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zhenxun_bot/zhenxun/services/ai/flow/workflow/base.py
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52f7dbdedf ♻️ 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>
2026-07-14 16:48:33 +08:00

234 lines
8.2 KiB
Python

from abc import ABC, abstractmethod
import asyncio
from collections.abc import AsyncIterator
from typing import cast
from zhenxun.services.ai.core.exceptions import (
AbortException,
ControlFlowExit,
ToolFatalError,
)
from zhenxun.services.ai.core.stream_events import AgentStreamEvent
from zhenxun.services.ai.run import RunContext
from zhenxun.services.ai.utils.logger import log_flow as logger
from .policies import (
AbortPolicy,
BaseFailurePolicy,
PolicyAction,
)
from .types import (
StepInput,
StepOutput,
StepType,
)
class StreamCapturer:
"""内部辅助类:透传工作流节点的内部流事件,并捕获最终的 StepOutput 产出值"""
def __init__(self, stream: AsyncIterator[AgentStreamEvent | StepOutput]):
self._stream = stream
self.output: StepOutput | None = None
async def __aiter__(self) -> AsyncIterator[AgentStreamEvent]:
async for event in self._stream:
if isinstance(event, StepOutput):
self.output = event
else:
yield event
class BaseNode(ABC):
"""工作流节点统一抽象基类"""
def __init__(
self,
name: str,
failure_policy: BaseFailurePolicy | None = None,
):
"""
初始化工作流节点基类。
参数:
name: 节点的唯一名称标识。
failure_policy: 该节点执行失败时的错误恢复与自愈策略,
默认使用中断策略 (AbortPolicy)。
"""
self.name = name
self.failure_policy = failure_policy or AbortPolicy()
@property
@abstractmethod
def node_type(self) -> StepType:
"""节点类型标识 (供子类实现)"""
pass
async def _handle_execution_failure(
self, e: BaseException, step_input: StepInput, context: RunContext, attempt: int
) -> tuple[str, StepOutput | None, StepInput | None, "BaseNode | None"]:
"""
解析执行异常并应用容错策略
"""
if isinstance(e, asyncio.CancelledError):
raise e
if isinstance(e, ControlFlowExit):
logger.info(
f"⏭️ [控制流拦截] Node '{self.name}' 触发中断信号: "
f"{type(e).__name__} - {e}"
)
content = str(e)
if getattr(e, "display_content", None):
content = str(getattr(e, "display_content"))
elif getattr(e, "display", None):
content = str(getattr(e, "display"))
elif getattr(e, "result_output", None):
content = str(getattr(e, "result_output"))
output = StepOutput(
step_name=self.name,
step_type=self.node_type,
content=content,
success=False,
stop=True,
error=f"{type(e).__name__}: {e}"
if isinstance(e, AbortException | ToolFatalError)
else None,
)
return "break", output, None, None
logger.warning(f"Node '{self.name}' 执行发生异常: {e}")
policy_result = await self.failure_policy.handle_failure(
self, e, step_input, context
)
if policy_result.action == PolicyAction.RETRY:
if policy_result.delay > 0:
await asyncio.sleep(policy_result.delay)
new_input = policy_result.new_input or step_input
logger.debug(f" 🔄 [节点重试] `{self.name}` 进行第 {attempt} 次重试...")
return "continue", None, new_input, None
elif policy_result.action == PolicyAction.FALLBACK:
fallback_node = policy_result.fallback_node
fallback_name = getattr(fallback_node, "name", "FallbackNode")
logger.info(
f"🔀 节点 {self.name} 执行失败,触发降级路由至: {fallback_name}"
)
return "fallback", None, None, fallback_node
elif policy_result.action == PolicyAction.CONTINUE:
logger.warning(f"Node '{self.name}' 执行异常,已被策略自动跳过: {e}")
output = StepOutput(
step_name=self.name,
step_type=self.node_type,
content=f"节点执行失败,已通过策略自动跳过: {type(e).__name__} - {e}",
success=False,
stop=False,
error=f"{type(e).__name__}: {e}",
)
return "break", output, None, None
else:
logger.error(f"Node '{self.name}' 执行崩溃,已被策略中断执行流: {e}")
output = StepOutput(
step_name=self.name,
step_type=self.node_type,
content=f"执行崩溃: {type(e).__name__} - {e}",
success=False,
stop=True,
error=f"{type(e).__name__}: {e}",
)
return "break", output, None, None
@abstractmethod
async def run_stream(
self, step_input: StepInput, context: RunContext
) -> AsyncIterator[AgentStreamEvent | StepOutput]:
"""子类必须实现的核心流式执行逻辑"""
if False:
yield cast(StepOutput, None)
async def aexecute(self, step_input: StepInput, context: RunContext) -> StepOutput:
"""非流式执行(聚合流并返回最终结果),子类无需重写"""
output = None
async for event in self.aexecute_stream(step_input, context):
if isinstance(event, StepOutput):
output = event
if output is None:
output = StepOutput(
step_name=self.name,
step_type=self.node_type,
content="节点未产生有效输出",
success=False,
)
return output
async def aexecute_stream(
self, step_input: StepInput, context: RunContext
) -> AsyncIterator[AgentStreamEvent | StepOutput]:
"""标准化模板方法:处理缓存快进、授权挂起、异常熔断与生命周期事件分发"""
logger.debug(f" ⚙️ [节点] `{self.name}` 开始执行...")
cached_out = context.state.get("__completed_steps__", {}).get(self.name)
if cached_out and cached_out.success:
logger.debug(f"⏭️ 快进跳过已完成节点: {self.name}")
yield cached_out
return
current_input = step_input
attempt = 1
while True:
output = None
try:
async for event in self.run_stream(current_input, context):
if isinstance(event, StepOutput):
output = event
output.step_name = self.name
output.step_type = self.node_type
else:
yield event
if output is None:
output = StepOutput(
step_name=self.name,
step_type=self.node_type,
content="执行完毕,无数据返回",
success=True,
)
context.upstream_results[self.name] = output.content
break
except BaseException as e:
(
action_cmd,
output,
new_input,
fallback_node,
) = await self._handle_execution_failure(
e, current_input, context, attempt
)
if action_cmd == "continue":
current_input = (
new_input if new_input is not None else current_input
)
attempt += 1
continue
elif action_cmd == "fallback" and fallback_node:
async for evt in fallback_node.aexecute_stream(
current_input, context
):
if isinstance(evt, StepOutput):
output = evt
else:
yield evt
break
assert output is not None
yield output