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

55 lines
1.9 KiB
Python

"""Runtime helpers for request-scoped model selection."""
from __future__ import annotations
from contextvars import Token
from typing import Any
from deeptutor.services.config.provider_runtime import ResolvedLLMConfig, resolve_llm_runtime_config
from deeptutor.services.llm import config as llm_config_module
from deeptutor.services.llm.config import LLMConfig
def llm_config_from_resolved(resolved: ResolvedLLMConfig) -> LLMConfig:
"""Convert provider-runtime output into the LLM service config shape."""
return LLMConfig(
model=resolved.model,
api_key=resolved.api_key,
base_url=resolved.base_url,
effective_url=resolved.effective_url,
binding=resolved.binding,
provider_name=resolved.provider_name,
provider_mode=resolved.provider_mode,
api_version=resolved.api_version,
extra_headers=resolved.extra_headers,
reasoning_effort=resolved.reasoning_effort,
context_window=resolved.context_window,
)
def resolve_llm_config_for_selection(selection: Any) -> LLMConfig:
"""Resolve the LLM config for a chat/session selection reference."""
if selection is None:
return llm_config_module.get_llm_config()
return llm_config_from_resolved(resolve_llm_runtime_config(llm_selection=selection))
def activate_llm_selection(selection: Any) -> tuple[LLMConfig, Token[LLMConfig | None]]:
"""Resolve and install a scoped LLM config for the current async context."""
config = resolve_llm_config_for_selection(selection)
token = llm_config_module.set_scoped_llm_config(config)
return config, token
def reset_llm_selection(token: Token[LLMConfig | None] | None) -> None:
if token is not None:
llm_config_module.reset_scoped_llm_config(token)
__all__ = [
"activate_llm_selection",
"llm_config_from_resolved",
"reset_llm_selection",
"resolve_llm_config_for_selection",
]