102 lines
3.0 KiB
Python
102 lines
3.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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数据模型
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"""
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from enum import StrEnum
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from time import time_ns
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from typing import List, Dict
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from uuid import uuid4
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from pydantic import BaseModel, Field
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from pydantic_ai.messages import ModelMessage, ModelMessagesTypeAdapter
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from sqlmodel import Field as SqlField, SQLModel
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# 消息历史数据表模型
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# 需使用 reflex db init 初始化数据库表，若重新初始化需手动删除 alembic 相关配置和文件夹
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class MessageHistory(SQLModel, table=True):
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id: str = SqlField(
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default_factory=lambda: uuid4().hex,
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primary_key=True,
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description="消息唯一标识",
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)
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chat_id: str = SqlField(index=True, description="聊天唯一标识")
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new_message: str = SqlField(description="新消息")
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create_at: int = SqlField(
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index=True,
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description="创建时间戳（毫秒级）",
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)
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@staticmethod
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def adapt(chat_id: str, new_message: List[ModelMessage]) -> "MessageHistory":
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return MessageHistory(
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chat_id=chat_id,
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new_message=ModelMessagesTypeAdapter.dump_json(new_message).decode(
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"utf-8"
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), # 序列化为 JSON 字符串
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create_at=time_ns() // 1_000_000, # 毫秒级时间戳
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)
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"""
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聊天、对话和消息关系：
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一次聊天包含若干轮对话，每轮对话包含用户提示词（user_prompt）和输出（output）
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其中，
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输出包含若干片段（part）
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"""
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class PartType(StrEnum):
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"""片段类型"""
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THINKING = "thinking"
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TEXT = "text"
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CALL = "call"
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TOOL_ARGS = "tool_args"
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TOOL_RETURN = "tool_return"
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FINISHED = "finished"
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ERROR = "error"
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# 动态生成前缀和片段类型映射表
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PREFIX_MAPING = {f"{i:02d}:": t for i, t in enumerate(PartType)}
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class Part(BaseModel):
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"""片段类（仅就输出消息）"""
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part_type: PartType = Field(..., description="片段类型")
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content: str = Field(default="", description="片段内容")
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is_streaming: bool = Field(
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default=False,
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description="流式输出状态，True 表示正在流式输出，False 表示非正在流式输出",
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)
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is_open: bool = Field(
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default=False,
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description="折叠面板打开状态，True表示打开，False表示关闭",
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)
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class Dialog(BaseModel):
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"""对话类"""
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user_prompt: str = Field(..., description="用户提示词")
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output: Dict[str, Part] = Field(
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default_factory=dict, description="输出，键为片段唯一标识，值为片段对象"
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)
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class Chat(BaseModel):
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"""聊天类"""
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description: str = Field(default="新聊天", description="聊天描述")
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is_streaming: bool = Field(
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default=False,
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description="流式输出状态，True 表示正在流式输出，False 表示非正在流式输出",
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)
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dialogs: Dict[str, Dialog] = Field(
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default_factory=dict, description="对话列表，键为对话唯一标识，值为对话对象"
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)
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