267 lines
11 KiB
Python
267 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Pydantic AI 聊天智能体和相关模块
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"""
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# 列举导入模块
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from enum import StrEnum
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from typing import AsyncGenerator, List, Optional, Union
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from uuid import uuid4
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from numpy._core.numeric import str_
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from pydantic_ai import Agent
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from pydantic_ai.capabilities import AgentCapability
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from pydantic_ai.messages import (
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AgentStreamEvent,
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ModelMessage,
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PartStartEvent,
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ThinkingPart,
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ToolSearchCallPart,
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ThinkingPartDelta,
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ToolCallPart,
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LoadCapabilityCallPart,
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TextPartDelta,
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ToolCallPartDelta,
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FunctionToolResultEvent,
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PartDeltaEvent,
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TextPart,
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PartEndEvent,
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)
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.output import OutputSpec
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from pydantic_ai.providers.openai import OpenAIProvider
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from pydantic_ai.run import AgentRunResultEvent
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from pydantic import BaseModel, Field
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DEFAULT_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 Event(BaseModel):
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"""
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事件类
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"""
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event_kind: str = Field(..., description="事件种类")
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content: Optional[str] = Field(default=None, description="事件内容")
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part_index: Optional[int] = Field(default=None, description="分片索引")
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part_kind: str = Field(..., description="分片种类")
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tool_name: Optional[str] = Field(default=None, description="工具名称")
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class AIAgent:
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"""
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基于 Pydantic AI 封装的智能体
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"""
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def __init__(
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self,
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chat_id: str,
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instructions: Optional[str] = None,
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output_type: OutputSpec = str,
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capabilities: Optional[List[AgentCapability]] = None,
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retries: int = 1,
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):
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"""
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初始化
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:param chat_id: 聊天唯一标识
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:param instructions: 指令
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:param capabilities: 技能列表，默认为不使用技能
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:param output_type: 输出类型
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:param retries: 重试次数，默认为1次
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:return: 智能体实例
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"""
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# 聊天唯一标识
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self.chat_id = chat_id
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# 一次聊天（chat）包含若干论对话（dialog），每轮对话包含用户提示词（user_prompt）和输出（output）。其中，输出包含若干分片（Part）
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# 本轮对话工具调用唯一标识与片段索引映射表
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self.tool_call_ids: dict[str, int] = {}
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# 本轮对话新增消息列表
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self.new_messages: List[ModelMessage] = []
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# 若指令为空则使用默认指令
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if not instructions:
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instructions = DEFAULT_INSTRUCTIONS
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# 初始化智能体
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self.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=capabilities,
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output_type=output_type,
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retries=retries,
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)
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async def run(
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self,
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user_prompt: str | List[str],
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message_history: Optional[List[ModelMessage]] = None,
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) -> AsyncGenerator[Event]:
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"""
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运行
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:param user_prompt: 用户提示词（用户提示词）
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:param flush_delay: 刷新延迟时长（单位为秒）
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:yield: AsyncGenerator[Event]
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"""
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async with self.agent.run_stream_events(
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user_prompt=user_prompt,
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message_history=message_history,
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) as events:
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async for event in events:
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match event:
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# ========== 分片开始事件 ==========
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case PartStartEvent(event_kind=event_kind, index=index, part=part):
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match part:
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# 思考分片开始事件
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case ThinkingPart(part_kind=part_kind, content=content):
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yield Event(
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event_kind=event_kind,
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content=content,
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part_index=index,
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part_kind=part_kind,
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)
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# 检索工具分片开始事件
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case ToolSearchCallPart(
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part_kind=part_kind, tool_call_id=tool_call_id, tool_name=tool_name
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):
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# 记录工具调用唯一标识与片段索引映射
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self.tool_call_ids[tool_call_id] = index
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 加载能力分片开始事件
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case LoadCapabilityCallPart(
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part_kind=part_kind, tool_call_id=tool_call_id, tool_name=tool_name
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):
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# 记录工具调用唯一标识与片段索引映射
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self.tool_call_ids[tool_call_id] = index
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 调用工具分片开始事件
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case ToolCallPart(
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part_kind=part_kind,
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tool_call_id=tool_call_id,
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tool_name=tool_name,
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):
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# 记录工具调用唯一标识与片段索引映射
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self.tool_call_ids[tool_call_id] = index
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 文本分片开始事件
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case TextPart(part_kind=part_kind, content=content):
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yield Event(
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event_kind=event_kind,
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content=content,
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part_index=index,
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part_kind=part_kind,
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)
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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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event_kind=event_kind, index=index, delta=delta
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):
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match delta:
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# 文本分片增量事件
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case TextPartDelta(
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part_delta_kind=part_delta_kind,
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content_delta=content_delta,
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):
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yield Event(
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event_kind=event_kind,
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content=content_delta, # 增量
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part_index=index,
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part_kind=part_delta_kind,
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)
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# ========== 分片结束事件 ==========
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case PartEndEvent(event_kind=event_kind, index=index, part=part):
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match part:
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# 思考分片结束事件
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case ThinkingPart(part_kind=part_kind, content=content):
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yield Event(
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event_kind=event_kind,
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content=content,
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part_index=index,
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part_kind=part_kind,
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)
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# 检索工具分片结束事件
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case ToolSearchCallPart(part_kind=part_kind, tool_name=tool_name):
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 加载能力分片结束事件
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case LoadCapabilityCallPart(part_kind=part_kind, tool_name=tool_name):
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 调用工具分片结束事件
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case ToolCallPart(part_kind=part_kind, tool_name=tool_name):
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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# 文本分片结束事件
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case TextPart(part_kind=part_kind):
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yield Event(
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event_kind=event_kind,
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part_index=index,
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part_kind=part_kind,
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)
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# ========== 函数工具结果回调事件 ==========
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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=event_kind,
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content=content,
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part_index=index,
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part_kind=part_kind,
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tool_name=tool_name,
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)
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