from __future__ import annotations """ 运行时（Run）相关核心类型定义 """ from collections.abc import AsyncIterator import json from typing import Any, Generic, cast from pydantic import BaseModel, ConfigDict, Field, PrivateAttr from zhenxun.services.ai.core.messages import AgentMessage, LLMMessage, UsageInfo from zhenxun.services.ai.core.messages.types import OutputDataT from zhenxun.services.ai.core.options import BaseOutputDefinition from zhenxun.services.ai.core.stream_events import ( AgentStreamEvent, EventBus, ToolCallStartEvent, ToolStreamChunkEvent, ) from zhenxun.services.ai.guardrails import BaseGuardrail, GuardrailSource from zhenxun.utils.pydantic_compat import model_dump, model_validator class ChatSummary(BaseModel): total: int = 0 """大模型调用总次数""" total_latency_ms: float = 0.0 """大模型调用总耗时（毫秒）""" by_stop_reason: dict[str, int] = Field(default_factory=dict) """按停止原因分类的大模型调用计数""" class ToolSummary(BaseModel): total: int = 0 """工具执行总次数""" ok: int = 0 """工具成功执行次数""" error: int = 0 """工具执行失败次数""" total_latency_ms: float = 0.0 """工具执行总耗时（毫秒）""" by_name: dict[str, dict[str, float | int]] = Field(default_factory=dict) """按工具名称细分的执行状态统计""" class AgentRunSummary(BaseModel): """Agent 单次运行的全局可观测性遥测摘要""" chats: ChatSummary = Field(default_factory=ChatSummary) """大模型调用遥测摘要""" tools: ToolSummary = Field(default_factory=ToolSummary) """工具执行遥测摘要""" usage: UsageInfo = Field(default_factory=UsageInfo) """Token 消耗总计""" total_latency_ms: float = 0.0 """智能体运行总耗时（毫秒）""" class HandoffPayload(BaseModel): """移交信息载荷""" target: str """移交的目标智能体名称""" reason: str = "" """移交的原因描述""" context_data: Any = "" """随移交传递的上下文数据""" class AgentRunResult(BaseModel, Generic[OutputDataT]): """Agent 单次无状态运行的结果""" output: OutputDataT """最终输出数据""" messages: list[AgentMessage] = Field(default_factory=list) """本次运行产生/更新的历史消息""" usage: UsageInfo = Field(default_factory=UsageInfo) """本次运行的Token消耗总计""" structured_data: Any | None = None """拦截到的结构化结果字典""" telemetry: AgentRunSummary | None = None """单次运行的完整可观测性遥测摘要""" handoff: HandoffPayload | None = None """向外抛出的软移交载荷（存在时说明Agent发起了移交请求）""" class Config: arbitrary_types_allowed = True @property def llm_messages(self) -> list[LLMMessage]: """动态视图：过滤掉内部业务事件 (AgentEvent)， 仅返回纯净的底层聊天消息历史""" return [m for m in self.messages if isinstance(m, LLMMessage)] class AgentRunStart(AgentStreamEvent): """智能体运行开始""" agent_name: str """启动运行的智能体名称""" class AgentRunError(AgentStreamEvent): """智能体运行发生异常""" error: BaseException """智能体运行过程中抛出的异常""" class AgentRunEnd(AgentStreamEvent): """智能体运行完全结束""" result: AgentRunResult[Any] """智能体运行结束后的完整结果""" class StreamedRunResult(Generic[OutputDataT]): """ 智能体流式运行的结果代理对象。 提供高度解耦的方法来消费底层事件流，支持获取纯净文本或全部事件。 """ def __init__(self, event_bus: EventBus): self._event_bus = event_bus self.is_complete: bool = False self._result: AgentRunResult[OutputDataT] | None = None async def stream_events(self) -> "AsyncIterator[AgentStreamEvent]": """获取底层的所有原始事件（包含工具调用过程等）""" async for event in self._event_bus: if isinstance(event, AgentRunEnd): self._result = cast(AgentRunResult[OutputDataT], event.result) self.is_complete = True elif isinstance(event, AgentRunError): raise event.error.with_traceback(None) from None yield event async def stream_text(self, delta: bool = False) -> AsyncIterator[str]: """ 过滤大模型的输出文本。 """ full_text = "" async for _ in self.stream_events(): pass if self._result is not None: output = self._result.output if isinstance(output, str): full_text = output else: if isinstance(output, BaseModel): full_text = json.dumps(model_dump(output), ensure_ascii=False) else: full_text = str(output) if delta: yield full_text else: yield full_text async def get_output(self) -> OutputDataT: """阻塞并等待整个 Agent 执行完毕，返回最终的解析输出数据""" if self._result is not None: return self._result.output async for _ in self.stream_events(): pass if self._result is None: raise RuntimeError("Agent 运行异常结束，未产生最终结果。") return self._result.output async def get_run_result(self) -> AgentRunResult[OutputDataT]: """获取完整的运行结果对象（包含 Token 消耗等）""" if self._result is not None: return self._result await self.get_output() return cast(AgentRunResult[OutputDataT], self._result) async def forward_to( self, target_event_bus: EventBus | None, prefix_name: str ) -> AgentRunResult[OutputDataT]: """将底层的事件流自动格式化并转发给另一个事件发射器， 常用于 DelegateTool 嵌套调用""" async for event in self.stream_events(): if not target_event_bus: continue if isinstance(event, ToolStreamChunkEvent): await target_event_bus.emit( ToolStreamChunkEvent( tool_name=f"{prefix_name} -> {event.tool_name}", content=event.content, metadata=event.metadata, ) ) elif isinstance(event, ToolCallStartEvent): intent_str = ( f" (意图: {event.intent})" if getattr(event, "intent", None) else "" ) await target_event_bus.emit( ToolStreamChunkEvent( tool_name=prefix_name, content=f"🔁 正在调用工具: {event.tool_name}...{intent_str}", ) ) return await self.get_run_result() class AgentTask(BaseModel): """标准化数据契约（意图载体 Payload），定义大模型需要做什么及产出什么格式""" id: str = Field(default_factory=lambda: __import__("uuid").uuid4().hex) """任务的唯一标识符""" name: str | None = None """任务的简短名称""" description: str """详细的任务指令（告诉大模型具体需要做什么）""" expected_output: str """预期输出的自然语言描述（指导大模型如何组织最终答案）""" response_model: type[BaseModel] | BaseOutputDefinition | None = None """强制要求返回的强类型结构 (Pydantic Model) 或 OutputDefinition，为空则返回普通文本""" tools: list[Any] | None = None """针对此特定任务动态追加或覆盖的工具列表""" guardrails: list[GuardrailSource] | None = None """护栏验证列表。支持传入函数、BaseGuardrail 实例， 或直接传入自然语言字符串规则（自动转为 LLM 裁判）""" _parsed_guardrails: list[BaseGuardrail] = PrivateAttr(default_factory=list) model_config = ConfigDict(arbitrary_types_allowed=True) @model_validator(mode="after") def _parse_and_set_guardrails(self) -> AgentTask: from zhenxun.services.ai.guardrails import parse_guardrails self._parsed_guardrails = parse_guardrails(self.guardrails) return self class RunIntent(BaseModel): """标准化且归一化的运行时意图载体""" text: str = "" """提取出的纯文本指令（用于路由、日志和并发控制判断）""" original_input: Any = None """用户最原始的输入对象（如 UniMessage 等，用于多模态图像/音频数据提取）""" payload_to_render: Any = None """将要被压入 MessageBuilder 渲染为大模型 Prompt 的实际载体""" task_obj: AgentTask | None = None """如果是强类型任务契约，存储其原始对象引用""" response_model: type[BaseModel] | BaseOutputDefinition | None = None """提取出的强类型输出约束""" extra_tools: list[Any] = Field(default_factory=list) """提取出的附加工具集""" guardrails: list[BaseGuardrail] = Field(default_factory=list) """提取出的安全护栏集""" model_config = ConfigDict(arbitrary_types_allowed=True) @classmethod def from_input(cls, prompt: Any) -> "RunIntent": task_obj = None text_content = "" extra_tools = [] response_model = None guardrails = [] payload_to_render = prompt if isinstance(prompt, AgentTask): task_obj = prompt response_model = task_obj.response_model if task_obj.tools: extra_tools.extend(task_obj.tools) if hasattr(task_obj, "_parsed_guardrails"): guardrails.extend(task_obj._parsed_guardrails) prompt_parts = [ f"### 📋 [任务指令]\n{task_obj.description}", f"### 🎯 [预期产出要求]\n{task_obj.expected_output}", ] text_content = "\n\n".join(prompt_parts) payload_to_render = text_content elif prompt is not None: text_content = getattr(prompt, "description", None) or ( getattr(prompt, "extract_plain_text", lambda: str(prompt))() if prompt else str(prompt) ) else: text_content = "" payload_to_render = None return cls( text=text_content, original_input=prompt, payload_to_render=payload_to_render, task_obj=task_obj, response_model=response_model, extra_tools=extra_tools, guardrails=guardrails, ) __all__ = [ "AgentRunResult", "AgentTask", "OutputDataT", "RunIntent", "StreamedRunResult", ]