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https://github.com/fawney19/Aether.git
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feat: 新增 OAuth 密钥专用编辑对话框 & 统一预设模型管理
- 新增 OAuthKeyEditDialog 组件，支持编辑 OAuth 账号的名称、备注、优先级、RPM 限制、缓存 TTL、熔断探测等配置 - OAuth 账号现在也支持自动获取模型功能，包含模型过滤规则配置 - 移除 OAuth 密钥编辑/权限按钮的 v-if 限制，统一操作入口 - 新增 preset_models.py 模块，统一管理 Kiro/Codex 等无 /v1/models 端点的预设模型 - 重构 Kiro 和 Codex 插件，改用统一的 create_preset_models_fetcher 工厂函数
This commit is contained in:
AAEE86
@@ -18,37 +18,13 @@ from urllib.parse import urlencode
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from src.core.logger import logger
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# ---------------------------------------------------------------------------
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# Fixed model catalog
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# Preset model catalog
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# ---------------------------------------------------------------------------
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# Codex upstream (chatgpt.com/backend-api/codex) has no /v1/models endpoint.
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# Return a static list of known models.
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_CODEX_MODELS: list[dict[str, Any]] = [
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{
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"id": "gpt-5.2",
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"object": "model",
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"owned_by": "openai",
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"display_name": "gpt-5.2",
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},
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{
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"id": "gpt-5.2-codex",
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"object": "model",
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"owned_by": "openai",
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"display_name": "gpt-5.2-codex",
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},
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]
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# We use the unified preset models registry from preset_models.py.
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from src.services.provider.preset_models import create_preset_models_fetcher
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async def fetch_models_codex(
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ctx: Any,
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timeout_seconds: float, # noqa: ARG001
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) -> tuple[list[dict], list[str], bool, dict[str, Any] | None]:
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"""Return a fixed model catalog for Codex.
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Codex upstream does not expose a ``/v1/models`` endpoint, so we skip the
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HTTP call entirely and return a hardcoded list.
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"""
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_ = ctx
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return list(_CODEX_MODELS), [], True, None
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fetch_models_codex = create_preset_models_fetcher("codex")
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# ---------------------------------------------------------------------------
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@@ -23,49 +23,13 @@ from src.services.provider.adapters.kiro.constants import (
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from src.services.provider.adapters.kiro.context import get_kiro_request_context
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# ---------------------------------------------------------------------------
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# Fixed model catalog
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# Preset model catalog
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# ---------------------------------------------------------------------------
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# Kiro upstream has no /v1/models endpoint. We return a static list matching
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# the models accepted by map_model() in converter.py.
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_KIRO_MODELS: list[dict[str, Any]] = [
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{
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"id": "claude-sonnet-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Sonnet 4.5",
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},
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{
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"id": "claude-opus-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Opus 4.5",
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},
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{
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"id": "claude-opus-4.6",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Opus 4.6",
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},
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{
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"id": "claude-haiku-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Haiku 4.5",
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},
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]
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# Kiro upstream has no /v1/models endpoint. We use the unified preset models
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# registry from preset_models.py.
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from src.services.provider.preset_models import create_preset_models_fetcher
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async def fetch_models_kiro(
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ctx: Any,
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timeout_seconds: float, # noqa: ARG001
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) -> tuple[list[dict], list[str], bool, dict[str, Any] | None]:
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"""Return a fixed model catalog for Kiro.
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Kiro upstream does not expose a ``/v1/models`` endpoint, so we skip the
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HTTP call entirely and return a hardcoded list.
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"""
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_ = ctx # not needed — no upstream call
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return list(_KIRO_MODELS), [], True, None
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fetch_models_kiro = create_preset_models_fetcher("kiro")
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# ---------------------------------------------------------------------------
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158
src/services/provider/preset_models.py
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158
src/services/provider/preset_models.py
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@@ -0,0 +1,158 @@
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"""预设模型管理模块
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为不支持自动获取模型的反代提供商（如 Kiro、Codex）提供统一的预设模型管理。
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使用方式：
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1. 在 PRESET_MODELS 中定义各 provider_type 的预设模型列表
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2. 在 plugin.py 中调用 create_preset_models_fetcher() 创建 fetcher 函数
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3. 将 fetcher 注册到 UpstreamModelsFetcherRegistry
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"""
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from __future__ import annotations
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from typing import Any
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# ---------------------------------------------------------------------------
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# 预设模型定义
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# ---------------------------------------------------------------------------
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# 各 provider_type 对应的预设模型列表
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# 格式: provider_type -> list of model dicts
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PRESET_MODELS: dict[str, list[dict[str, Any]]] = {
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# Kiro (Claude CLI 反代)
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"kiro": [
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{
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"id": "claude-sonnet-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Sonnet 4.5",
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},
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{
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"id": "claude-opus-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Opus 4.5",
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},
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{
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"id": "claude-opus-4.6",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Opus 4.6",
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},
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{
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"id": "claude-haiku-4.5",
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"object": "model",
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"owned_by": "anthropic",
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"display_name": "Claude Haiku 4.5",
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},
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],
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# Codex (OpenAI CLI 反代)
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"codex": [
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{
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"id": "gpt-5",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5",
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},
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{
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"id": "gpt-5-codex",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5 Codex",
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},
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{
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"id": "gpt-5-codex-mini",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5 Codex Mini",
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},
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{
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"id": "gpt-5.1",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.1",
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},
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{
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"id": "gpt-5.1-codex",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.1 Codex",
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},
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{
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"id": "gpt-5.1-codex-mini",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.1 Codex Mini",
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},
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{
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"id": "gpt-5.1-codex-max",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.1 Codex Max",
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},
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{
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"id": "gpt-5.2",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.2",
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},
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{
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"id": "gpt-5.2-codex",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.2 Codex",
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},
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{
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"id": "gpt-5.3-codex",
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"object": "model",
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"owned_by": "openai",
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"display_name": "GPT-5.3 Codex",
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},
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],
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}
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# ---------------------------------------------------------------------------
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# Fetcher 工厂函数
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# ---------------------------------------------------------------------------
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def get_preset_models(provider_type: str) -> list[dict[str, Any]]:
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"""获取指定 provider_type 的预设模型列表。"""
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return list(PRESET_MODELS.get(provider_type.lower(), []))
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def create_preset_models_fetcher(
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provider_type: str,
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) -> Any:
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"""创建一个返回预设模型列表的 fetcher 函数。
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Args:
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provider_type: 提供商类型（如 "kiro", "codex"）
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Returns:
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符合 UpstreamModelsFetcherRegistry 签名的 async fetcher 函数
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"""
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models = get_preset_models(provider_type)
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async def fetch_preset_models(
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ctx: Any,
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timeout_seconds: float, # noqa: ARG001
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) -> tuple[list[dict], list[str], bool, dict[str, Any] | None]:
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"""Return preset model catalog.
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This provider does not expose a /v1/models endpoint, so we skip the
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HTTP call entirely and return a hardcoded list.
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"""
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_ = ctx
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_ = timeout_seconds
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return list(models), [], True, None
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return fetch_preset_models
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__all__ = [
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"PRESET_MODELS",
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"create_preset_models_fetcher",
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"get_preset_models",
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]
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