on miss."""
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return _REGISTRY.get(action_type)
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def all_actions() -> dict[str, ActionDefinition]:
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"""Snapshot of the registry as a defensive copy."""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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"""``ActionDefinition`` dataclass — the v1-minimum action shape."""
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"""``ActionDefinition`` dataclass and handler signature."""
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from __future__ import annotations
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@ -7,36 +7,10 @@ from dataclasses import dataclass
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from typing import Any
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ActionHandler = Callable[[dict[str, Any]], Awaitable[Any]]
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"""The signature every action handler must satisfy.
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Identical in shape to ``CapabilityHandler`` — both receive a
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caller-validated input dict and return an arbitrary output. The
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distinction is purely architectural: capabilities are the low-level
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"what SurfSense can do" surface, actions are the user-facing
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building blocks composed into a plan.
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"""
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@dataclass(frozen=True, slots=True)
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class ActionDefinition:
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"""A user-facing step type the plan editor can compose.
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v1 trims the dataclass to the five fields necessary for
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registry dispatch and form rendering. The full design (§4)
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includes ``output_contract``, ``uses_capabilities``, and
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``produces_artifacts``; all three are deferred until a consumer
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feature requires them:
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- ``output_contract`` — the loose ``agent_task`` action declares
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its output shape per-step via ``config.output_schema``, so the
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action-level contract is not needed in v1.
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- ``uses_capabilities`` — would let the NL generator do static
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analysis of which capabilities each action invokes; deferred
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because v1 ships a single (``agent_task``) action.
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- ``produces_artifacts`` — deferred alongside the artifact
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pipeline (see §13 decision 26).
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"""
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type: str
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name: str
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description: str
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"""Capability registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Capability registry."""
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from __future__ import annotations
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"""Capability registry: in-memory dict + ``register_capability`` API."""
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"""In-memory capability registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,33 +8,16 @@ _REGISTRY: dict[str, Capability] = {}
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def register_capability(capability: Capability) -> None:
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"""Add a capability to the in-memory registry.
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Raises ``ValueError`` on duplicate ``id`` — registration is
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idempotent only at the module level (a module's
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``register_capability`` call runs once per process), so a
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duplicate is always a bug.
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"""
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"""Register a capability. Raises on duplicate id."""
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if capability.id in _REGISTRY:
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raise ValueError(
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f"Capability already registered: {capability.id!r}"
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)
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raise ValueError(f"Capability already registered: {capability.id!r}")
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_REGISTRY[capability.id] = capability
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def get_capability(capability_id: str) -> Capability | None:
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"""Look up one capability by id. Returns ``None`` on miss."""
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return _REGISTRY.get(capability_id)
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def all_capabilities() -> dict[str, Capability]:
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"""Snapshot of the registry as a defensive copy.
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Returned dict is safe to iterate while other code calls
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``register_capability`` (which v1 never does post-startup, but
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the contract holds anyway).
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"""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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"""``Capability`` dataclass — the v1-minimum five-field shape."""
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"""``Capability`` dataclass and handler signature. Locked at five fields for v1."""
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from __future__ import annotations
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@ -7,32 +7,10 @@ from dataclasses import dataclass
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from typing import Any
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CapabilityHandler = Callable[[dict[str, Any]], Awaitable[Any]]
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"""The signature every capability handler must satisfy.
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The handler is a closure that already holds whatever runtime context
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it needs (DB session, search-space scope, logger, etc.). The
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registry only passes through the caller's input dict — the same dict
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that was validated against ``input_schema``.
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"""
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@dataclass(frozen=True, slots=True)
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class Capability:
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"""The unit of "what SurfSense can do," consumed by every layer.
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v1 keeps the dataclass to exactly five fields. Earlier drafts
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considered ``name``, ``required_credentials``, ``side_effects``,
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``expected_duration_seconds``, and ``cost_estimate``; every one
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of those has been removed until a concrete consumer feature
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requires it (see ``automation-design-plan.md`` §3, decision v1).
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The handler is a ready-to-call function. It does not receive a
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context argument — context is bound at registration time by the
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factory that builds the closure (so a capability returned to an
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agent's tool list looks identical to one returned to an
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automation's action runtime).
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"""
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id: str
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description: str
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input_schema: dict[str, Any]
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"""Trigger registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Trigger registry."""
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from __future__ import annotations
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@ -1,4 +1,4 @@
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"""Trigger registry: in-memory dict + ``register_trigger`` API."""
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"""In-memory trigger registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,26 +8,16 @@ _REGISTRY: dict[str, TriggerDefinition] = {}
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def register_trigger(trigger: TriggerDefinition) -> None:
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"""Add a trigger to the in-memory registry.
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Raises ``ValueError`` on duplicate ``type`` — registration runs
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once per process, so a duplicate is always a bug.
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"""
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"""Register a trigger. Raises on duplicate type."""
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if trigger.type in _REGISTRY:
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raise ValueError(
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f"Trigger already registered: {trigger.type!r}"
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)
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raise ValueError(f"Trigger already registered: {trigger.type!r}")
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_REGISTRY[trigger.type] = trigger
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def get_trigger(trigger_type: str) -> TriggerDefinition | None:
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"""Look up one trigger by type. Returns ``None`` on miss."""
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return _REGISTRY.get(trigger_type)
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def all_triggers() -> dict[str, TriggerDefinition]:
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"""Snapshot of the registry as a defensive copy."""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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@ -1,4 +1,4 @@
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"""``TriggerDefinition`` dataclass — declarative trigger metadata, no handler."""
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"""``TriggerDefinition`` dataclass. Declarative; firing is the dispatcher's job."""
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from __future__ import annotations
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@ -8,27 +8,6 @@ from typing import Any
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@dataclass(frozen=True, slots=True)
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class TriggerDefinition:
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"""A trigger type the dispatcher knows how to fire.
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Triggers are purely declarative: the dispatcher (a single
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process-wide component, not a per-type handler) reads the
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``automation_triggers`` table and decides when each row should
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fire. The trigger's job here is to declare its input/output
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contract:
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- ``config_schema``: JSON Schema for the persisted
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``AutomationTrigger.config`` — used by the form editor and
|
||||
validated on save.
|
||||
- ``payload_schema``: JSON Schema for the payload the dispatcher
|
||||
will deliver to the executor at fire time (e.g., a schedule
|
||||
trigger emits ``fired_at`` / ``scheduled_for`` /
|
||||
``last_fired_at``).
|
||||
|
||||
No ``handler`` field — firing is a dispatcher responsibility,
|
||||
not a per-trigger one. This keeps the dispatcher single and
|
||||
leaves trigger types as pure metadata.
|
||||
"""
|
||||
|
||||
type: str
|
||||
description: str
|
||||
config_schema: dict[str, Any]
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""Pydantic schemas: definition envelope, trigger configs, action configs."""
|
||||
"""Pydantic schemas for the automation definition and per-type configs."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""Per-action config schemas: one file per action type registered in v1."""
|
||||
"""Per-action config schemas, one per action type."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
|
|||
|
|
@ -8,59 +8,20 @@ from pydantic import BaseModel, ConfigDict, Field
|
|||
|
||||
|
||||
class AgentTaskActionConfig(BaseModel):
|
||||
"""Config for an ``agent_task`` plan step.
|
||||
|
||||
Validated against ``PlanStep.config`` whenever the step's
|
||||
``action`` is ``agent_task``. The step instructs the LangGraph
|
||||
Deep Agent runtime to:
|
||||
|
||||
1. Receive ``prompt`` (with all preceding-step outputs and inputs
|
||||
already rendered by the template engine).
|
||||
2. Run the agent with access to *exactly* the capabilities named
|
||||
in ``tools`` — nothing else from the registry is visible to
|
||||
this agent invocation.
|
||||
3. Return a JSON object matching ``output_schema`` (recommended;
|
||||
the executor validates and re-prompts on mismatch).
|
||||
|
||||
``output_schema`` is the design's "dynamic output contract" —
|
||||
instead of locking the output shape on the ActionDefinition (as
|
||||
tight actions do), the user declares the shape they want for this
|
||||
specific step, and the agent has to match it.
|
||||
"""
|
||||
"""Run a LangGraph Deep Agent restricted to a scoped capability list."""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
prompt: str = Field(
|
||||
...,
|
||||
description=(
|
||||
"The task prompt rendered through the Jinja sandbox. May "
|
||||
"reference automation inputs and prior-step outputs."
|
||||
),
|
||||
min_length=1,
|
||||
)
|
||||
prompt: str = Field(..., min_length=1, description="Task prompt; Jinja-rendered.")
|
||||
tools: list[str] = Field(
|
||||
default_factory=list,
|
||||
description=(
|
||||
"Allowlist of capability IDs the agent may call (e.g., "
|
||||
"'search_space.query'). Empty list = no tool access; the "
|
||||
"agent must answer from the prompt alone."
|
||||
),
|
||||
description="Capability IDs the agent may call. Empty = no tool access.",
|
||||
)
|
||||
model: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional LiteLLM model identifier (e.g., "
|
||||
"'anthropic/claude-sonnet-4-7'). Omitted means the "
|
||||
"automation falls back to the search space's default "
|
||||
"agent_llm_id."
|
||||
),
|
||||
description="LiteLLM model id. Defaults to the search space's agent_llm_id.",
|
||||
)
|
||||
output_schema: dict[str, Any] | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional JSON Schema declaring the shape the agent must "
|
||||
"return. Strongly recommended; the editor warns when "
|
||||
"missing. Validated by the executor before binding to "
|
||||
"``output_as``."
|
||||
),
|
||||
description="JSON Schema the agent must return. Recommended.",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""Automation definition envelope: the editable structured spec users author and run."""
|
||||
"""Automation definition envelope and its building blocks."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``AutomationDefinition`` — the top-level envelope persisted in ``automations.definition``."""
|
||||
"""``AutomationDefinition`` — top-level envelope persisted in ``automations.definition``."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -12,78 +12,15 @@ from .trigger_spec import TriggerSpec
|
|||
|
||||
|
||||
class AutomationDefinition(BaseModel):
|
||||
"""The top-level JSON shape stored in ``automations.definition``.
|
||||
|
||||
This is the editable spec a user authors (or the NL generator
|
||||
produces). The envelope is structural only — every nested
|
||||
discriminator (``triggers[].type``, ``plan[].action``) is resolved
|
||||
against the registries at validation time, so adding a new
|
||||
trigger or action type does not require touching this schema.
|
||||
|
||||
See ``automation-design-plan.md`` §5 for the worked example and
|
||||
rationale.
|
||||
"""
|
||||
"""Top-level shape of an automation. See automation-design-plan.md §5."""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
schema_version: str = Field(
|
||||
default="1.0",
|
||||
description=(
|
||||
"Schema version of the envelope itself. Migrations bump "
|
||||
"this when the envelope shape changes; nested per-type "
|
||||
"configs evolve independently via the registries."
|
||||
),
|
||||
)
|
||||
name: str = Field(
|
||||
...,
|
||||
description="Short, user-facing name shown in lists.",
|
||||
min_length=1,
|
||||
max_length=200,
|
||||
)
|
||||
goal: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional plain-language statement of what the "
|
||||
"automation is for. Used by the NL generator's review "
|
||||
"pass and by the UI's run dialog."
|
||||
),
|
||||
)
|
||||
inputs: InputsBlock | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional input contract. When omitted, the automation "
|
||||
"accepts no inputs at fire time."
|
||||
),
|
||||
)
|
||||
triggers: list[TriggerSpec] = Field(
|
||||
default_factory=list,
|
||||
description=(
|
||||
"Triggers that fire this automation. Empty list means "
|
||||
"the automation is only runnable via the manual "
|
||||
"``Run now`` path."
|
||||
),
|
||||
)
|
||||
plan: list[PlanStep] = Field(
|
||||
...,
|
||||
description=(
|
||||
"Ordered sequence of steps. Executed in array order — "
|
||||
"no parallelism, no DAGs, no loops at the envelope "
|
||||
"level."
|
||||
),
|
||||
min_length=1,
|
||||
)
|
||||
execution: ExecutionBlock = Field(
|
||||
default_factory=ExecutionBlock,
|
||||
description=(
|
||||
"Execution defaults (timeouts, retries, concurrency, "
|
||||
"budget). All fields default to safe values; the block "
|
||||
"may be omitted entirely."
|
||||
),
|
||||
)
|
||||
metadata: MetadataBlock = Field(
|
||||
default_factory=MetadataBlock,
|
||||
description=(
|
||||
"Free-form metadata (tags, NL-generator breadcrumbs, "
|
||||
"UI annotations). Tolerates unknown keys by design."
|
||||
),
|
||||
)
|
||||
schema_version: str = "1.0"
|
||||
name: str = Field(..., min_length=1, max_length=200)
|
||||
goal: str | None = None
|
||||
inputs: InputsBlock | None = None
|
||||
triggers: list[TriggerSpec] = Field(default_factory=list)
|
||||
plan: list[PlanStep] = Field(..., min_length=1)
|
||||
execution: ExecutionBlock = Field(default_factory=ExecutionBlock)
|
||||
metadata: MetadataBlock = Field(default_factory=MetadataBlock)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``ExecutionBlock`` — the ``execution`` section of the automation definition."""
|
||||
"""``ExecutionBlock`` — automation-wide execution defaults (overridable per step)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -10,67 +10,16 @@ from .plan_step import PlanStep
|
|||
|
||||
|
||||
class ExecutionBlock(BaseModel):
|
||||
"""The ``execution`` block of an ``AutomationDefinition``.
|
||||
|
||||
Carries automation-wide defaults that individual ``PlanStep``s
|
||||
can override. Every field has a sane default so an automation
|
||||
definition may omit the block entirely; in that case all defaults
|
||||
apply.
|
||||
|
||||
``on_failure`` is a secondary plan that runs only when the main
|
||||
``plan`` fails after retries exhaust. It uses the same
|
||||
``PlanStep`` shape as the main plan and shares the same execution
|
||||
semantics.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
timeout_seconds: int = Field(
|
||||
default=600,
|
||||
gt=0,
|
||||
description=(
|
||||
"Hard wall-clock cap for the entire run. The executor "
|
||||
"transitions the run to ``timed_out`` when this is "
|
||||
"exceeded."
|
||||
),
|
||||
)
|
||||
max_retries: int = Field(
|
||||
default=2,
|
||||
ge=0,
|
||||
description=(
|
||||
"Per-step retry budget applied when a step raises a "
|
||||
"retryable error. Steps may override per-step."
|
||||
),
|
||||
)
|
||||
retry_backoff: Literal["exponential", "linear", "none"] = Field(
|
||||
default="exponential",
|
||||
description="Backoff policy between retries.",
|
||||
)
|
||||
concurrency: Literal[
|
||||
"drop_if_running", "queue", "always"
|
||||
] = Field(
|
||||
default="drop_if_running",
|
||||
description=(
|
||||
"Behaviour when a new fire arrives while a previous run "
|
||||
"is still in progress. ``drop_if_running`` skips the new "
|
||||
"fire, ``queue`` enqueues it, ``always`` runs it in "
|
||||
"parallel."
|
||||
),
|
||||
)
|
||||
timeout_seconds: int = Field(default=600, gt=0, description="Wall-clock cap for the run.")
|
||||
max_retries: int = Field(default=2, ge=0, description="Per-step retry budget.")
|
||||
retry_backoff: Literal["exponential", "linear", "none"] = "exponential"
|
||||
concurrency: Literal["drop_if_running", "queue", "always"] = "drop_if_running"
|
||||
budget_cap_usd: float | None = Field(
|
||||
default=None,
|
||||
gt=0,
|
||||
description=(
|
||||
"Optional mid-flight cost cap in USD. The executor kills "
|
||||
"the run when accumulated cost exceeds this value. v1 "
|
||||
"treats this as an advisory because cost tracking lands "
|
||||
"with the executor in a later step."
|
||||
),
|
||||
default=None, gt=0, description="Kill the run when accumulated cost exceeds this."
|
||||
)
|
||||
on_failure: list[PlanStep] = Field(
|
||||
default_factory=list,
|
||||
description=(
|
||||
"Secondary plan executed only when the main plan fails "
|
||||
"after retries exhaust. Empty list means no fallback."
|
||||
),
|
||||
description="Steps run when the main plan fails after retries.",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``InputsBlock`` — the ``inputs`` section of the automation definition."""
|
||||
"""``InputsBlock`` — JSON Schema for inputs an automation accepts at fire time."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -8,23 +8,6 @@ from pydantic import BaseModel, ConfigDict, Field
|
|||
|
||||
|
||||
class InputsBlock(BaseModel):
|
||||
"""The ``inputs`` block of an ``AutomationDefinition``.
|
||||
|
||||
Holds a JSON Schema describing what data the automation accepts at
|
||||
fire time. The same schema is used by:
|
||||
|
||||
- The form editor (to render the manual-run dialog).
|
||||
- The dispatcher (to validate trigger payloads before enqueueing
|
||||
executor work).
|
||||
- The template engine (to expose ``{{ inputs.* }}`` references in
|
||||
plan-step configs).
|
||||
|
||||
The ``schema`` value is the JSON-Schema dict itself, not a
|
||||
Pydantic model — automations express their input contract in pure
|
||||
JSON Schema so it round-trips losslessly through the database and
|
||||
the NL generator.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(
|
||||
extra="forbid",
|
||||
populate_by_name=True,
|
||||
|
|
@ -34,10 +17,5 @@ class InputsBlock(BaseModel):
|
|||
schema_: dict[str, Any] = Field(
|
||||
...,
|
||||
alias="schema",
|
||||
description=(
|
||||
"JSON Schema (draft-07 compatible) describing the inputs "
|
||||
"this automation accepts. Properties may use the special "
|
||||
"``$last_fired_at`` default literal to bind to the "
|
||||
"trigger's last fire time."
|
||||
),
|
||||
description="JSON Schema (draft-07) for accepted inputs.",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``MetadataBlock`` — the ``metadata`` section of the automation definition."""
|
||||
"""``MetadataBlock`` — free-form metadata on a definition. Extra keys allowed."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -6,31 +6,9 @@ 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.",
|
||||
)
|
||||
tags: list[str] = Field(default_factory=list)
|
||||
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)."
|
||||
),
|
||||
default=False, description="True when produced by the NL generator."
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``PlanStep`` — one entry in the envelope's ``plan`` array."""
|
||||
"""``PlanStep`` — one step in the sequential plan."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -8,79 +8,21 @@ from pydantic import BaseModel, ConfigDict, Field
|
|||
|
||||
|
||||
class PlanStep(BaseModel):
|
||||
"""One step in an automation's sequential plan.
|
||||
|
||||
Steps run in array order, no parallelism, no DAGs, no loops. The
|
||||
``when`` Jinja expression provides conditional skip; branching is
|
||||
achieved by ``when`` clauses on multiple steps. For looping or
|
||||
parallel work, the user routes through ``agent_task`` and lets the
|
||||
agent reason about it.
|
||||
|
||||
``config`` is dispatched against the action registry at
|
||||
validation time — its shape is determined by
|
||||
``ActionDefinition.config_schema`` for the ``action`` value.
|
||||
|
||||
``output_as`` binds the step's typed output into the template
|
||||
namespace for later steps, e.g. ``output_as: 'summary'`` then
|
||||
``{{ summary.bullets }}`` in a downstream step's config.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
step_id: str = Field(
|
||||
...,
|
||||
description=(
|
||||
"Unique-within-plan identifier. Used in run logs and as "
|
||||
"the default for ``output_as`` when not provided."
|
||||
),
|
||||
min_length=1,
|
||||
)
|
||||
action: str = Field(
|
||||
...,
|
||||
description=(
|
||||
"Action-type discriminator (e.g., ``agent_task``). "
|
||||
"Resolved against the action registry."
|
||||
),
|
||||
min_length=1,
|
||||
)
|
||||
step_id: str = Field(..., min_length=1, description="Unique within the plan.")
|
||||
action: str = Field(..., min_length=1, description="Action type; resolved via registry.")
|
||||
when: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional Jinja expression evaluated against the run "
|
||||
"context. Step is skipped when the expression is "
|
||||
"falsy."
|
||||
),
|
||||
description="Optional Jinja expression; step is skipped when falsy.",
|
||||
)
|
||||
config: dict[str, Any] = Field(
|
||||
default_factory=dict,
|
||||
description=(
|
||||
"Action-type-specific config. Validated against the "
|
||||
"registered ``ActionDefinition.config_schema`` for "
|
||||
"``action`` at definition-save time. Jinja templates "
|
||||
"inside config are rendered at step-execute time."
|
||||
),
|
||||
description="Action-type-specific config; Jinja-rendered at execute time.",
|
||||
)
|
||||
output_as: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Name to bind the step output under for downstream "
|
||||
"steps. Defaults to ``step_id`` when omitted."
|
||||
),
|
||||
)
|
||||
max_retries: int | None = Field(
|
||||
default=None,
|
||||
ge=0,
|
||||
description=(
|
||||
"Per-step override of the automation-level ``max_retries``. "
|
||||
"Omitted means inherit from execution block."
|
||||
),
|
||||
)
|
||||
timeout_seconds: int | None = Field(
|
||||
default=None,
|
||||
gt=0,
|
||||
description=(
|
||||
"Per-step override of the automation-level "
|
||||
"``timeout_seconds``. Omitted means inherit from "
|
||||
"execution block."
|
||||
),
|
||||
description="Bind step output under this name. Defaults to step_id.",
|
||||
)
|
||||
max_retries: int | None = Field(default=None, ge=0)
|
||||
timeout_seconds: int | None = Field(default=None, gt=0)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``TriggerSpec`` — one entry in the envelope's ``triggers`` array."""
|
||||
"""``TriggerSpec`` — one entry in the definition's ``triggers[]`` array."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -8,33 +8,10 @@ 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,
|
||||
)
|
||||
type: str = Field(..., min_length=1, description="Trigger type; resolved via registry.")
|
||||
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."
|
||||
),
|
||||
description="Type-specific config; validated against the trigger's schema.",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""Per-trigger config schemas: one file per trigger type registered in v1."""
|
||||
"""Per-trigger config schemas, one per trigger type."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""``ManualTriggerConfig`` — config for the ``manual`` trigger type (empty in v1)."""
|
||||
"""``ManualTriggerConfig`` — config for the ``manual`` trigger (empty in v1)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
|
@ -6,16 +6,4 @@ from pydantic import BaseModel, ConfigDict
|
|||
|
||||
|
||||
class ManualTriggerConfig(BaseModel):
|
||||
"""Config for the UI-driven ``manual`` trigger.
|
||||
|
||||
Validated against ``AutomationTrigger.config`` whenever the
|
||||
persisted ``type`` is ``manual``. v1 carries no configurable
|
||||
fields — the "Run now" affordance simply fires this trigger with
|
||||
an empty config object. The model exists so the registry dispatch
|
||||
is uniform across all trigger types.
|
||||
|
||||
Future versions may add fields here (e.g., a fixed prompt to
|
||||
pre-fill the run dialog with) without breaking v1 payloads.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
|
|
|||
|
|
@ -6,28 +6,7 @@ from pydantic import BaseModel, ConfigDict, Field
|
|||
|
||||
|
||||
class ScheduleTriggerConfig(BaseModel):
|
||||
"""Config for a cron-driven trigger.
|
||||
|
||||
Validated against ``AutomationTrigger.config`` whenever the
|
||||
persisted ``type`` is ``schedule``. The cron expression is
|
||||
evaluated by Celery Beat's source; the timezone is an IANA name
|
||||
(e.g., ``Africa/Kigali``) and is required so the user's cron is
|
||||
unambiguous across DST boundaries.
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
cron: str = Field(
|
||||
...,
|
||||
description=(
|
||||
"Five-field cron expression. Minimum resolution is one "
|
||||
"minute; the form editor warns when intervals tighter "
|
||||
"than 15 minutes are used."
|
||||
),
|
||||
examples=["0 9 * * 1-5"],
|
||||
)
|
||||
timezone: str = Field(
|
||||
...,
|
||||
description="IANA timezone name (e.g., 'Africa/Kigali', 'UTC').",
|
||||
examples=["Africa/Kigali"],
|
||||
)
|
||||
cron: str = Field(..., description="Five-field cron expression.", examples=["0 9 * * 1-5"])
|
||||
timezone: str = Field(..., description="IANA timezone.", examples=["Africa/Kigali"])
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue