Teknium
98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
"""Data-policy confirmation helpers for model selection surfaces.
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Some inference tiers are cheap *because* the vendor trains future models on your prompts and
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completions. Selecting one for the low price without realising the data trade-off is a real footgun.
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Why a static table (not a ProviderProfile hook): the guard runs inside core selection code
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(``auth.py`` / ``web_server.py``), which never calls into the active provider profile for a
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selection-time warning.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Callable, Optional
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@dataclass(frozen=True)
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class DataTrainingWarning:
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"""Confirmation payload for models whose tier trains on user data."""
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model: str
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provider: str
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message: str
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# ── Rule table ────────────────────────────────────────────────────────────
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# Each rule: (human label, predicate over (model_lower, provider_lower), message).
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# Extensible — new data-collection tiers from other vendors slot in here without
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# touching the call sites. Predicates are intentionally conservative: match an
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# explicit, vendor-documented id rather than guessing from price alone (price is
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# only a corroborating signal and can change).
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def _is_meta_contributor(model_lower: str, provider_lower: str) -> bool:
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# Meta Model API "contributor" tier (muse-spark-1.2-contributor and any
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# future -contributor checkpoints). Match on the id suffix; do not require a
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# specific provider id so it fires whether selected via the meta-ai plugin,
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# a gateway, or a custom endpoint that serves the same model id.
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return model_lower.endswith("-contributor") or "contributor" in model_lower.split("-")
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_META_CONTRIBUTOR_MESSAGE = (
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"!!! CONTRIBUTOR TIER — TRAINS ON YOUR DATA !!!\n"
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"\n"
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"muse-spark-1.2-contributor is Meta's contributor tier: heavily discounted\n"
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"token pricing in exchange for permission to use your prompts and completions\n"
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"to train future Meta models.\n"
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"\n"
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" Price per 1M tokens: input $0.10 | output $0.20 | cached input $0.002\n"
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" (vs. standard muse-spark-1.2: input $1.25 | output $4.25 | cached $0.15)\n"
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"\n"
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"It lowers the barrier to entry for prototyping, testing integrations, and\n"
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"scaling experiments where training on your data is acceptable. Do NOT use it\n"
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"for confidential, proprietary, personal, or otherwise sensitive data. For the\n"
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"same model at standard pricing with no training on your data, select the\n"
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"standard variant, muse-spark-1.2.\n"
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"\n"
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"Source: https://dev.meta.ai/docs/pricing-rate-limits/\n"
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"Confirm only if training on your prompts and completions is acceptable."
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)
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# (predicate, message) pairs, evaluated in order; first match wins.
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_RULES: tuple[tuple[Callable[[str, str], bool], str], ...] = (
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(_is_meta_contributor, _META_CONTRIBUTOR_MESSAGE),
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)
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def data_training_warning(
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model_name: str,
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*,
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provider: Optional[str] = None,
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base_url: Optional[str] = None, # noqa: ARG001 — reserved for host-scoped rules
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) -> Optional[DataTrainingWarning]:
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"""Return a warning payload when *model_name* selects a data-training tier.
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Returns ``None`` when no rule matches (the common case). Callers should run this after model
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resolution so aliases / provider-specific ids have settled, and surface ``.message`` as a
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confirm prompt.
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"""
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model = (model_name or "").strip()
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if not model:
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return None
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model_lower = model.lower()
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provider_lower = (provider or "").strip().lower()
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for predicate, message in _RULES:
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try:
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if predicate(model_lower, provider_lower):
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return DataTrainingWarning(
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model=model,
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provider=(provider or "").strip(),
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message=message,
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)
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except Exception:
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# A misbehaving predicate must never break model selection.
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continue
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return None
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