mirror of
https://github.com/FuQuan233/nonebot-plugin-llmchat.git
synced 2026-08-13 10:09:27 +00:00
84 lines
4.6 KiB
Python
Executable file
84 lines
4.6 KiB
Python
Executable file
from pydantic import BaseModel, Field, model_validator
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class PresetConfig(BaseModel):
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"""API预设配置。"""
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name: str = Field(..., description="预设名称（唯一标识）")
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api_base: str = Field(..., description="API基础地址")
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api_key: str = Field(..., description="API密钥")
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model_name: str = Field(..., description="模型名称")
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max_tokens: int = Field(default=2048, description="最大响应token数")
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temperature: float = Field(default=0.7, description="生成温度（0-2]")
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proxy: str = Field(default="", description="HTTP代理服务器")
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support_mcp: bool = Field(default=False, description="是否支持MCP")
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support_image: bool = Field(default=False, description="是否支持图片输入")
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stream: bool = Field(default=False, description="是否使用流式响应")
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extra_body: dict = Field(default_factory=dict, description="额外请求体字段")
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request_with_reasoning_content: bool = Field(
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default=False,
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description="工具调用后是否向API回传推理内容",
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)
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class MCPServerConfig(BaseModel):
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"""MCP服务器配置。"""
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command: str | None = Field(default=None, description="stdio模式下MCP命令")
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args: list[str] | None = Field(default_factory=list, description="stdio命令参数")
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env: dict[str, str] | None = Field(default_factory=dict, description="stdio环境变量")
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url: str | None = Field(default=None, description="远程MCP服务器地址")
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headers: dict[str, str] | None = Field(default_factory=dict, description="HTTP请求头")
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transport: str | None = Field(default=None, description="sse或streamable_http")
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friendly_name: str | None = Field(default=None, description="MCP服务器友好名称")
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additional_prompt: str | None = Field(default=None, description="额外提示词")
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@model_validator(mode="after")
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def validate_transport(self):
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if bool(self.command) == bool(self.url):
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raise ValueError("MCP服务器必须且只能配置 command 或 url 其中之一")
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if self.transport not in {None, "sse", "streamable_http"}:
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raise ValueError("transport 必须是 sse 或 streamable_http")
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return self
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class ScopedConfig(BaseModel):
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"""LLM Chat Plugin配置。"""
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api_presets: list[PresetConfig] = Field(..., description="API预设列表")
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history_size: int = Field(default=20, ge=1, description="LLM上下文消息保留数量")
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past_events_size: int = Field(default=10, ge=1, description="触发时发送的消息数量")
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request_timeout: int = Field(default=30, ge=1, description="API请求超时时间（秒）")
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max_tool_rounds: int = Field(default=8, ge=1, le=50, description="最大工具调用轮数")
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max_repeated_tool_calls: int = Field(default=2, ge=1, le=10, description="相同工具调用最多执行次数")
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mcp_timeout: int = Field(default=30, ge=1, description="MCP操作超时时间（秒）")
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default_preset: str = Field(default="off", description="默认预设名称")
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random_trigger_prob: float = Field(default=0.05, ge=0.0, le=1.0, description="随机触发概率")
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default_prompt: str = Field(
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default="你的回答应该尽量简洁、幽默、可以使用一些语气词、颜文字。你应该拒绝回答任何政治相关的问题。",
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description="默认提示词",
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)
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mcp_server_cwd: str | None = Field(default=None, description="stdio MCP服务器全局工作目录")
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mcp_servers: dict[str, MCPServerConfig] = Field(default_factory=dict, description="MCP服务器配置")
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blacklist_user_ids: set[int] = Field(default_factory=set, description="黑名单用户ID")
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ignore_prefixes: list[str] = Field(default_factory=list, description="忽略的消息前缀")
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enable_private_chat: bool = Field(default=False, description="是否启用私聊")
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private_chat_preset: str = Field(default="off", description="私聊默认预设")
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@model_validator(mode="after")
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def validate_presets(self):
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names = [preset.name for preset in self.api_presets]
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if not names:
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raise ValueError("api_presets 至少需要一个预设")
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if len(names) != len(set(names)):
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raise ValueError("api_presets 中的预设名称不能重复")
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available = set(names) | {"off"}
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if self.default_preset not in available:
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raise ValueError(f"default_preset 不存在: {self.default_preset}")
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if self.private_chat_preset not in available:
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raise ValueError(f"private_chat_preset 不存在: {self.private_chat_preset}")
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return self
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class Config(BaseModel):
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llmchat: ScopedConfig
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