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chore: import upstream snapshot with attribution
2026-07-13 13:00:43 +08:00

37 lines
1.4 KiB
Python

"""Helpers for resolving per-partner LLM model selection."""
from __future__ import annotations
from typing import Any
from deeptutor.services.llm.config import LLMConfig
from deeptutor.services.model_selection import LLMSelection
from deeptutor.services.model_selection.runtime import resolve_llm_config_for_selection
def normalize_partner_llm_selection(value: Any) -> dict[str, str] | None:
"""Return a validated selection dict, or ``None`` for system default."""
selection = LLMSelection.from_payload(value)
return selection.to_dict() if selection else None
def resolve_partner_llm_config(partner_config: Any) -> LLMConfig:
"""Resolve the effective LLM config for a partner config object.
Configs store ``llm_selection`` as a stable catalog reference. Configs
migrated from TutorBot may still carry a raw ``model`` string, which is
applied as a model-only override on top of the system default provider.
"""
selection = normalize_partner_llm_selection(getattr(partner_config, "llm_selection", None))
if selection:
return resolve_llm_config_for_selection(selection)
base = resolve_llm_config_for_selection(None)
legacy_model = str(getattr(partner_config, "model", "") or "").strip()
if legacy_model:
return base.model_copy(update={"model": legacy_model})
return base
__all__ = ["normalize_partner_llm_selection", "resolve_partner_llm_config"]