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@ -69,9 +69,23 @@ class PartKind(StrEnum):
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RUN_RETURN = "run-return"
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class Kind(StrEnum):
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"""分段种类"""
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THINKING = "thinking"
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TOOL_SEARCH = "tool-search"
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CAPABILITY_LOAD = "capability-load"
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TOOL_CALL = "tool-call"
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TEXT = "text"
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TOOL_RETURN = "tool-return"
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RETRY_PROMPT = "retry-prompt"
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RUN_RETURN = "run-return"
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class Reasoning(BaseModel):
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"""推理类"""
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kind: Kind = Field(..., description="推理类型")
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content: str = Field(default="", description="推理内容")
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@ -84,11 +98,7 @@ class Run(BaseModel):
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)
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is_reasoning: bool = Field(
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default=False,
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description="推理状态，True 表示正在推理，False 表示非正在推理",
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)
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is_collapsed: bool = Field(
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default=False,
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description="推理面板折叠状态，True 表示折叠，False 表示展开",
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description="正在推理状态，True 表示正在推理，False 表示非正在推理",
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)
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assistant_content: str = Field(default="", description="回复正文")
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is_streaming: bool = Field(
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@ -25,7 +25,7 @@ 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, Event, EventKind, PartKind
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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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@ -52,15 +52,10 @@ class ConversationState(rx.State):
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对话状态
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"""
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# 初始化当前对话唯一标识
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conversation_id: str = str(uuid7())
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# 初始化对话字典
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conversations: Dict[str, Conversation] = {conversation_id: Conversation()}
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# 初始化当前运行唯一标识
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run_id: Optional[str] = None
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# 初始化当前运行工具调用唯一标识与片段索引映射表
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run_mapping: dict[str, int] = {}
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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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@ -97,12 +92,10 @@ class ConversationState(rx.State):
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if not description:
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description = "新对话"
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# 生成并设置为当前对话唯一标识
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self.conversation_id = str(uuid7())
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# 创建对话
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self.conversations[self.conversation_id] = Conversation(description=description)
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# 初始化当前运行唯一标识
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self.run_id = None
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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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@ -122,24 +115,18 @@ class ConversationState(rx.State):
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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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# 生成并设置为当前对话唯一标识
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self.conversation_id = str(uuid7())
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# 创建对话
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self.conversations[self.conversation_id] = Conversation()
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# 初始化当前运行唯一标识
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self.run_id = None
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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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# 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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# 初始化当前运行唯一标识
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self.run_id = next(reversed(self.conversations[self.conversation_id].runs.keys()), None)
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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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设置为当前对话唯一标识
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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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@ -194,9 +181,6 @@ class ConversationState(rx.State):
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if not conversation:
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return
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# 更新当前运行唯一标识
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self.run_id = str(uuid7())
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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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@ -209,8 +193,16 @@ class ConversationState(rx.State):
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conversation_id=self.conversation_id
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)
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# 重置当前运行工具调用唯一标识与片段索引映射表
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self.run_mapping.clear()
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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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@ -220,7 +212,6 @@ class ConversationState(rx.State):
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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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event_kind=event_kind,
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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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@ -230,113 +221,56 @@ class ConversationState(rx.State):
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case ThinkingPart(
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part_kind=part_kind, content=content
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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=content,
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part_index=index,
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previous_part_kind=PartKind(
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previous_part_kind
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),
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part_kind=PartKind(part_kind),
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)
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# 检索工具分片开始事件
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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_kind=tool_kind,
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tool_call_id=tool_call_id,
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tool_name=tool_name,
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tool_call_id=tool_call_id
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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=EventKind(event_kind),
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part_index=index,
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previous_part_kind=PartKind(
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previous_part_kind
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),
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part_kind=PartKind(tool_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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# 加载能力分片开始事件
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case LoadCapabilityCallPart(
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tool_kind=tool_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=EventKind(event_kind),
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part_index=index,
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previous_part_kind=PartKind(
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previous_part_kind
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),
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part_kind=PartKind(tool_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=EventKind(event_kind),
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part_index=index,
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previous_part_kind=PartKind(
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previous_part_kind
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),
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part_kind=PartKind(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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# 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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yield Event(
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event_kind=EventKind(event_kind),
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event_content=content,
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part_index=index,
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previous_part_kind=PartKind(
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previous_part_kind
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),
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part_kind=PartKind(part_kind),
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run_id=current_run_id,
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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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event_kind=event_kind, index=index, delta=delta
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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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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=EventKind(event_kind),
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event_content=content_delta or "",
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part_index=index,
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part_kind=PartKind(part_delta_kind),
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run_id=current_run_id,
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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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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=EventKind(event_kind),
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event_content=content_delta,
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part_index=index,
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part_kind=PartKind(part_delta_kind),
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run_id=current_run_id,
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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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@ -350,26 +284,17 @@ class ConversationState(rx.State):
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case ThinkingPart(
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part_kind=part_kind, content=content
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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=content,
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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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run_id=current_run_id,
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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_kind=tool_kind, tool_name=tool_name
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tool_call_id=tool_call_id
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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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# 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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@ -394,18 +319,6 @@ class ConversationState(rx.State):
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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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# 文本分片结束事件
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case TextPart(
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part_kind=part_kind, content=content
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):
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yield Event(
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event_kind=EventKind(event_kind),
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event_content=content,
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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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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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