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- 删除全部 Python 源码 (src/) 及 Alembic 迁移脚本，归档至 _deprecated_py_src/ - 重构 Rust gateway ai_pipeline: 拆分 planner/finalize 模块，新增 contracts/adaptation 层 - 重组 handlers 模块为 admin/public/proxy/internal/shared 子模块结构 - 新增 executor 模块，引入 Rust 原生数据库迁移 (aether-data/migrations) - 简化 CI/Docker 构建流程，移除 base image 二级构建，统一为单一 app image - 移除 Python 相关基础设施文件 (entrypoint.sh, gunicorn_conf.py, Dockerfile.base)
672 lines
28 KiB
Python
672 lines
28 KiB
Python
"""
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Model 映射缓存服务 - 减少模型查询
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架构说明
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========
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本服务采用混合 async/sync 模式：
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- 缓存操作（CacheService）：真正的 async，使用 aioredis
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- 数据库查询（db.query）：同步的 SQLAlchemy Session
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设计决策
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--------
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1. 保持 async 方法签名：因为缓存命中时完全异步，性能最优
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2. 缓存未命中时的同步查询：FastAPI 会在线程池中执行，不会阻塞事件循环
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3. 调用方必须在 async 上下文中使用 await
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使用示例
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--------
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global_model = await ModelCacheService.resolve_global_model_by_name_or_mapping(db, "gpt-4")
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"""
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import time
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from sqlalchemy.orm import Session
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from src.config.constants import CacheTTL
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from src.core.cache_service import CacheService
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from src.core.logger import logger
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from src.core.metrics import (
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model_mapping_conflict_total,
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model_mapping_resolution_duration_seconds,
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model_mapping_resolution_total,
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)
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from src.models.database import GlobalModel, Model
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class ModelCacheService:
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"""Model 映射缓存服务
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提供 GlobalModel 和 Model 的缓存查询功能，减少数据库访问。
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所有公开方法均为 async，需要在 async 上下文中调用。
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"""
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# 缓存 TTL（秒）- 使用统一常量
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CACHE_TTL = CacheTTL.MODEL
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PROVIDER_MAPPING_INDEX_CACHE_KEY = "global_model:resolve_index:provider_model_mappings"
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MODEL_MAPPING_RULES_CACHE_KEY = "global_model:resolve_index:model_mappings"
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@staticmethod
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async def get_model_by_id(db: Session, model_id: str) -> Model | None:
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"""
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获取 Model（带缓存）
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Args:
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db: 数据库会话
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model_id: Model ID
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Returns:
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Model 对象或 None
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"""
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cache_key = f"model:id:{model_id}"
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# 1. 尝试从缓存获取
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cached_data = await CacheService.get(cache_key)
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if cached_data:
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logger.debug(f"Model 缓存命中: {model_id}")
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return ModelCacheService._dict_to_model(cached_data)
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# 2. 缓存未命中，查询数据库
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model = db.query(Model).filter(Model.id == model_id).first()
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# 3. 写入缓存
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if model:
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model_dict = ModelCacheService._model_to_dict(model)
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await CacheService.set(cache_key, model_dict, ttl_seconds=ModelCacheService.CACHE_TTL)
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logger.debug(f"Model 已缓存: {model_id}")
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return model
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@staticmethod
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async def get_global_model_by_id(db: Session, global_model_id: str) -> GlobalModel | None:
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"""
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获取 GlobalModel（带缓存）
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Args:
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db: 数据库会话
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global_model_id: GlobalModel ID
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Returns:
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GlobalModel 对象或 None
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"""
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cache_key = f"global_model:id:{global_model_id}"
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# 1. 尝试从缓存获取
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cached_data = await CacheService.get(cache_key)
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if cached_data:
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logger.debug(f"GlobalModel 缓存命中: {global_model_id}")
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return ModelCacheService._dict_to_global_model(cached_data)
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# 2. 缓存未命中，查询数据库
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global_model = db.query(GlobalModel).filter(GlobalModel.id == global_model_id).first()
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# 3. 写入缓存
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if global_model:
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global_model_dict = ModelCacheService._global_model_to_dict(global_model)
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await CacheService.set(
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cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
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)
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logger.debug(f"GlobalModel 已缓存: {global_model_id}")
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return global_model
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@staticmethod
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async def get_model_by_provider_and_global_model(
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db: Session, provider_id: str, global_model_id: str
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) -> Model | None:
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"""
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通过 Provider ID 和 GlobalModel ID 获取 Model（带缓存）
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Args:
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db: 数据库会话
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provider_id: Provider ID
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global_model_id: GlobalModel ID
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Returns:
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Model 对象或 None
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"""
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cache_key = f"model:provider_global:{provider_id}:{global_model_id}"
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hit_count_key = f"model:provider_global:hits:{provider_id}:{global_model_id}"
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# 1. 尝试从缓存获取
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cached_data = await CacheService.get(cache_key)
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if cached_data:
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logger.debug(
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f"Model 缓存命中(provider+global): {provider_id[:8]}...+{global_model_id[:8]}..."
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)
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# 递增命中计数，同时刷新 TTL
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await CacheService.incr(hit_count_key, ttl_seconds=ModelCacheService.CACHE_TTL)
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return ModelCacheService._dict_to_model(cached_data)
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# 2. 缓存未命中，查询数据库
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model = (
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db.query(Model)
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.filter(
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Model.provider_id == provider_id,
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Model.global_model_id == global_model_id,
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Model.is_active == True,
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)
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.first()
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)
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# 3. 写入缓存
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if model:
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model_dict = ModelCacheService._model_to_dict(model)
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await CacheService.set(cache_key, model_dict, ttl_seconds=ModelCacheService.CACHE_TTL)
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# 重置命中计数（新缓存从1开始）
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await CacheService.set(hit_count_key, 1, ttl_seconds=ModelCacheService.CACHE_TTL)
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logger.debug(
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f"Model 已缓存(provider+global): {provider_id[:8]}...+{global_model_id[:8]}..."
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)
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return model
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@staticmethod
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async def get_global_model_by_name(db: Session, name: str) -> GlobalModel | None:
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"""
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通过名称获取 GlobalModel（带缓存）
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Args:
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db: 数据库会话
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name: GlobalModel 名称
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Returns:
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GlobalModel 对象或 None
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"""
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cache_key = f"global_model:name:{name}"
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# 1. 尝试从缓存获取
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cached_data = await CacheService.get(cache_key)
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if cached_data:
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logger.debug(f"GlobalModel 缓存命中(名称): {name}")
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return ModelCacheService._dict_to_global_model(cached_data)
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# 2. 缓存未命中，查询数据库
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global_model = db.query(GlobalModel).filter(GlobalModel.name == name).first()
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# 3. 写入缓存
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if global_model:
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global_model_dict = ModelCacheService._global_model_to_dict(global_model)
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await CacheService.set(
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cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
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)
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logger.debug(f"GlobalModel 已缓存(名称): {name}")
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return global_model
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@staticmethod
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async def invalidate_model_cache(
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model_id: str,
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provider_id: str | None = None,
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global_model_id: str | None = None,
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provider_model_name: str | None = None,
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provider_model_mappings: list | None = None,
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) -> None:
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"""清除 Model 缓存
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Args:
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model_id: Model ID
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provider_id: Provider ID（用于清除 provider_global 缓存）
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global_model_id: GlobalModel ID（用于清除 provider_global 缓存）
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provider_model_name: provider_model_name（用于清除 resolve 缓存）
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provider_model_mappings: 映射名称列表（用于清除 resolve 缓存）
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"""
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# 清除 model:id 缓存
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await CacheService.delete(f"model:id:{model_id}")
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# 清除 provider_global 缓存及其命中计数（如果提供了必要参数）
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if provider_id and global_model_id:
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await CacheService.delete(f"model:provider_global:{provider_id}:{global_model_id}")
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await CacheService.delete(f"model:provider_global:hits:{provider_id}:{global_model_id}")
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logger.debug(
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f"Model 缓存已清除: {model_id}, provider_global:{provider_id[:8]}...:{global_model_id[:8]}..."
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)
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else:
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logger.debug(f"Model 缓存已清除: {model_id}")
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# 清除 resolve 缓存（provider_model_name 和 mappings 可能都被用作解析 key）
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resolve_keys_to_clear = []
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if provider_model_name:
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resolve_keys_to_clear.append(provider_model_name)
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if provider_model_mappings:
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for mapping_entry in provider_model_mappings:
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if isinstance(mapping_entry, dict):
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mapping_name = mapping_entry.get("name", "").strip()
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if mapping_name:
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resolve_keys_to_clear.append(mapping_name)
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for key in resolve_keys_to_clear:
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await CacheService.delete(f"global_model:resolve:{key}")
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if resolve_keys_to_clear:
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logger.debug(f"Model resolve 缓存已清除: {resolve_keys_to_clear}")
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# provider_model_mappings 更新后，需要重建映射索引缓存。
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await CacheService.delete(ModelCacheService.PROVIDER_MAPPING_INDEX_CACHE_KEY)
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@staticmethod
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async def invalidate_global_model_cache(global_model_id: str, name: str | None = None) -> None:
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"""清除 GlobalModel 缓存"""
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await CacheService.delete(f"global_model:id:{global_model_id}")
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if name:
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await CacheService.delete(f"global_model:name:{name}")
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# 同时清除 resolve 缓存，因为 GlobalModel.name 也是一个 resolve key
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await CacheService.delete(f"global_model:resolve:{name}")
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# 全量清除 resolve 缓存，确保映射规则变更后不命中旧缓存
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try:
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await CacheService.delete_pattern("global_model:resolve:*")
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await CacheService.delete(ModelCacheService.PROVIDER_MAPPING_INDEX_CACHE_KEY)
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await CacheService.delete(ModelCacheService.MODEL_MAPPING_RULES_CACHE_KEY)
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except Exception as e:
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logger.error(f"GlobalModel resolve 缓存清除失败，可能导致映射不一致: {e}")
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logger.debug(f"GlobalModel 缓存已清除: {global_model_id}")
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@staticmethod
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async def invalidate_all_resolve_cache() -> None:
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"""
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清除所有 GlobalModel 解析缓存
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在 Provider 启用/禁用时调用，因为 Provider 状态变更会影响模型解析结果。
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"""
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try:
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deleted = await CacheService.delete_pattern("global_model:resolve:*")
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await CacheService.delete(ModelCacheService.PROVIDER_MAPPING_INDEX_CACHE_KEY)
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await CacheService.delete(ModelCacheService.MODEL_MAPPING_RULES_CACHE_KEY)
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logger.debug(f"已清除 {deleted} 个 GlobalModel resolve 缓存")
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except Exception as e:
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logger.error(f"GlobalModel resolve 缓存清除失败: {e}")
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@staticmethod
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async def _get_provider_mapping_index(
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db: Session,
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) -> dict[str, list[dict[str, object]]]:
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cached_data = await CacheService.get(ModelCacheService.PROVIDER_MAPPING_INDEX_CACHE_KEY)
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if isinstance(cached_data, dict):
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return {
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str(name): value
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for name, value in cached_data.items()
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if isinstance(name, str) and isinstance(value, list)
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}
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from src.models.database import Provider
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rows = (
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db.query(Model, GlobalModel)
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.join(Provider, Model.provider_id == Provider.id)
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.join(GlobalModel, Model.global_model_id == GlobalModel.id)
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.filter(
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Provider.is_active == True,
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Model.is_active == True,
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GlobalModel.is_active == True,
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Model.provider_model_mappings.isnot(None),
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)
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.all()
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)
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index: dict[str, list[dict[str, object]]] = {}
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seen_pairs: set[tuple[str, str]] = set()
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for model, global_model in rows:
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raw_mappings = getattr(model, "provider_model_mappings", None)
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if not isinstance(raw_mappings, list):
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continue
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global_model_dict = ModelCacheService._global_model_to_dict(global_model)
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for raw in raw_mappings:
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if not isinstance(raw, dict):
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continue
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name = raw.get("name")
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if not isinstance(name, str):
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continue
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normalized_name = name.strip()
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if not normalized_name:
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continue
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pair_key = (normalized_name, str(global_model.id))
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if pair_key in seen_pairs:
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continue
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seen_pairs.add(pair_key)
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index.setdefault(normalized_name, []).append(global_model_dict)
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await CacheService.set(
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ModelCacheService.PROVIDER_MAPPING_INDEX_CACHE_KEY,
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index,
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ttl_seconds=ModelCacheService.CACHE_TTL,
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)
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return index
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@staticmethod
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async def _get_model_mapping_rules(
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db: Session,
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) -> list[dict[str, object]]:
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cached_data = await CacheService.get(ModelCacheService.MODEL_MAPPING_RULES_CACHE_KEY)
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if isinstance(cached_data, list):
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return [entry for entry in cached_data if isinstance(entry, dict)]
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rows = (
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db.query(GlobalModel)
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.filter(GlobalModel.is_active == True, GlobalModel.config.isnot(None))
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.all()
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)
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rules: list[dict[str, object]] = []
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for global_model in rows:
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config = getattr(global_model, "config", None) or {}
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mappings = config.get("model_mappings")
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if not isinstance(mappings, list) or not mappings:
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continue
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patterns = [
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pattern for pattern in mappings if isinstance(pattern, str) and pattern.strip()
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]
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if not patterns:
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continue
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rules.append(
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{
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"global_model": ModelCacheService._global_model_to_dict(global_model),
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"patterns": patterns,
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}
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)
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await CacheService.set(
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ModelCacheService.MODEL_MAPPING_RULES_CACHE_KEY,
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rules,
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ttl_seconds=ModelCacheService.CACHE_TTL,
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)
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return rules
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@staticmethod
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async def resolve_global_model_by_name_or_mapping(
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db: Session, model_name: str
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) -> GlobalModel | None:
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"""
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通过名称解析 GlobalModel（带缓存）
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查找顺序：
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1. 检查缓存
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2. 直接匹配 GlobalModel.name
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3. 通过 provider_model_name 匹配（查询 Model 表）
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4. 通过 provider_model_mappings 匹配（查询 Model 表）
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5. 通过 GlobalModel.config.model_mappings 匹配（支持正则）
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注意：provider_model_mappings 是 Provider 级别的映射配置，可能存在跨 Provider 冲突；
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如匹配到多个 GlobalModel，将记录告警并选择第一个匹配结果。
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Args:
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db: 数据库会话
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model_name: 模型名称（可以是 GlobalModel.name 或 provider_model_name）
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Returns:
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GlobalModel 对象或 None
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"""
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start_time = time.time()
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resolution_method = "not_found"
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cache_hit = False
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normalized_name = model_name.strip()
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if not normalized_name:
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return None
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cache_key = f"global_model:resolve:{normalized_name}"
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try:
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# 1. 尝试从缓存获取
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cached_data = await CacheService.get(cache_key)
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if cached_data:
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if cached_data == "NOT_FOUND":
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# 缓存的负结果
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cache_hit = True
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resolution_method = "not_found"
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logger.debug(f"GlobalModel 缓存命中(映射解析-未找到): {normalized_name}")
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return None
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if isinstance(cached_data, dict) and "supported_capabilities" not in cached_data:
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# 兼容旧缓存：字段不全时视为未命中，走 DB 刷新
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logger.debug(f"GlobalModel 缓存命中但 schema 过旧，刷新: {normalized_name}")
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else:
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cache_hit = True
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resolution_method = "direct_match" # 缓存命中时无法区分原始解析方式
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logger.debug(f"GlobalModel 缓存命中(映射解析): {normalized_name}")
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return ModelCacheService._dict_to_global_model(cached_data)
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# 2. 直接通过 GlobalModel.name 匹配（优先级最高）
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# 说明：如果存在同名 GlobalModel，应优先解析为 GlobalModel 本身，
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# 避免被某个 Provider 的 provider_model_name 误导导致解析到错误的 GlobalModel。
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global_model = (
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db.query(GlobalModel)
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.filter(GlobalModel.name == normalized_name, GlobalModel.is_active == True)
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.first()
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)
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if global_model:
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resolution_method = "direct_match"
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global_model_dict = ModelCacheService._global_model_to_dict(global_model)
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await CacheService.set(
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cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
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)
|
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logger.debug(f"GlobalModel 已缓存(映射解析-直接匹配): {normalized_name}")
|
||
return global_model
|
||
|
||
# 3. 通过 provider_model_name 匹配
|
||
from src.models.database import Provider
|
||
|
||
models_with_global = (
|
||
db.query(Model, GlobalModel)
|
||
.join(Provider, Model.provider_id == Provider.id)
|
||
.join(GlobalModel, Model.global_model_id == GlobalModel.id)
|
||
.filter(
|
||
Provider.is_active == True,
|
||
Model.is_active == True,
|
||
GlobalModel.is_active == True,
|
||
Model.provider_model_name == normalized_name,
|
||
)
|
||
.all()
|
||
)
|
||
|
||
# 收集匹配的 GlobalModel（只通过 provider_model_name 匹配）
|
||
matched_global_models: list[GlobalModel] = []
|
||
seen_global_model_ids: set[str] = set()
|
||
for model, gm in models_with_global:
|
||
if gm.id not in seen_global_model_ids:
|
||
seen_global_model_ids.add(gm.id)
|
||
matched_global_models.append(gm)
|
||
logger.debug(
|
||
f"模型名称 '{normalized_name}' 通过 provider_model_name 匹配到 "
|
||
f"GlobalModel: {gm.name} (Model: {model.id[:8]}...)"
|
||
)
|
||
|
||
# 如果通过 provider_model_name 找到了，返回
|
||
if matched_global_models:
|
||
resolution_method = "provider_model_name"
|
||
|
||
if len(matched_global_models) > 1:
|
||
# 检测到冲突（多个不同的 GlobalModel 有相同的 provider_model_name）
|
||
model_names = [gm.name for gm in matched_global_models if gm.name]
|
||
logger.warning(
|
||
f"模型映射冲突: 名称 '{normalized_name}' 匹配到多个不同的 GlobalModel: "
|
||
f"{', '.join(model_names)}，使用第一个匹配结果"
|
||
)
|
||
# 记录冲突指标
|
||
model_mapping_conflict_total.inc()
|
||
|
||
# 返回第一个匹配的 GlobalModel
|
||
result_global_model = matched_global_models[0]
|
||
global_model_dict = ModelCacheService._global_model_to_dict(result_global_model)
|
||
await CacheService.set(
|
||
cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
|
||
)
|
||
logger.debug(
|
||
f"GlobalModel 已缓存(映射解析-{resolution_method}): {normalized_name} -> {result_global_model.name}"
|
||
)
|
||
return result_global_model
|
||
|
||
# 4. 通过 provider_model_mappings 匹配
|
||
provider_mapping_index = await ModelCacheService._get_provider_mapping_index(db)
|
||
mapping_matched_global_models = [
|
||
ModelCacheService._dict_to_global_model(global_model_dict)
|
||
for global_model_dict in provider_mapping_index.get(normalized_name, [])
|
||
if isinstance(global_model_dict, dict)
|
||
]
|
||
|
||
for gm in mapping_matched_global_models:
|
||
logger.debug(
|
||
f"模型名称 '{normalized_name}' 通过 provider_model_mappings 匹配到 "
|
||
f"GlobalModel: {gm.name}"
|
||
)
|
||
|
||
if mapping_matched_global_models:
|
||
resolution_method = "provider_model_mappings"
|
||
|
||
if len(mapping_matched_global_models) > 1:
|
||
model_names = [gm.name for gm in mapping_matched_global_models if gm.name]
|
||
logger.warning(
|
||
f"模型映射冲突: 名称 '{normalized_name}' 匹配到多个不同的 GlobalModel: "
|
||
f"{', '.join(model_names)}，使用第一个匹配结果"
|
||
)
|
||
model_mapping_conflict_total.inc()
|
||
|
||
# 按名称排序确保确定性
|
||
result_global_model = sorted(
|
||
mapping_matched_global_models, key=lambda gm: gm.name or ""
|
||
)[0]
|
||
global_model_dict = ModelCacheService._global_model_to_dict(result_global_model)
|
||
await CacheService.set(
|
||
cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
|
||
)
|
||
logger.debug(
|
||
f"GlobalModel 已缓存(映射解析-{resolution_method}): "
|
||
f"{normalized_name} -> {result_global_model.name}"
|
||
)
|
||
return result_global_model
|
||
|
||
# 5. 通过 GlobalModel.config.model_mappings 匹配（支持正则）
|
||
from src.core.model_permissions import match_model_with_pattern
|
||
|
||
mapping_matches: list[GlobalModel] = []
|
||
for entry in await ModelCacheService._get_model_mapping_rules(db):
|
||
global_model_dict = entry.get("global_model")
|
||
patterns = entry.get("patterns")
|
||
if not isinstance(global_model_dict, dict) or not isinstance(patterns, list):
|
||
continue
|
||
for pattern in patterns:
|
||
if isinstance(pattern, str) and match_model_with_pattern(
|
||
pattern, normalized_name
|
||
):
|
||
mapping_matches.append(
|
||
ModelCacheService._dict_to_global_model(global_model_dict)
|
||
)
|
||
break
|
||
|
||
if mapping_matches:
|
||
resolution_method = "model_mappings"
|
||
|
||
if len(mapping_matches) > 1:
|
||
model_names = [gm.name for gm in mapping_matches if gm.name]
|
||
logger.warning(
|
||
f"模型映射冲突: 名称 '{normalized_name}' 匹配到多个不同的 GlobalModel: "
|
||
f"{', '.join(model_names)}，使用第一个匹配结果"
|
||
)
|
||
model_mapping_conflict_total.inc()
|
||
|
||
# 按名称排序确保确定性
|
||
result_global_model = sorted(mapping_matches, key=lambda gm: gm.name or "")[0]
|
||
global_model_dict = ModelCacheService._global_model_to_dict(result_global_model)
|
||
await CacheService.set(
|
||
cache_key, global_model_dict, ttl_seconds=ModelCacheService.CACHE_TTL
|
||
)
|
||
logger.debug(
|
||
f"GlobalModel 已缓存(映射解析-{resolution_method}): "
|
||
f"{normalized_name} -> {result_global_model.name}"
|
||
)
|
||
return result_global_model
|
||
|
||
# 6. 完全未找到
|
||
resolution_method = "not_found"
|
||
# 未找到匹配，缓存负结果
|
||
await CacheService.set(cache_key, "NOT_FOUND", ttl_seconds=ModelCacheService.CACHE_TTL)
|
||
logger.debug(f"GlobalModel 未找到(映射解析): {normalized_name}")
|
||
return None
|
||
|
||
finally:
|
||
# 记录监控指标
|
||
duration = time.time() - start_time
|
||
model_mapping_resolution_total.labels(
|
||
method=resolution_method, cache_hit=str(cache_hit).lower()
|
||
).inc()
|
||
model_mapping_resolution_duration_seconds.labels(method=resolution_method).observe(
|
||
duration
|
||
)
|
||
|
||
@staticmethod
|
||
def _model_to_dict(model: Model) -> dict:
|
||
"""将 Model 对象转换为字典"""
|
||
return {
|
||
"id": model.id,
|
||
"provider_id": model.provider_id,
|
||
"global_model_id": model.global_model_id,
|
||
"provider_model_name": model.provider_model_name,
|
||
"provider_model_mappings": getattr(model, "provider_model_mappings", None),
|
||
"is_active": model.is_active,
|
||
"is_available": model.is_available if hasattr(model, "is_available") else True,
|
||
"price_per_request": (
|
||
float(model.price_per_request) if model.price_per_request is not None else None
|
||
),
|
||
"tiered_pricing": model.tiered_pricing,
|
||
"supports_vision": model.supports_vision,
|
||
"supports_function_calling": model.supports_function_calling,
|
||
"supports_streaming": model.supports_streaming,
|
||
"supports_extended_thinking": model.supports_extended_thinking,
|
||
"supports_image_generation": getattr(model, "supports_image_generation", None),
|
||
"config": model.config,
|
||
}
|
||
|
||
@staticmethod
|
||
def _dict_to_model(model_dict: dict) -> Model:
|
||
"""从字典重建 Model 对象"""
|
||
model = Model(
|
||
id=model_dict["id"],
|
||
provider_id=model_dict["provider_id"],
|
||
global_model_id=model_dict["global_model_id"],
|
||
provider_model_name=model_dict["provider_model_name"],
|
||
provider_model_mappings=model_dict.get("provider_model_mappings"),
|
||
is_active=model_dict["is_active"],
|
||
is_available=model_dict.get("is_available", True),
|
||
price_per_request=model_dict.get("price_per_request"),
|
||
tiered_pricing=model_dict.get("tiered_pricing"),
|
||
supports_vision=model_dict.get("supports_vision"),
|
||
supports_function_calling=model_dict.get("supports_function_calling"),
|
||
supports_streaming=model_dict.get("supports_streaming"),
|
||
supports_extended_thinking=model_dict.get("supports_extended_thinking"),
|
||
supports_image_generation=model_dict.get("supports_image_generation"),
|
||
config=model_dict.get("config"),
|
||
)
|
||
return model
|
||
|
||
@staticmethod
|
||
def _global_model_to_dict(global_model: GlobalModel) -> dict:
|
||
"""将 GlobalModel 对象转换为字典"""
|
||
return {
|
||
"id": global_model.id,
|
||
"name": global_model.name,
|
||
"display_name": global_model.display_name,
|
||
"supported_capabilities": global_model.supported_capabilities,
|
||
"config": global_model.config,
|
||
"default_tiered_pricing": global_model.default_tiered_pricing,
|
||
"default_price_per_request": (
|
||
float(global_model.default_price_per_request)
|
||
if global_model.default_price_per_request is not None
|
||
else None
|
||
),
|
||
"is_active": global_model.is_active,
|
||
}
|
||
|
||
@staticmethod
|
||
def _dict_to_global_model(global_model_dict: dict) -> GlobalModel:
|
||
"""从字典重建 GlobalModel 对象"""
|
||
global_model = GlobalModel(
|
||
id=global_model_dict["id"],
|
||
name=global_model_dict["name"],
|
||
display_name=global_model_dict.get("display_name"),
|
||
supported_capabilities=global_model_dict.get("supported_capabilities") or [],
|
||
config=global_model_dict.get("config"),
|
||
default_tiered_pricing=global_model_dict.get("default_tiered_pricing"),
|
||
default_price_per_request=global_model_dict.get("default_price_per_request"),
|
||
is_active=global_model_dict.get("is_active", True),
|
||
)
|
||
return global_model
|