# ruff: noqa: E501 import asyncio import os import re from typing import Any import httpx from sqlalchemy.ext.asyncio import AsyncSession from yuxi.repositories.model_provider_repository import ( create_model_provider, delete_model_provider, get_model_provider, list_model_providers, update_model_provider, ) from yuxi.storage.postgres.models_business import ModelProvider VALID_MODEL_TYPES = {"chat", "embedding", "rerank"} VALID_MODEL_SOURCES = {"manual", "remote"} VALID_PROVIDER_TYPES = {"openai", "anthropic", "gemini", "ollama", "openrouter", "lmstudio"} _PROVIDER_ID_RE = re.compile(r"^[a-zA-Z0-9][a-zA-Z0-9_-]{1,99}$") DEFAULT_MODELS_ENDPOINT = "" DEFAULT_EMBEDDING_MODELS_ENDPOINT = "" _DEFAULT_BUILTIN_PROVIDERS: list[dict[str, Any]] = [ { "provider_id": "openai", "display_name": "OpenAI", "base_url": "https://api.openai.com/v1", "api_key_env": "OPENAI_API_KEY", "models_endpoint": "https://api.openai.com/v1/models", }, { "provider_id": "anthropic", "display_name": "Anthropic", "base_url": "https://api.anthropic.com", "api_key_env": "ANTHROPIC_API_KEY", "models_endpoint": "https://api.anthropic.com/models", }, { "provider_id": "google", "display_name": "Google Gemini", "base_url": "https://generativelanguage.googleapis.com", "api_key_env": "GEMINI_API_KEY", "models_endpoint": "https://generativelanguage.googleapis.com/v1beta/models", }, { "provider_id": "ollama-cloud", "display_name": "Ollama", "base_url": "http://localhost:11434", "models_endpoint": "http://localhost:11434/api/tags", }, { "provider_id": "lmstudio", "display_name": "LM Studio", "base_url": "http://localhost:1234/v1", "models_endpoint": "http://localhost:1234/v1/models", }, { "provider_id": "deepseek", "display_name": "DeepSeek", "base_url": "https://api.deepseek.com", "api_key_env": "DEEPSEEK_API_KEY", "models_endpoint": "https://api.deepseek.com/models", }, { "provider_id": "alibaba", "display_name": "DashScope", "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", "api_key_env": "DASHSCOPE_API_KEY", "models_endpoint": "https://dashscope.aliyuncs.com/compatible-mode/v1/models", }, { "provider_id": "alibaba-coding-plan-cn", "display_name": "Aliyun Coding Plan", "base_url": "https://coding.dashscope.aliyuncs.com/v1", "api_key_env": "DASHSCOPE_API_KEY", "models_endpoint": "https://coding.dashscope.aliyuncs.com/v1/models", }, { "provider_id": "alibaba-coding-plan", "display_name": "Aliyun Coding Plan (International)", "base_url": "https://coding-intl.dashscope.aliyuncs.com/v1", "api_key_env": "DASHSCOPE_API_KEY", "models_endpoint": "https://coding-intl.dashscope.aliyuncs.com/v1/models", }, { "provider_id": "zhipuai", "display_name": "Zhipu (BigModel)", "base_url": "https://open.bigmodel.cn/api/paas/v4", "api_key_env": "ZHIPUAI_API_KEY", "models_endpoint": "https://open.bigmodel.cn/api/paas/v4/models", }, { "provider_id": "zhipuai-coding-plan", "display_name": "Zhipu Coding Plan (BigModel)", "base_url": "https://open.bigmodel.cn/api/coding/paas/v4", "api_key_env": "ZHIPUAI_API_KEY", "models_endpoint": "https://open.bigmodel.cn/api/coding/paas/v4/models", }, { "provider_id": "zai", "display_name": "Zhipu (Z.AI)", "base_url": "https://api.z.ai/api/paas/v4", "api_key_env": "ZAI_API_KEY", "models_endpoint": "https://api.z.ai/api/paas/v4/models", }, { "provider_id": "zai-coding-plan", "display_name": "Zhipu Coding Plan (Z.AI)", "base_url": "https://api.z.ai/api/coding/paas/v4", "api_key_env": "ZAI_API_KEY", "models_endpoint": "https://api.z.ai/api/coding/paas/v4/models", }, { "provider_id": "moonshotai-cn", "display_name": "Moonshot", "base_url": "https://api.moonshot.cn/v1", "api_key_env": "MOONSHOT_API_KEY", "models_endpoint": "https://api.moonshot.cn/v1/models", }, { "provider_id": "moonshotai", "display_name": "Moonshot (International)", "base_url": "https://api.moonshot.ai/v1", "api_key_env": "MOONSHOT_API_KEY", "models_endpoint": "https://api.moonshot.ai/v1/models", }, { "provider_id": "minimax-cn", "display_name": "MiniMax", "base_url": "https://api.minimaxi.com/v1", "api_key_env": "MINIMAX_API_KEY", "models_endpoint": "https://api.minimaxi.com/v1/models", }, { "provider_id": "minimax", "display_name": "MiniMax (International)", "base_url": "https://api.minimax.io/v1", "api_key_env": "MINIMAX_API_KEY", "models_endpoint": "https://api.minimax.io/v1/models", }, { "provider_id": "openrouter", "display_name": "OpenRouter", "base_url": "https://openrouter.ai/api/v1", "api_key_env": "OPENROUTER_API_KEY", "capabilities": ["chat", "embedding"], "embedding_base_url": "https://openrouter.ai/api/v1/embeddings", "models_endpoint": "https://openrouter.ai/api/v1/models", "embedding_models_endpoint": "https://openrouter.ai/api/v1/embeddings/models", }, { "provider_id": "modelscope", "display_name": "ModelScope", "base_url": "https://api-inference.modelscope.cn/v1", "api_key_env": "MODELSCOPE_ACCESS_TOKEN", "models_endpoint": "https://api-inference.modelscope.cn/v1/models", }, { "provider_id": "opencode", "display_name": "OpenCode", "base_url": "https://opencode.ai/zen/v1", "models_endpoint": "https://opencode.ai/zen/v1/models", }, { "provider_id": "siliconflow-cn", "display_name": "SiliconFlow", "base_url": "https://api.siliconflow.cn/v1", "embedding_base_url": "https://api.siliconflow.cn/v1/embeddings", "rerank_base_url": "https://api.siliconflow.cn/v1/rerank", "api_key_env": "SILICONFLOW_API_KEY", "capabilities": ["chat", "embedding", "rerank"], "models_endpoint": "https://api.siliconflow.cn/v1/models?sub_type=chat", "embedding_models_endpoint": "https://api.siliconflow.cn/v1/models?sub_type=embedding", "rerank_models_endpoint": "https://api.siliconflow.cn/v1/models?sub_type=reranker", "enabled_models": [ {"id": "Pro/deepseek-ai/DeepSeek-V3.2", "type": "chat", "display_name": "Pro/deepseek-ai/DeepSeek-V3.2"}, {"id": "Pro/MiniMaxAI/MiniMax-M2.5", "type": "chat", "display_name": "Pro/MiniMaxAI/MiniMax-M2.5"}, { "id": "Pro/BAAI/bge-m3", "type": "embedding", "display_name": "Pro/BAAI/bge-m3", "dimension": 1024, "batch_size": 40, }, { "id": "BAAI/bge-m3", "type": "embedding", "display_name": "BAAI/bge-m3", "dimension": 1024, "batch_size": 40, }, { "id": "Qwen/Qwen3-Embedding-0.6B", "type": "embedding", "display_name": "Qwen/Qwen3-Embedding-0.6B", "dimension": 1024, "batch_size": 40, }, { "id": "Pro/BAAI/bge-reranker-v2-m3", "type": "rerank", "display_name": "Pro/BAAI/bge-reranker-v2-m3", }, { "id": "BAAI/bge-reranker-v2-m3", "type": "rerank", "display_name": "BAAI/bge-reranker-v2-m3", }, ], }, { "provider_id": "siliconflow", "display_name": "SiliconFlow (International)", "base_url": "https://api.siliconflow.com/v1", "embedding_base_url": "https://api.siliconflow.com/v1/embeddings", "rerank_base_url": "https://api.siliconflow.com/v1/rerank", "api_key_env": "SILICONFLOW_API_KEY", "capabilities": ["chat", "embedding", "rerank"], "models_endpoint": "https://api.siliconflow.com/v1/models?sub_type=chat", "embedding_models_endpoint": "https://api.siliconflow.com/v1/models?sub_type=embedding", "rerank_models_endpoint": "https://api.siliconflow.com/v1/models?sub_type=reranker", "enabled_models": [ {"id": "deepseek-ai/DeepSeek-V3.2", "type": "chat", "display_name": "deepseek-ai/DeepSeek-V3.2"}, {"id": "MiniMaxAI/MiniMax-M2.5", "type": "chat", "display_name": "MiniMaxAI/MiniMax-M2.5"}, { "id": "Pro/BAAI/bge-m3", "type": "embedding", "display_name": "Pro/BAAI/bge-m3", "dimension": 1024, "batch_size": 40, }, { "id": "BAAI/bge-m3", "type": "embedding", "display_name": "BAAI/bge-m3", "dimension": 1024, "batch_size": 40, }, { "id": "Qwen/Qwen3-Embedding-0.6B", "type": "embedding", "display_name": "Qwen/Qwen3-Embedding-0.6B", "dimension": 1024, "batch_size": 40, }, { "id": "Pro/BAAI/bge-reranker-v2-m3", "type": "rerank", "display_name": "Pro/BAAI/bge-reranker-v2-m3", }, { "id": "BAAI/bge-reranker-v2-m3", "type": "rerank", "display_name": "BAAI/bge-reranker-v2-m3", }, ], }, ] def _normalize_list(value: Any) -> list: return value if isinstance(value, list) else [] def _normalize_dict(value: Any) -> dict: return value if isinstance(value, dict) else {} def _validate_provider_id(provider_id: str) -> None: if not _PROVIDER_ID_RE.match(provider_id): raise ValueError("provider_id 只能包含字母、数字、下划线和中划线，长度 2-100") def _normalize_model_item(model: dict[str, Any]) -> dict[str, Any]: """规范化模型配置对象，校验运行所需字段。""" model_id = str(model.get("id") or "").strip() if not model_id: raise ValueError("模型 id 不能为空") model_type = str(model.get("type") or "unknown").strip() if model_type not in VALID_MODEL_TYPES: raise ValueError(f"启用模型 {model_id} 的 type 必须是 chat、embedding 或 rerank") # source 区分手动添加 vs 远端拉取，用于跳过远端清单存在性的视觉警告。 source = str(model.get("source") or "remote").strip() if source not in VALID_MODEL_SOURCES: raise ValueError(f"模型 {model_id} 的 source 必须是 manual 或 remote") normalized = dict(model) normalized["id"] = model_id normalized["type"] = model_type normalized["source"] = source normalized["display_name"] = str(model.get("display_name") or model.get("name") or model_id) normalized["extra"] = _normalize_dict(model.get("extra")) if model_type == "embedding": dimension = normalized.get("dimension") if dimension not in (None, ""): normalized["dimension"] = int(dimension) if normalized.get("batch_size") not in (None, ""): normalized["batch_size"] = int(normalized["batch_size"]) return normalized def _normalize_model_list(models: Any) -> list[dict[str, Any]]: normalized_models: list[dict[str, Any]] = [] seen_ids: set[str] = set() for item in _normalize_list(models): if not isinstance(item, dict): raise ValueError("模型配置必须是对象列表") normalized = _normalize_model_item(item) if normalized["id"] in seen_ids: raise ValueError(f"模型 id 重复: {normalized['id']}") seen_ids.add(normalized["id"]) normalized_models.append(normalized) return normalized_models def _validate_models_capabilities(enabled_models: list[dict], capabilities: set[str]) -> None: """校验 enabled_models 中所有模型的 type 都在 provider capabilities 范围内。""" for model in enabled_models or []: if model["type"] not in capabilities: raise ValueError(f"模型 {model['id']} 的 type={model['type']} 不在 provider 能力 {sorted(capabilities)} 内") def _normalize_payload(data: dict[str, Any], *, partial: bool = False) -> dict[str, Any]: payload = dict(data) if not partial or "provider_id" in payload: provider_id = str(payload.get("provider_id") or "").strip() _validate_provider_id(provider_id) payload["provider_id"] = provider_id if not partial or "display_name" in payload: display_name = str(payload.get("display_name") or "").strip() if not display_name: raise ValueError("display_name 不能为空") payload["display_name"] = display_name if not partial or "base_url" in payload: base_url = str(payload.get("base_url") or "").strip() if not base_url: raise ValueError("base_url 不能为空") payload["base_url"] = base_url for endpoint_field, default_endpoint in ( ("models_endpoint", DEFAULT_MODELS_ENDPOINT), ("embedding_models_endpoint", DEFAULT_EMBEDDING_MODELS_ENDPOINT), ("rerank_models_endpoint", None), ): if endpoint_field in payload: endpoint = str(payload.get(endpoint_field) or "").strip() payload[endpoint_field] = endpoint or default_endpoint elif not partial and default_endpoint is not None: payload[endpoint_field] = default_endpoint provider_type = payload.get("provider_type") if provider_type is None and not partial: payload["provider_type"] = "openai" elif provider_type is not None: if provider_type not in VALID_PROVIDER_TYPES: raise ValueError(f"provider_type 必须是 {', '.join(sorted(VALID_PROVIDER_TYPES))} 之一") if "capabilities" in payload: payload["capabilities"] = _normalize_list(payload.get("capabilities")) elif not partial: payload["capabilities"] = [] capabilities = set(payload.get("capabilities") or []) if "embedding" in capabilities: embedding_base_url = str(payload.get("embedding_base_url") or "").strip() if not embedding_base_url: raise ValueError("embedding provider 必须配置 embedding_base_url") payload["embedding_base_url"] = embedding_base_url embedding_endpoint = str(payload.get("embedding_models_endpoint") or "").strip() if embedding_endpoint and not embedding_endpoint.startswith(("http://", "https://")): raise ValueError("embedding_models_endpoint 必须是完整的 HTTP URL") payload["embedding_models_endpoint"] = embedding_endpoint if "rerank" in capabilities: rerank_base_url = str(payload.get("rerank_base_url") or "").strip() if not rerank_base_url: raise ValueError("rerank provider 必须配置 rerank_base_url") payload["rerank_base_url"] = rerank_base_url rerank_endpoint = str(payload.get("rerank_models_endpoint") or "").strip() if rerank_endpoint and not rerank_endpoint.startswith(("http://", "https://")): raise ValueError("rerank_models_endpoint 必须是完整的 HTTP URL") payload["rerank_models_endpoint"] = rerank_endpoint if "enabled_models" in payload: payload["enabled_models"] = _normalize_model_list(payload.get("enabled_models")) elif not partial: payload["enabled_models"] = [] if "headers_json" in payload: payload["headers_json"] = _normalize_dict(payload.get("headers_json")) elif not partial: payload["headers_json"] = {} if "extra_json" in payload: payload["extra_json"] = _normalize_dict(payload.get("extra_json")) elif not partial: payload["extra_json"] = {} if "is_enabled" in payload: payload["is_enabled"] = bool(payload["is_enabled"]) elif not partial: payload["is_enabled"] = True if "is_builtin" in payload: payload["is_builtin"] = bool(payload["is_builtin"]) elif not partial: payload["is_builtin"] = False # 仅当本次 payload 同时携带 capabilities 与 enabled_models 时做一致性校验， # 防止前端把超出 provider.capabilities 的模型 type 写入。 # partial 模式下若只更新其中一项，跳过校验避免误判（DB 已有值不可见）。 if "capabilities" in payload and "enabled_models" in payload: capabilities_set = set(payload.get("capabilities") or []) if capabilities_set: _validate_models_capabilities(payload.get("enabled_models"), capabilities_set) return payload def _resolve_api_key(provider: ModelProvider) -> str | None: if provider.api_key: return provider.api_key if provider.api_key_env: return os.getenv(provider.api_key_env) return None def _check_credential_status(provider: ModelProvider) -> str: """检查 provider 的凭证配置状态。仅对启用的 provider 做校验。""" if not provider.is_enabled: return "ok" if provider.api_key: return "ok" if provider.api_key_env: return "ok" if os.getenv(provider.api_key_env) else "warning" return "warning" def _models_url(base_url: str, endpoint: str | None = DEFAULT_MODELS_ENDPOINT) -> str: base = base_url.rstrip("/") if not endpoint: return base endpoint = endpoint.strip() if endpoint.startswith(("http://", "https://")): return endpoint return f"{base}/{endpoint.lstrip('/')}" def _normalize_remote_model(raw_model: dict[str, Any], model_type: str = "chat") -> dict[str, Any]: model_id = str(raw_model.get("id") or "").strip() if not model_id: return {} architecture = _normalize_dict(raw_model.get("architecture")) top_provider = _normalize_dict(raw_model.get("top_provider")) raw_type = raw_model.get("type") normalized_type = raw_type if raw_type in VALID_MODEL_TYPES else model_type normalized = { "id": model_id, "object": raw_model.get("object"), "created": raw_model.get("created"), "owned_by": raw_model.get("owned_by"), "type": normalized_type, "display_name": raw_model.get("name") or model_id, "description": raw_model.get("description"), "context_length": raw_model.get("context_length") or top_provider.get("context_length"), "max_completion_tokens": top_provider.get("max_completion_tokens"), "input_modalities": architecture.get("input_modalities") or [], "output_modalities": architecture.get("output_modalities") or [], "supported_parameters": raw_model.get("supported_parameters") or [], "pricing": raw_model.get("pricing") or {}, "default_parameters": raw_model.get("default_parameters") or {}, "raw_metadata": raw_model, "extra": {}, } return {key: value for key, value in normalized.items() if value is not None} async def get_all_model_providers(db: AsyncSession) -> list[ModelProvider]: """获取全部独立模型供应商配置。""" return await list_model_providers(db) async def get_model_provider_by_id(db: AsyncSession, provider_id: str) -> ModelProvider | None: """按 provider_id 获取独立模型供应商配置。""" return await get_model_provider(db, provider_id) async def ensure_builtin_model_providers_in_db(db: AsyncSession) -> None: """确保独立模型配置模块的内置 provider 模板存在。 这里只补不存在的内置 provider，不覆盖管理员已编辑的配置。 """ existing = await list_model_providers(db) existing_ids = {p.provider_id: p for p in existing} for provider_def in _DEFAULT_BUILTIN_PROVIDERS: provider_id = provider_def["provider_id"] existing_provider = existing_ids.get(provider_id) if existing_provider: if not existing_provider.enabled_models and provider_def.get("enabled_models"): existing_provider.enabled_models = _normalize_model_list(provider_def["enabled_models"]) existing_provider.capabilities = provider_def.get("capabilities") or existing_provider.capabilities existing_provider.updated_by = "system" await db.flush() continue payload = {key: value for key, value in provider_def.items() if value is not None} payload["enabled_models"] = provider_def.get("enabled_models", []) payload["headers_json"] = payload.get("headers_json") or {} payload["extra_json"] = payload.get("extra_json") or {} payload["is_enabled"] = provider_id == "siliconflow-cn" payload["is_builtin"] = True payload["created_by"] = "system" payload["updated_by"] = "system" await create_model_provider(db, _normalize_payload(payload)) async def create_provider_config(db: AsyncSession, data: dict[str, Any], username: str) -> ModelProvider: """创建独立模型供应商配置。""" payload = _normalize_payload(data) if await get_model_provider(db, payload["provider_id"]): raise ValueError(f"供应商 {payload['provider_id']} 已存在") payload["created_by"] = username payload["updated_by"] = username return await create_model_provider(db, payload) async def update_provider_config( db: AsyncSession, provider_id: str, data: dict[str, Any], username: str, ) -> ModelProvider | None: """更新独立模型供应商配置。""" provider = await get_model_provider(db, provider_id) if provider is None: return None payload = _normalize_payload(data, partial=True) # partial 更新时仅传 enabled_models，结合 DB 中现有 capabilities 校验 if "enabled_models" in payload and "capabilities" not in payload: existing_caps = set(provider.capabilities or []) if existing_caps: _validate_models_capabilities(payload.get("enabled_models"), existing_caps) payload["updated_by"] = username return await update_model_provider(db, provider, payload) async def delete_provider_config(db: AsyncSession, provider_id: str) -> bool: """删除独立模型供应商配置。""" provider = await get_model_provider(db, provider_id) if provider is None: return False await delete_model_provider(db, provider) return True async def _fetch_models_from_endpoint( client: httpx.AsyncClient, provider: ModelProvider, headers: dict[str, str], endpoint: str | None, model_type: str, ) -> list[dict[str, Any]]: """按单个模型类型端点拉取并规范化远端模型列表。""" if not endpoint: return [] response = await client.get(_models_url(provider.base_url, endpoint), headers=headers) response.raise_for_status() payload = response.json() raw_models = payload.get("data") if isinstance(payload, dict) else payload if not isinstance(raw_models, list): raise ValueError(f"{endpoint} 响应必须是列表或包含 data 列表") models = [] for raw_model in raw_models: if isinstance(raw_model, dict): normalized = _normalize_remote_model(raw_model, model_type) if normalized: models.append(normalized) return models async def fetch_remote_models(provider: ModelProvider) -> list[dict[str, Any]]: """按 provider 配置实时拉取远端模型列表，不落库。 Chat 模型默认走 /models；embedding 只有 provider 声明能力时才走 /embeddings/models；rerank 供应商没有稳定通用端点，配置了 endpoint 才拉取。 """ headers = dict(provider.headers_json or {}) api_key = _resolve_api_key(provider) if api_key: headers.setdefault("Authorization", f"Bearer {api_key}") capabilities = set(provider.capabilities or []) endpoint_specs = [ (provider.models_endpoint or DEFAULT_MODELS_ENDPOINT, "chat"), ] if "embedding" in capabilities: endpoint_specs.append((provider.embedding_models_endpoint or DEFAULT_EMBEDDING_MODELS_ENDPOINT, "embedding")) if "rerank" in capabilities and provider.rerank_models_endpoint: endpoint_specs.append((provider.rerank_models_endpoint, "rerank")) seen_ids: set[tuple[str, str]] = set() models: list[dict[str, Any]] = [] async with httpx.AsyncClient(timeout=20.0) as client: results = await asyncio.gather( *[ _fetch_models_from_endpoint(client, provider, headers, endpoint, model_type) for endpoint, model_type in endpoint_specs ] ) for fetched_models in results: for model in fetched_models: model_key = (model["id"], model["type"]) if model_key in seen_ids: continue seen_ids.add(model_key) models.append(model) return models