Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

hermes_cli/model_data_policy_guard.py

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+"""Data-policy confirmation helpers for model selection surfaces.
+
+Some inference tiers are cheap *because* the vendor trains future models on your
+prompts and completions. Selecting one for the low price without realising the
+data trade-off is a real footgun. This guard mirrors
+``hermes_cli.model_cost_guard`` — it returns a warning payload that the CLI and
+web model-selection flows surface as an explicit confirm step.
+
+Why a static table (not a ProviderProfile hook): the guard runs inside core
+selection code (``auth.py`` / ``web_server.py``), which never calls into the
+active provider profile for a selection-time warning. Keeping the rule set here
+also means it renders regardless of which provider plugin happens to be loaded,
+and it stays testable without importing arbitrary third-party plugin code into
+the selection path.
+
+The status is NOT machine-readable anywhere today: neither models.dev nor the
+Meta ``/v1/models`` payload exposes a training/retention flag (verified
+2026-08-07). The only reliable signals are the vendor-documented model id and
+its anomalously low pricing, so the rule keys on the id.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+
+@dataclass(frozen=True)
+class DataTrainingWarning:
+    """Confirmation payload for models whose tier trains on user data."""
+
+    model: str
+    provider: str
+    message: str
+
+
+# ── Rule table ────────────────────────────────────────────────────────────
+# Each rule: (human label, predicate over (model_lower, provider_lower), message).
+# Extensible — new data-collection tiers from other vendors slot in here without
+# touching the call sites. Predicates are intentionally conservative: match an
+# explicit, vendor-documented id rather than guessing from price alone (price is
+# only a corroborating signal and can change).
+
+def _is_meta_contributor(model_lower: str, provider_lower: str) -> bool:
+    # Meta Model API "contributor" tier (muse-spark-1.2-contributor and any
+    # future -contributor checkpoints). Match on the id suffix; do not require a
+    # specific provider id so it fires whether selected via the meta-ai plugin,
+    # a gateway, or a custom endpoint that serves the same model id.
+    return model_lower.endswith("-contributor") or "contributor" in model_lower.split("-")
+
+
+_META_CONTRIBUTOR_MESSAGE = (
+    "!!! CONTRIBUTOR TIER — TRAINS ON YOUR DATA !!!\n"
+    "\n"
+    "This is Meta's contributor tier. Selecting it permits Meta to use your\n"
+    "prompts and completions to train future Meta models.\n"
+    "\n"
+    "See current pricing and rate limits for the Meta Model API here:\n"
+    "  https://dev.meta.ai/docs/pricing-rate-limits/\n"
+    "\n"
+    "It lowers the barrier to entry for prototyping, testing integrations, and\n"
+    "scaling experiments where training on your data is acceptable. Do NOT use it\n"
+    "for confidential, proprietary, personal, or otherwise sensitive data. For the\n"
+    "same model with no training on your data, select the standard variant\n"
+    "(without the -contributor suffix).\n"
+    "\n"
+    "Confirm only if training on your prompts and completions is acceptable."
+)
+
+
+# (predicate, message) pairs, evaluated in order; first match wins.
+_RULES: tuple[tuple[Callable[[str, str], bool], str], ...] = (
+    (_is_meta_contributor, _META_CONTRIBUTOR_MESSAGE),
+)
+
+
+def data_training_warning(
+    model_name: str,
+    *,
+    provider: Optional[str] = None,
+    base_url: Optional[str] = None,  # noqa: ARG001 — reserved for host-scoped rules
+) -> Optional[DataTrainingWarning]:
+    """Return a warning payload when *model_name* selects a data-training tier.
+
+    Returns ``None`` when no rule matches (the common case). Callers should run
+    this after model resolution so aliases / provider-specific ids have settled,
+    and surface ``.message`` as a confirm prompt.
+    """
+    model = (model_name or "").strip()
+    if not model:
+        return None
+    model_lower = model.lower()
+    provider_lower = (provider or "").strip().lower()
+
+    for predicate, message in _RULES:
+        try:
+            if predicate(model_lower, provider_lower):
+                return DataTrainingWarning(
+                    model=model,
+                    provider=(provider or "").strip(),
+                    message=message,
+                )
+        except Exception:
+            # A misbehaving predicate must never break model selection.
+            continue
+    return None