wuyang
62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Literal
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Mode = Literal["chat", "work"]
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Strength = Literal["light", "medium", "high"]
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@dataclass(frozen=True, slots=True)
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class ModelSpec:
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public_id: str
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display_name: str
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mode: Mode
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strength: Strength
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provider_model: str
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max_iterations: int
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context_char_budget: int
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_TIERS = {
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"light": ("轻度", "ChatGPT-5.6:Luna", 8, 120_000),
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"medium": ("中", "ChatGPT-5.6:Terra", 16, 240_000),
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"high": ("高", "ChatGPT-5.6:Sol", 24, 400_000),
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}
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MODEL_SPECS: dict[str, ModelSpec] = {
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f"{mode}-{strength}": ModelSpec(
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public_id=f"{mode}-{strength}",
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display_name=f"{'Chat' if mode == 'chat' else 'Work'} · {label}",
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mode=mode,
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strength=strength,
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provider_model=provider,
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max_iterations=max_iterations,
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context_char_budget=context_budget,
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)
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for mode in ("chat", "work")
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for strength, (label, provider, max_iterations, context_budget) in _TIERS.items()
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}
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def get_model_spec(model_id: str) -> ModelSpec:
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try:
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return MODEL_SPECS[model_id]
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except KeyError as exc:
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raise ValueError(f"Unsupported model: {model_id}") from exc
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def openai_model_list() -> dict:
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return {
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"object": "list",
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"data": [
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{
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"id": spec.public_id,
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"object": "model",
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"owned_by": "k1412-agent",
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"name": spec.display_name,
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}
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for spec in MODEL_SPECS.values()
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],
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}
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