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feat(automations): implement model eligibility checks for automation creation
- Added model eligibility checks to ensure automations can only use billable models (premium or BYOK). - Introduced new API endpoint to report model eligibility status for search spaces. - Updated frontend components to display eligibility alerts and disable creation options when models are not billable. - Enhanced automation creation forms to reflect model eligibility, preventing users from submitting invalid configurations. - Implemented server-side logic to capture and preserve model preferences across automation edits, ensuring consistent behavior during execution.
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surfsense_backend/app/automations/services/model_policy.py
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surfsense_backend/app/automations/services/model_policy.py
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"""Model-billing policy for automations.
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Automations run unattended, so every run must be **billable**: it may only use
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either a premium global model (``billing_tier == "premium"``) or a user-provided
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BYOK model (a positive config id pointing at a per-user/per-space DB row). Free
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global models and Auto mode are blocked, because Auto can dispatch to a free
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deployment and free models aren't metered in premium credits.
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Config id conventions (shared across chat / image / vision):
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- ``id == 0`` → Auto mode (``AUTO_MODE_ID`` / ``IMAGE_GEN_AUTO_MODE_ID`` /
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``VISION_AUTO_MODE_ID``). Blocked.
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- ``id < 0`` → global YAML/OpenRouter config. Allowed only if premium.
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- ``id > 0`` → user BYOK DB row. Always allowed.
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This module is the single source of truth used by both creation-time enforcement
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(``AutomationService.create`` and the ``create_automation`` chat tool) and the
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runtime backstop (``agent_task`` dependencies).
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Literal
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if TYPE_CHECKING:
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from app.db import SearchSpace
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ModelKind = Literal["llm", "image", "vision"]
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_KIND_LABEL: dict[ModelKind, str] = {
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"llm": "agent LLM",
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"image": "image generation model",
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"vision": "vision model",
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}
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def _is_premium_global(kind: ModelKind, config_id: int) -> bool:
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"""Return True if a negative (global) config id is a premium tier model."""
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from app.config import config as app_config
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cfg: dict | None = None
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if kind == "llm":
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from app.agents.new_chat.llm_config import load_global_llm_config_by_id
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cfg = load_global_llm_config_by_id(config_id)
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elif kind == "image":
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cfg = next(
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(
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c
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for c in app_config.GLOBAL_IMAGE_GEN_CONFIGS
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if c.get("id") == config_id
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),
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None,
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)
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else: # vision
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cfg = next(
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(
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c
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for c in app_config.GLOBAL_VISION_LLM_CONFIGS
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if c.get("id") == config_id
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),
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None,
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)
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if not cfg:
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return False
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return str(cfg.get("billing_tier", "free")).lower() == "premium"
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def _classify(kind: ModelKind, config_id: int | None) -> tuple[bool, str]:
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"""Classify a resolved config id as allowed or blocked.
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Returns ``(allowed, reason)``; ``reason`` is empty when allowed.
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"""
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label = _KIND_LABEL[kind]
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if config_id is None or config_id == 0:
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return (
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False,
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f"The {label} is set to Auto mode. Automations require an explicit "
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"premium model or your own (BYOK) model so every run is billable.",
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)
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if config_id > 0:
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# Positive id → user-owned BYOK config. Always allowed.
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return True, ""
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# Negative id → global config. Allowed only if premium.
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if _is_premium_global(kind, config_id):
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return True, ""
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return (
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False,
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f"The {label} is a free model. Automations can only use premium models "
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"or your own (BYOK) models so every run is billable.",
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)
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def get_model_eligibility(
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*,
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agent_llm_id: int | None,
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image_generation_config_id: int | None,
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vision_llm_config_id: int | None,
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) -> dict:
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"""Return ``{"allowed": bool, "violations": [...]}`` for explicit config ids.
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The ID-based core shared by both the search-space path (creation/eligibility)
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and the captured-snapshot path (runtime backstop). Each violation is
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``{"kind", "config_id", "reason"}``.
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"""
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checks: list[tuple[ModelKind, int | None]] = [
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("llm", agent_llm_id),
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("image", image_generation_config_id),
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("vision", vision_llm_config_id),
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]
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violations: list[dict] = []
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for kind, config_id in checks:
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allowed, reason = _classify(kind, config_id)
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if not allowed:
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violations.append({"kind": kind, "config_id": config_id, "reason": reason})
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return {"allowed": not violations, "violations": violations}
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def get_automation_model_eligibility(search_space: SearchSpace) -> dict:
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"""Return ``{"allowed": bool, "violations": [...]}`` for a search space.
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Used by the eligibility endpoint and the chat tool's early check. Thin
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wrapper over :func:`get_model_eligibility`.
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"""
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return get_model_eligibility(
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agent_llm_id=search_space.agent_llm_id,
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image_generation_config_id=search_space.image_generation_config_id,
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vision_llm_config_id=search_space.vision_llm_config_id,
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)
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class AutomationModelPolicyError(Exception):
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"""Raised when a search space's models are not billable for automations."""
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def __init__(self, violations: list[dict]) -> None:
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self.violations = violations
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reasons = "; ".join(v["reason"] for v in violations)
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super().__init__(
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reasons or "Automations require premium or BYOK models for all model slots."
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)
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def assert_models_billable(
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*,
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agent_llm_id: int | None,
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image_generation_config_id: int | None,
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vision_llm_config_id: int | None,
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) -> None:
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"""Raise :class:`AutomationModelPolicyError` if any explicit id is not billable.
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The ID-based core used by the runtime backstop against an automation's
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captured model snapshot.
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"""
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result = get_model_eligibility(
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agent_llm_id=agent_llm_id,
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image_generation_config_id=image_generation_config_id,
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vision_llm_config_id=vision_llm_config_id,
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)
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if not result["allowed"]:
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raise AutomationModelPolicyError(result["violations"])
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def assert_automation_models_billable(search_space: SearchSpace) -> None:
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"""Raise :class:`AutomationModelPolicyError` if any model slot is not billable."""
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result = get_automation_model_eligibility(search_space)
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if not result["allowed"]:
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raise AutomationModelPolicyError(result["violations"])
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