9f73851858
Add MiniMax (https://platform.minimaxi.com/) as a built-in chat model provider with M2.7, M2.7-highspeed, M2.5, and M2.5-highspeed models. MiniMax provides OpenAI-compatible API, so it works seamlessly with the existing OpenAIBase and ChatOpenAI integration paths. Users only need to set MINIMAX_API_KEY to start using MiniMax models. Changes: - Add MiniMax to DEFAULT_CHAT_MODEL_PROVIDERS (models.py) - Add MINIMAX_API_KEY to .env.template - Add MiniMax provider icon and frontend registration - Update model-config docs with MiniMax entry - Add 22 unit tests + 3 integration tests
256 lines
9.2 KiB
Python
256 lines
9.2 KiB
Python
"""
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默认模型配置
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该文件定义了系统支持的所有默认模型配置，包括：
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- 聊天模型（LLM）
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- 嵌入模型（Embedding）
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- 重排序模型（Reranker）
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"""
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from pydantic import BaseModel, Field
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class ChatModelProvider(BaseModel):
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"""聊天模型提供商配置"""
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name: str = Field(..., description="提供商显示名称")
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url: str = Field(..., description="提供商文档或模型列表 URL")
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base_url: str = Field(..., description="API 基础 URL")
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default: str = Field(..., description="默认模型名称")
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env: str = Field(..., description="API Key 环境变量名")
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models: list[str] = Field(default_factory=list, description="支持的模型列表")
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custom: bool = Field(default=False, description="是否为自定义供应商")
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class EmbedModelInfo(BaseModel):
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"""嵌入模型配置"""
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name: str = Field(..., description="模型名称")
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dimension: int = Field(..., description="向量维度")
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base_url: str = Field(..., description="API 基础 URL")
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api_key: str = Field(..., description="API Key 或环境变量名")
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model_id: str | None = Field(None, description="可选的模型 ID")
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batch_size: int = Field(40, description="批量向量化大小")
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class RerankerInfo(BaseModel):
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"""重排序模型配置"""
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name: str = Field(..., description="模型名称")
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base_url: str = Field(..., description="API 基础 URL")
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api_key: str = Field(..., description="API Key 或环境变量名")
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# ============================================================
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# 默认聊天模型配置
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# ============================================================
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DEFAULT_CHAT_MODEL_PROVIDERS: dict[str, ChatModelProvider] = {
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"openai": ChatModelProvider(
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name="OpenAI",
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url="https://platform.openai.com/docs/models",
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base_url="https://api.openai.com/v1",
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default="gpt-5-mini",
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env="OPENAI_API_KEY",
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models=["gpt-5.2", "gpt-5-mini", "gpt-5.2-pro"],
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),
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"deepseek": ChatModelProvider(
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name="DeepSeek",
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url="https://platform.deepseek.com/api-docs/zh-cn/pricing",
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base_url="https://api.deepseek.com/v1",
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default="deepseek-chat",
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env="DEEPSEEK_API_KEY",
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models=["deepseek-chat", "deepseek-reasoner"],
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),
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"zhipu": ChatModelProvider(
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name="智谱AI (Zhipu)",
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url="https://open.bigmodel.cn/dev/api",
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base_url="https://open.bigmodel.cn/api/paas/v4/",
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default="glm-4.7-flash",
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env="ZHIPUAI_API_KEY",
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models=["glm-5", "glm-4.5-air", "glm-4.7-flash"],
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),
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"siliconflow": ChatModelProvider(
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name="SiliconFlow",
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url="https://cloud.siliconflow.cn/models",
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base_url="https://api.siliconflow.cn/v1",
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default="Pro/deepseek-ai/DeepSeek-V3.2",
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env="SILICONFLOW_API_KEY",
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models=[
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"Pro/deepseek-ai/DeepSeek-V3.2",
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"Pro/MiniMaxAI/MiniMax-M2.5",
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"Pro/zai-org/GLM-5",
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"Pro/moonshotai/Kimi-K2.5",
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],
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),
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# "together": ChatModelProvider(
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# name="Together",
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# url="https://api.together.ai/models",
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# base_url="https://api.together.xyz/v1/",
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# default="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
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# env="TOGETHER_API_KEY",
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# models=["meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"],
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# ),
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"dashscope": ChatModelProvider(
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name="阿里百炼 (DashScope)",
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url="https://bailian.console.aliyun.com/?switchAgent=10226727&productCode=p_efm#/model-market",
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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default="qwen-max-latest",
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env="DASHSCOPE_API_KEY",
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models=[
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"qwen-max-latest",
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"qwen-plus-latest",
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"qwen-turbo-latest",
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],
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),
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"ark": ChatModelProvider(
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name="豆包（Ark）",
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url="https://console.volcengine.com/ark/region:ark+cn-beijing/model",
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base_url="https://ark.cn-beijing.volces.com/api/v3",
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default="doubao-seed-2-0-lite-260215",
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env="ARK_API_KEY",
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models=[
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"doubao-seed-2-0-pro-260215",
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"doubao-seed-2-0-lite-260215",
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"doubao-seed-2-0-mini-260215",
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],
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),
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"minimax": ChatModelProvider(
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name="MiniMax",
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url="https://platform.minimaxi.com/document/introduction",
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base_url="https://api.minimax.io/v1",
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default="MiniMax-M2.7",
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env="MINIMAX_API_KEY",
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models=[
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"MiniMax-M2.7",
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"MiniMax-M2.7-highspeed",
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"MiniMax-M2.5",
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"MiniMax-M2.5-highspeed",
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],
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),
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"openrouter": ChatModelProvider(
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name="OpenRouter",
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url="https://openrouter.ai/models",
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base_url="https://openrouter.ai/api/v1",
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default="x-ai/grok-4.1-fast",
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env="OPENROUTER_API_KEY",
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models=[
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"anthropic/claude-opus-4.6",
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"anthropic/claude-sonnet-4.5",
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"x-ai/grok-4.1-fast",
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"x-ai/grok-4",
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],
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),
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# "moonshot": ChatModelProvider(
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# name="月之暗面",
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# url="https://platform.moonshot.cn/docs/overview",
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# base_url="https://api.moonshot.cn/v1",
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# default="kimi-latest",
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# env="MOONSHOT_API_KEY",
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# models=[
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# "kimi-latest",
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# "kimi-k2-thinking",
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# "kimi-k2-0905-preview",
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# ],
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# ), # 目前适配有问题 Error code: 400 - {'error': {'message': 'Invalid request: function name is invalid, must start with a letter and can contain letters, numbers, underscores, and dashes', 'type': 'invalid_request_error'}} # noqa: E501
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"modelscope": ChatModelProvider(
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name="ModelScope",
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url="https://www.modelscope.cn/docs/model-service/API-Inference/intro",
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base_url="https://api-inference.modelscope.cn/v1/",
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default="deepseek-ai/DeepSeek-V3.2",
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env="MODELSCOPE_ACCESS_TOKEN",
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models=["ZhipuAI/GLM-5", "ZhipuAI/GLM-4.7-Flash", "MiniMax/MiniMax-M2.5", "moonshotai/Kimi-K2.5", ""],
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),
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}
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# ============================================================
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# 默认嵌入模型配置
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# ============================================================
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DEFAULT_EMBED_MODELS: dict[str, EmbedModelInfo] = {
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"siliconflow/BAAI/bge-m3": EmbedModelInfo(
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model_id="siliconflow/BAAI/bge-m3",
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name="BAAI/bge-m3",
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dimension=1024,
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base_url="https://api.siliconflow.cn/v1/embeddings",
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api_key="SILICONFLOW_API_KEY",
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),
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"siliconflow/Pro/BAAI/bge-m3": EmbedModelInfo(
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model_id="siliconflow/Pro/BAAI/bge-m3",
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name="Pro/BAAI/bge-m3",
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dimension=1024,
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base_url="https://api.siliconflow.cn/v1/embeddings",
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api_key="SILICONFLOW_API_KEY",
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),
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"siliconflow/Qwen/Qwen3-Embedding-0.6B": EmbedModelInfo(
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model_id="siliconflow/Qwen/Qwen3-Embedding-0.6B",
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name="Qwen/Qwen3-Embedding-0.6B",
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dimension=1024,
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base_url="https://api.siliconflow.cn/v1/embeddings",
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api_key="SILICONFLOW_API_KEY",
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),
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"vllm/Qwen/Qwen3-Embedding-0.6B": EmbedModelInfo(
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model_id="vllm/Qwen/Qwen3-Embedding-0.6B",
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name="Qwen3-Embedding-0.6B",
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dimension=1024,
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base_url="http://localhost:8000/v1/embeddings",
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api_key="no_api_key",
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),
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"ollama/nomic-embed-text": EmbedModelInfo(
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model_id="ollama/nomic-embed-text",
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name="nomic-embed-text",
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dimension=768,
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base_url="http://localhost:11434/api/embed",
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api_key="no_api_key",
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),
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"ollama/bge-m3": EmbedModelInfo(
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model_id="ollama/bge-m3",
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name="bge-m3",
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dimension=1024,
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base_url="http://localhost:11434/api/embed",
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api_key="no_api_key",
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),
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"dashscope/text-embedding-v4": EmbedModelInfo(
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model_id="dashscope/text-embedding-v4",
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name="text-embedding-v4",
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dimension=1024,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings",
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api_key="DASHSCOPE_API_KEY",
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batch_size=10,
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),
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}
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# ============================================================
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# 默认重排序模型配置
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# ============================================================
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DEFAULT_RERANKERS: dict[str, RerankerInfo] = {
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"siliconflow/BAAI/bge-reranker-v2-m3": RerankerInfo(
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name="BAAI/bge-reranker-v2-m3",
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base_url="https://api.siliconflow.cn/v1/rerank",
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api_key="SILICONFLOW_API_KEY",
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),
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"siliconflow/Pro/BAAI/bge-reranker-v2-m3": RerankerInfo(
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name="Pro/BAAI/bge-reranker-v2-m3",
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base_url="https://api.siliconflow.cn/v1/rerank",
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api_key="SILICONFLOW_API_KEY",
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),
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"dashscope/gte-rerank-v2": RerankerInfo(
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name="gte-rerank-v2",
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base_url="https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
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api_key="DASHSCOPE_API_KEY",
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),
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"dashscope/qwen3-rerank": RerankerInfo(
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name="qwen3-rerank",
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base_url="https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
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api_key="DASHSCOPE_API_KEY",
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),
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"vllm/BAAI/bge-reranker-v2-m3": RerankerInfo(
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name="BAAI/bge-reranker-v2-m3",
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base_url="http://localhost:8000/v1/rerank",
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api_key="no_api_key",
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),
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}
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