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♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 (#2149)
* ♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 - 【重构】重构 `BaseRunnable` 并引入统一的 `RunIntent` 意图载体，规范 Agent、Team 和 Workflow 的执行流 - 【解耦】将中期记忆槽和长期向量记忆从 `MemoryConfig` 中解耦，转为独立的能力组件与工具箱进行管理 - 【记忆】移除 `MemoryReader` 和 `MemoryWriter`，统一封装为 `SessionMemoryContext` 会话记忆门面 - 【RAG】重构检索器与存储后端接口，统一采用 `QueryRequest` 进行多维度联合检索，并引入 `InMemoryScorer` 提升打分性能 - 【事件】优化 `EventBus` 异步事件分发机制，引入队列机制确保事件按序处理，避免并发竞态问题 - 【依赖注入】移除 `memory` 注入项，优化 `DependencyInjector` 的签名解析缓存以提升性能 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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webjoin111
pre-commit-ci[bot]
webjoin111
pre-commit-ci[bot]
parent
922d092650
commit
52f7dbdedf
@@ -1,7 +1,8 @@
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from abc import ABC, abstractmethod
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import asyncio
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from collections.abc import Awaitable, Callable
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import json
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from typing import Any
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from typing import Any, Literal, cast
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from zhenxun.services.ai.capabilities import CombinedCapability
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from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
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@@ -27,8 +28,9 @@ from zhenxun.services.ai.core.messages import (
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ToolReturnPart,
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VideoPart,
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)
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from zhenxun.services.ai.core.models import LLMContext
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from zhenxun.services.ai.core.models import CancellationToken, LLMContext, ToolChoice
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from zhenxun.services.ai.core.options import GenerationConfig
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from zhenxun.services.ai.core.protocols.tool import ToolExecutable
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from zhenxun.services.ai.core.stream_events import (
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LLMEndEvent,
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LLMStartEvent,
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@@ -37,7 +39,8 @@ from zhenxun.services.ai.core.stream_events import (
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from zhenxun.services.ai.flow.agent.models import AgentRunResources, AgentState
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from zhenxun.services.ai.llm.engine.router import LLMOrchestrator
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from zhenxun.services.ai.run import AgentRunResult, RunContext
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from zhenxun.services.ai.run.session import session_manager
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from zhenxun.services.ai.run.models import OutputDataT
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from zhenxun.services.ai.run.session import SessionInfo, session_manager
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from zhenxun.services.ai.tools.engine.executor import ToolExecutor
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from zhenxun.services.ai.tools.models import ToolResult
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from zhenxun.services.ai.utils.logger import log_agent as logger
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@@ -57,7 +60,7 @@ class BaseAgentExecutor(ABC):
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async def run(
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self, state: AgentState, resources: AgentRunResources
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) -> AgentRunResult[Any]:
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) -> AgentRunResult[OutputDataT]:
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"""
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核心模板方法 (Template Method)。
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组织整个大模型推导与工具调用的生命周期循环。如无必要，请勿重写此方法。
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@@ -156,7 +159,7 @@ class BaseAgentExecutor(ABC):
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@abstractmethod
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async def on_fallback(
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self, state: AgentState, resources: AgentRunResources
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) -> AgentRunResult[Any]:
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) -> AgentRunResult[OutputDataT]:
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"""生命周期: 当大模型思考循环达到 max_cycles 时触发，执行兜底策略。"""
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pass
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@@ -181,7 +184,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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return result.is_retryable
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def _check_follow_up(
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self, state: AgentState, resources: AgentRunResources, session_info: Any
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self, state: AgentState, resources: AgentRunResources, session_info: SessionInfo
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) -> bool:
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"""检查追加队列，排空并合并数据到上下文，返回是否发现新消息"""
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follow_ups = session_info.follow_up_queue.drain()
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@@ -201,8 +204,8 @@ class StandardAgentExecutor(BaseAgentExecutor):
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state: AgentState,
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resources: AgentRunResources,
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messages: list[AgentMessage],
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tools: list[Any] | None,
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tool_choice: Any = None,
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tools: list[ToolExecutable | dict[str, Any]] | None,
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tool_choice: str | dict[str, Any] | ToolChoice | None = None,
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) -> ChatResponse:
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"""执行 LLM 请求，处理基础指标遥测统计，并将新对话上下文追加到状态流"""
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run_context = resources.run_context
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@@ -274,10 +277,10 @@ class StandardAgentExecutor(BaseAgentExecutor):
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messages: list[LLMMessage],
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config: GenerationConfig,
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run_context: RunContext,
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tools: list[Any] | None = None,
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tool_choice: Any = None,
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tools: list[ToolExecutable | dict[str, Any]] | None = None,
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tool_choice: str | dict[str, Any] | ToolChoice | None = None,
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extra: dict[str, Any] | None = None,
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cancellation_token: Any = None,
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cancellation_token: CancellationToken | None = None,
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) -> ChatResponse:
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request = ChatRequest(
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messages=messages,
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@@ -307,9 +310,24 @@ class StandardAgentExecutor(BaseAgentExecutor):
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async def on_start(self, state: AgentState, resources: AgentRunResources) -> None:
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resources.run_context.run.messages = state.messages
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async def _execute_phase(
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self,
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phase_func: Callable[[AgentState, AgentRunResources], Awaitable[None]],
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state: AgentState,
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resources: AgentRunResources,
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session_info: SessionInfo,
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) -> Literal["NEXT", "RESET_CYCLE", "FINISH"]:
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"""统一处理阶段执行与状态机完成/追加检查的高阶函数"""
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await phase_func(state, resources)
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if state.is_finished:
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if self._check_follow_up(state, resources, session_info):
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return "RESET_CYCLE"
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return "FINISH"
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return "NEXT"
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async def run(
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self, state: AgentState, resources: AgentRunResources
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) -> AgentRunResult[Any]:
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) -> AgentRunResult[OutputDataT]:
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"""覆盖基类的模板方法，实现灵活的 while 控制流和 FOLLOW_UP 合并"""
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await self.on_start(state, resources)
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session_info = await session_manager.get_or_create(
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@@ -325,29 +343,35 @@ class StandardAgentExecutor(BaseAgentExecutor):
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await self.build_llm_request(state, resources)
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await self.execute_llm(state, resources)
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await self.handle_llm_response(state, resources)
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if state.is_finished:
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if self._check_follow_up(state, resources, session_info):
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cycle_count = 0
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continue
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status = await self._execute_phase(
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self.handle_llm_response, state, resources, session_info
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)
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if status == "RESET_CYCLE":
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cycle_count = 0
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continue
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if status == "FINISH":
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assert state.final_result is not None
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return state.final_result
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await self.filter_tool_calls(state, resources)
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if state.is_finished:
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if self._check_follow_up(state, resources, session_info):
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cycle_count = 0
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continue
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status = await self._execute_phase(
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self.filter_tool_calls, state, resources, session_info
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)
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if status == "RESET_CYCLE":
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cycle_count = 0
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continue
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if status == "FINISH":
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assert state.final_result is not None
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return state.final_result
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await self.execute_tools(state, resources)
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await self.handle_tool_results(state, resources)
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if state.is_finished:
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if self._check_follow_up(state, resources, session_info):
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cycle_count = 0
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continue
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status = await self._execute_phase(
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self.handle_tool_results, state, resources, session_info
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)
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if status == "RESET_CYCLE":
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cycle_count = 0
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continue
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if status == "FINISH":
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assert state.final_result is not None
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return state.final_result
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@@ -544,7 +568,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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def _assemble_tool_message(
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self,
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original_call: ToolCallPart,
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res_or_exc: Any,
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res_or_exc: BaseException | tuple[ToolCallPart, ToolResult],
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tool_res: ToolResult | None,
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state: AgentState,
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) -> LLMMessage:
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@@ -631,7 +655,7 @@ class StandardAgentExecutor(BaseAgentExecutor):
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async def on_fallback(
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self, state: AgentState, resources: AgentRunResources
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) -> AgentRunResult[Any]:
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) -> AgentRunResult[OutputDataT]:
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run_context = resources.run_context
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if not resources.config.enable_fallback_summary:
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@@ -666,10 +690,13 @@ class StandardAgentExecutor(BaseAgentExecutor):
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tool_choice="none",
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)
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return model_construct(
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AgentRunResult,
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output=fallback_response.text,
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messages=state.messages,
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structured_data=None,
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usage=state.usage,
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return cast(
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AgentRunResult[OutputDataT],
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model_construct(
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AgentRunResult,
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output=fallback_response.text,
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messages=state.messages,
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structured_data=None,
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usage=state.usage,
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),
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)
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