mirror of
https://github.com/RockChinQ/QChatGPT.git
synced 2024-11-16 19:57:04 +08:00
121 lines
3.4 KiB
Python
121 lines
3.4 KiB
Python
from __future__ import annotations
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import typing
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import enum
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import pydantic
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from ..platform.types import message as platform_message
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class FunctionCall(pydantic.BaseModel):
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name: str
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arguments: str
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class ToolCall(pydantic.BaseModel):
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id: str
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type: str
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function: FunctionCall
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class ImageURLContentObject(pydantic.BaseModel):
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url: str
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def __str__(self):
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return self.url[:128] + ('...' if len(self.url) > 128 else '')
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class ContentElement(pydantic.BaseModel):
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type: str
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"""内容类型"""
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text: typing.Optional[str] = None
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image_url: typing.Optional[ImageURLContentObject] = None
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def __str__(self):
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if self.type == 'text':
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return self.text
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elif self.type == 'image_url':
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return f'[图片]({self.image_url})'
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else:
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return '未知内容'
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@classmethod
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def from_text(cls, text: str):
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return cls(type='text', text=text)
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@classmethod
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def from_image_url(cls, image_url: str):
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return cls(type='image_url', image_url=ImageURLContentObject(url=image_url))
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class Message(pydantic.BaseModel):
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"""消息"""
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role: str # user, system, assistant, tool, command, plugin
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"""消息的角色"""
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name: typing.Optional[str] = None
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"""名称，仅函数调用返回时设置"""
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content: typing.Optional[list[ContentElement]] | typing.Optional[str] = None
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"""内容"""
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tool_calls: typing.Optional[list[ToolCall]] = None
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"""工具调用"""
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tool_call_id: typing.Optional[str] = None
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def readable_str(self) -> str:
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if self.content is not None:
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return str(self.role) + ": " + str(self.get_content_platform_message_chain())
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elif self.tool_calls is not None:
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return f'调用工具: {self.tool_calls[0].id}'
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else:
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return '未知消息'
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def get_content_platform_message_chain(self, prefix_text: str="") -> platform_message.MessageChain | None:
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"""将内容转换为平台消息 MessageChain 对象
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Args:
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prefix_text (str): 首个文字组件的前缀文本
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"""
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if self.content is None:
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return None
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elif isinstance(self.content, str):
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return platform_message.MessageChain([platform_message.Plain(prefix_text+self.content)])
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elif isinstance(self.content, list):
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mc = []
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for ce in self.content:
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if ce.type == 'text':
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mc.append(platform_message.Plain(ce.text))
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elif ce.type == 'image_url':
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if ce.image_url.url.startswith("http"):
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mc.append(platform_message.Image(url=ce.image_url.url))
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else: # base64
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b64_str = ce.image_url.url
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if b64_str.startswith("data:"):
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b64_str = b64_str.split(",")[1]
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mc.append(platform_message.Image(base64=b64_str))
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# 找第一个文字组件
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if prefix_text:
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for i, c in enumerate(mc):
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if isinstance(c, platform_message.Plain):
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mc[i] = platform_message.Plain(prefix_text+c.text)
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break
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else:
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mc.insert(0, platform_message.Plain(prefix_text))
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return platform_message.MessageChain(mc)
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