436 lines
16 KiB
Python
436 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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"""
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对话状态
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"""
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from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, cast
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from pydantic_ai import Agent, ThinkingPartDelta
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from pydantic_ai._uuid import uuid7
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from pydantic_ai.capabilities import AgentCapability
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from pydantic_ai.messages import (
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FunctionToolCallEvent,
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FunctionToolResultEvent,
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LoadCapabilityCallPart,
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ModelMessage,
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PartDeltaEvent,
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PartEndEvent,
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PartStartEvent,
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TextPart,
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TextPartDelta,
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ThinkingPart,
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ToolCallPart,
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ToolSearchCallPart,
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)
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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import reflex as rx
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from application.models import Conversation, EventKind, PartKind, Run, Reasoning, Kind
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from application.state.create_conversation_modal import CreateConversationModalState
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from application.state.database import DatabaseState
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instructions: str = """
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# 角色
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专业友好AI助手，结构化解答各类问题。
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# 输出硬性规则
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1. 全文强制标准Markdown，禁止纯文本；不要额外说明排版格式，直接输出内容；
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2. 层级使用 `#/##/###`，列表用 `-` 无序列表或数字有序列表；
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3. 代码块用 ```语言名``` 包裹；
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4. 重点内容标注 **粗体**/*斜体*；
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5. 思考、工具日志仅输出文本，适配前端折叠面板，禁止输出HTML标签；
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6. 内容分点拆分，排版整洁适配前端Markdown渲染。
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# 行文要求
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语言通俗，逻辑完整简洁，无多余废话。
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"""
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class ConversationState(rx.State):
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"""
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对话状态
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"""
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# 初始化对话字典
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conversations: Dict[str, Conversation] = {str(uuid7()): Conversation()}
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# 当前对话唯一标识
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conversation_id: str = next(reversed(conversations.keys()))
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# 初始化智能体
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agent: Agent = Agent(
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model=OpenAIChatModel(
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model_name="deepseek-v4-flash",
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provider=OpenAIProvider(
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base_url="https://tokenhub.tencentmaas.com/v1",
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api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
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),
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),
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instructions=instructions,
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capabilities=None,
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output_type=str,
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retries=1,
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)
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@rx.var
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def get_conversations(self) -> Dict[str, Conversation]:
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"""
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获取对话字典，用于前端按照创建倒序渲染对话历史
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:return: 对话字典（倒序）
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"""
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return dict(reversed(self.conversations.items()))
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@rx.event
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async def create_conversation(self, form_data: Dict[str, Any]) -> None:
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"""
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创建对话
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:param form_data: 表单数据
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:return: None
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"""
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# 获取描述
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description = form_data["description"].strip()
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if not description:
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description = "新对话"
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# 创建对话
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self.conversations[str(uuid7())] = Conversation(description=description)
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# 将末位对话唯一标识设置为当前对话唯一标识
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self.conversation_id = next(reversed(self.conversations.keys()))
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# 获取创建对话模态窗状态
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create_conversation_modal_state = await self.get_state(
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CreateConversationModalState
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)
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# 关闭创建对话模态窗
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create_conversation_modal_state.is_open = False
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@rx.event
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def delete_conversation(self, conversation_id: str) -> None:
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"""
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删除对话
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:param conversation_id: 对话唯一标识
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:return: None
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"""
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if conversation_id not in self.conversations:
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return
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del self.conversations[conversation_id]
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# 删除后，若对话字典为空则创建对话
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if not self.conversations:
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self.conversations[str(uuid7())] = Conversation()
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# 删除后，若当前对话唯一标识不存在则将末位对话唯一标识设置为当前对话唯一标识
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if self.conversation_id not in self.conversations:
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self.conversation_id = next(reversed(self.conversations.keys()))
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@rx.event
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def switch_conversation(self, conversation_id: str) -> None:
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"""
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将指定对话唯一标识设置为当前对话唯一标识
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:param conversation_id: 指定对话唯一标识
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:return: None
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"""
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if conversation_id not in self.conversations:
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return
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self.conversation_id = conversation_id
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@rx.var
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def get_conversation_description(self) -> str:
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"""
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获取当前对话描述，用于前端渲染对话标题
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:return: 当前对话描述
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"""
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# 当前对话
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conversation = self.conversations.get(self.conversation_id)
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return conversation.description if conversation else "新对话"
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@rx.var
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def get_runs(self) -> Dict[str, Run]:
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"""
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获取运行字典，用于前端渲染运行历史
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:return: 运行字典
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"""
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# 当前对话
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conversation = self.conversations.get(self.conversation_id)
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return conversation.runs if conversation else {}
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@rx.var
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def get_run_streaming_status(self) -> bool:
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"""
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获取当前运行流式输出状态
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:return: 当前运行流式输出状态（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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# 当前对话
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conversation = self.conversations.get(self.conversation_id)
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if not conversation or not conversation.runs:
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return False
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# 末位运行
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run = next(reversed(conversation.runs.values()))
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return run.is_streaming
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@rx.event
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async def run(self, form_data: dict[str, Any]) -> AsyncGenerator[None]:
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"""
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运行
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:param form_data: 表单数据
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:return: AsyncGenerator
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"""
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# 当前对话
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conversation = self.conversations.get(self.conversation_id)
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if not conversation:
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return
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# 获取用户提示词
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user_prompt = form_data.get("user_prompt", "").strip()
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if not user_prompt:
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return
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# 获取数据库状态
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database_state = await self.get_state(DatabaseState)
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# 获取消息历史
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message_history = await database_state.get_message_history(
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conversation_id=self.conversation_id
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)
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# 初始化工具调用唯一标识与片段索引映射字典
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tool_call_ids : Dict[int, str] = {}
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# 创建运行
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conversation.runs[str(uuid7())] = Run(user_prompt=user_prompt)
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# 将末位运行唯一标识、运行设置为当前运行唯一标识、运行
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run_id, run = next(reversed(conversation.runs.items()))
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# 将流式输出状态设置为正在流式输出
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run.is_streaming = True
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yield # 通知前端渲染流式输出状态
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async for event in self.agent.run_stream_events(
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conversation_id=self.conversation_id,
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user_prompt=user_prompt,
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message_history=message_history,
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):
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match event:
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# ========== 分片开始事件 ==========
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case PartStartEvent(
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index=index,
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part=part,
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previous_part_kind=previous_part_kind,
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):
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match part:
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# 思考分片开始事件
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case ThinkingPart(
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part_kind=part_kind, content=content
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):
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# 若上一分片种类为空则将正在推理状态设置为正在推理
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if not previous_part_kind:
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run.is_reasoning = True
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# 初始化推理
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run.reasonings[index] = Reasoning(kind=Kind.THINKING, content=content)
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yield # 通知前端渲染推理
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case ToolSearchCallPart(
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tool_call_id=tool_call_id
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):
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# 创建工具调用唯一标识与片段索引映射
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tool_call_ids[index] = tool_call_id
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# 初始化推理
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run.reasonings[index] = Reasoning(kind=Kind.TOOL_SEARCH, content="正在检索")
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yield # 通知前端渲染推理
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# 文本分片开始事件
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case TextPart(
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part_kind=part_kind, content=content
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):
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# 初始化回复正文
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run.assistant_content = content
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yield # 通知前端渲染回复正文
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case _:
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continue
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# ========== 分片增量事件（前端仅就文本片段实现打字机效果） ==========
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case PartDeltaEvent(
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index=index, delta=delta
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):
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match delta:
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# 思考分片增量事件
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case ThinkingPartDelta(
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content_delta=content_delta,
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):
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run.reasonings[index].content += content_delta or ""
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yield # 通知前端渲染推理
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# 文本分片增量事件
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case TextPartDelta(
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content_delta=content_delta,
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):
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run.assistant_content += content_delta
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yield # 通知前端渲染回复正文
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case _:
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continue
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# ========== 分片结束事件 ==========
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case PartEndEvent(
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event_kind=event_kind,
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index=index,
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part=part,
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next_part_kind=next_part_kind,
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):
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match part:
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# 思考分片结束事件
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case ThinkingPart(
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part_kind=part_kind, content=content
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):
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# 若下一分片种类为文本则将正在推理状态设置为非正在推理
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if next_part_kind == Kind.TEXT:
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run.is_reasoning = False
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# 检索工具分片结束事件
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case ToolSearchCallPart(
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tool_call_id=tool_call_id
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):
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# 创建工具调用唯一标识与片段索引映射
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tool_call_ids[index] = tool_call_id
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# 加载能力分片结束事件
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case LoadCapabilityCallPart(
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tool_kind=tool_kind, tool_name=tool_name
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):
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yield Event(
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event_kind=EventKind(event_kind),
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part_index=index,
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part_kind=PartKind(tool_kind),
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next_part_kind=PartKind(next_part_kind),
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tool_name=tool_name,
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run_id=current_run_id,
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)
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# 调用工具分片结束事件
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case ToolCallPart(
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part_kind=part_kind, tool_name=tool_name
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):
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yield Event(
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event_kind=EventKind(event_kind),
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part_index=index,
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part_kind=PartKind(part_kind),
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next_part_kind=PartKind(next_part_kind),
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tool_name=tool_name,
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run_id=current_run_id,
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)
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# ========== 函数工具调用事件 ==========
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case FunctionToolCallEvent(
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event_kind=event_kind,
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part=part,
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):
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yield Event(
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event_kind=EventKind(event_kind),
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part_index=self.tool_call_ids.get(
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part.tool_call_id
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),
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part_kind=PartKind(part.part_kind),
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tool_name=part.tool_name,
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run_id=current_run_id,
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)
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# ========== 函数工具回调事件 ==========
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case FunctionToolResultEvent(
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event_kind=event_kind,
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content=content,
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part=part,
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):
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yield Event(
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event_kind=EventKind(event_kind),
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event_content=(
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content if isinstance(content, str) else ""
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), # 暂仅处理文本类型，后续处理多模态和缓存
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part_index=self.tool_call_ids.get(
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part.tool_call_id
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),
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part_kind=PartKind(part.part_kind),
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tool_name=part.tool_name,
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run_id=current_run_id,
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)
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yield Event(
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event_kind=EventKind.RUN_END,
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run_id=current_run_id,
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run_new_messages=agent_run.new_messages(),
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)
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# 运行，处理分片生命周期事件
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async for self.agent.run(
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conversation_id=self.conversation_id,
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user_prompt=user_prompt,
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message_history=message_history,
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) as async_event:
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if (
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not event
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or not self.current_run_id
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or self.current_run_id != event.run_id
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):
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continue
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# ========== 运行开始 ==========
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if event.event_kind == EventKind.RUN_START:
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# 将事件运行唯一标识设置为当前运行唯一标识
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self.current_run_id = event.run_id
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# 创建运行
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conversation.runs[self.current_run_id] = Run(
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user_prompt=user_prompt, is_streaming=True
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)
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yield # 通知前端渲染
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continue
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# 当前运行
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current_run = current_conversation.runs[self.current_run_id]
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# ========== 推理 ==========
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if (
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event.event_kind == EventKind.PART_START
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and event.part_kind == PartKind.THINKING
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):
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current_run.thinking += event.event_content
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yield # 通知前端渲染
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continue
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if part_index := event.part_index:
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if part_index not in current_run.reasonings:
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current_run.reasonings[part_index] += event.event_content
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yield # 通知前端渲染
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continue
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# ========== 回答 ==========
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if (
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event.part_kind == PartKind.TEXT
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and event.event_content != EventKind.PART_END
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):
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current_run.answer += event.event_content
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yield # 通知前端渲染
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continue
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# ========== 运行结束 ==========
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if event.event_kind == EventKind.RUN_END:
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# 保存运行新增消息
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await database_state.save_new_message(
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conversation_id=self.current_conversation_id,
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run_id=self.current_run_id,
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run_new_message=event.run_new_messages,
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)
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# 设置当前运行流式输出状态非正在流式输出
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current_run.is_streaming = False
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yield # 通知前端渲染
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continue
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