212 lines
7.9 KiB
Python
212 lines
7.9 KiB
Python
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from sqlalchemy.orm import Session
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from app.core.config import settings
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from app.models.model_config import UserModelConfig
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from app.schemas.model_config import ModelConfigCreate, ModelConfigUpdate
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PROVIDER_OPTIONS: List[Dict[str, Any]] = [
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{
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"provider": "dashscope",
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"label": "DashScope",
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"default_api_base": settings.DASH_SCOPE_API_BASE,
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"default_chat_model": settings.DASH_SCOPE_CHAT_MODEL or "qwen3-max",
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"default_embedding_model": settings.DASH_SCOPE_EMBEDDINGS_MODEL or "text-embedding-v4",
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"chat_models": ["qwen3-max", "qwen-plus", "qwen-turbo", "qwen-max"],
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"embedding_models": ["text-embedding-v4", "text-embedding-v3", "text-embedding-v2"],
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"requires_api_key": True,
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"supports_custom_api_base": True,
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},
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{
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"provider": "openai",
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"label": "OpenAI",
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"default_api_base": settings.OPENAI_API_BASE,
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"default_chat_model": settings.OPENAI_MODEL or "gpt-4o",
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"default_embedding_model": settings.OPENAI_EMBEDDINGS_MODEL or "text-embedding-3-small",
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"chat_models": ["gpt-4o", "gpt-4o-mini", "gpt-4.1", "gpt-4.1-mini"],
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"embedding_models": ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"],
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"requires_api_key": True,
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"supports_custom_api_base": True,
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},
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{
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"provider": "openai_compatible",
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"label": "OpenAI Compatible",
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"default_api_base": "",
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"default_chat_model": "qwen3-max",
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"default_embedding_model": "text-embedding-v4",
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"chat_models": ["qwen3-max", "deepseek-chat", "gpt-4o-mini"],
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"embedding_models": ["text-embedding-v4", "text-embedding-3-small"],
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"requires_api_key": True,
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"supports_custom_api_base": True,
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},
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{
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"provider": "ollama",
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"label": "Ollama",
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"default_api_base": settings.OLLAMA_API_BASE,
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"default_chat_model": settings.OLLAMA_MODEL,
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"default_embedding_model": settings.OLLAMA_EMBEDDINGS_MODEL,
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"chat_models": [settings.OLLAMA_MODEL, "llama3.1", "qwen2.5", "deepseek-r1:7b"],
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"embedding_models": [settings.OLLAMA_EMBEDDINGS_MODEL, "nomic-embed-text", "mxbai-embed-large"],
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"requires_api_key": False,
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"supports_custom_api_base": True,
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},
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]
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def provider_options_response() -> Dict[str, Any]:
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first = PROVIDER_OPTIONS[0]
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return {
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"providers": PROVIDER_OPTIONS,
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"defaults": {
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"provider": first["provider"],
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"api_base": first["default_api_base"],
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"chat_model": first["default_chat_model"],
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"embedding_model": first["default_embedding_model"],
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},
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}
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def _provider_option(provider: str) -> Dict[str, Any]:
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normalized = (provider or "dashscope").strip().lower()
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for option in PROVIDER_OPTIONS:
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if option["provider"] == normalized:
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return option
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raise ValueError(f"Unsupported model provider: {provider}")
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def _normalized_payload(payload: Dict[str, Any], existing: Optional[UserModelConfig] = None) -> Dict[str, Any]:
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provider = str(payload.get("provider") or getattr(existing, "provider", "dashscope")).strip().lower()
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option = _provider_option(provider)
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api_base = payload.get("api_base")
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if api_base is None and existing is not None:
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api_base = existing.api_base
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if not api_base:
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api_base = option["default_api_base"]
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if option["supports_custom_api_base"] and provider == "openai_compatible" and not api_base:
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raise ValueError("OpenAI Compatible provider requires an API base URL.")
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chat_model = str(payload.get("chat_model") or getattr(existing, "chat_model", "") or option["default_chat_model"]).strip()
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embedding_model = str(
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payload.get("embedding_model")
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or getattr(existing, "embedding_model", "")
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or option["default_embedding_model"]
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).strip()
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if not chat_model:
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raise ValueError("Chat model is required.")
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if not embedding_model:
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raise ValueError("Embedding model is required.")
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api_key = payload.get("api_key")
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if api_key is None and existing is not None:
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api_key = existing.api_key
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api_key = str(api_key or "").strip()
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if option["requires_api_key"] and not api_key:
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raise ValueError("API key is required for this provider.")
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name = payload.get("name")
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if name is None and existing is not None:
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name = existing.name
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name = str(name or "").strip()
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if not name:
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name = option["label"]
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return {
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"name": name,
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"provider": provider,
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"api_key": api_key,
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"api_base": str(api_base).strip() if api_base else None,
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"chat_model": chat_model,
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"embedding_model": embedding_model,
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"is_active": bool(payload.get("is_active", getattr(existing, "is_active", True))),
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}
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class ModelConfigService:
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@staticmethod
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def list_configs(db: Session, user_id: int) -> List[UserModelConfig]:
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return (
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db.query(UserModelConfig)
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.filter(UserModelConfig.user_id == user_id)
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.order_by(UserModelConfig.is_active.desc(), UserModelConfig.updated_at.desc())
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.all()
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)
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@staticmethod
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def get_config(db: Session, user_id: int, config_id: int) -> Optional[UserModelConfig]:
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return (
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db.query(UserModelConfig)
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.filter(UserModelConfig.id == config_id, UserModelConfig.user_id == user_id)
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.first()
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)
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@staticmethod
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def get_active_config(db: Session, user_id: int) -> Optional[UserModelConfig]:
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return (
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db.query(UserModelConfig)
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.filter(UserModelConfig.user_id == user_id, UserModelConfig.is_active.is_(True))
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.order_by(UserModelConfig.updated_at.desc())
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.first()
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)
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@staticmethod
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def require_active_config(db: Session, user_id: int) -> UserModelConfig:
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config = ModelConfigService.get_active_config(db, user_id)
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if config is None:
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raise ValueError("请先在 API 密钥页面新增并启用模型配置。")
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return config
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@staticmethod
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def create_config(db: Session, user_id: int, payload: ModelConfigCreate) -> UserModelConfig:
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data = _normalized_payload(payload.model_dump())
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if data["is_active"]:
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ModelConfigService._deactivate_user_configs(db, user_id)
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item = UserModelConfig(user_id=user_id, **data)
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db.add(item)
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db.commit()
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db.refresh(item)
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return item
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@staticmethod
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def update_config(
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db: Session,
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item: UserModelConfig,
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payload: ModelConfigUpdate,
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) -> UserModelConfig:
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raw = payload.model_dump(exclude_unset=True)
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if raw.get("api_key") == "":
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raw.pop("api_key")
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data = _normalized_payload(raw, existing=item)
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if data["is_active"]:
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ModelConfigService._deactivate_user_configs(db, item.user_id, exclude_id=item.id)
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for field, value in data.items():
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setattr(item, field, value)
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db.add(item)
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db.commit()
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db.refresh(item)
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return item
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@staticmethod
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def delete_config(db: Session, item: UserModelConfig) -> None:
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db.delete(item)
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db.commit()
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@staticmethod
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def touch_last_used(db: Session, item: UserModelConfig) -> UserModelConfig:
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item.last_used_at = datetime.utcnow()
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db.add(item)
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db.commit()
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db.refresh(item)
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return item
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@staticmethod
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def _deactivate_user_configs(db: Session, user_id: int, exclude_id: Optional[int] = None) -> None:
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query = db.query(UserModelConfig).filter(UserModelConfig.user_id == user_id)
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if exclude_id is not None:
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query = query.filter(UserModelConfig.id != exclude_id)
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query.update({UserModelConfig.is_active: False}, synchronize_session=False)
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