feat：架构重构
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+20
-204
@@ -1,4 +1,4 @@
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"""输入解析、意图分流和需求理解流程"""
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"""输入 → 意图（是不是明确的问答）→ 问答节点或 screen agent；agent 追问时暂停等待用户"""
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import inspect
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@@ -6,19 +6,10 @@ from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
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from langgraph.graph import END, StateGraph
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from app.config import settings
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from app.processor.nodes.action_node import ActionNode, can_generate_actions
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from app.processor.nodes.clarify_node import ClarifyNode
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from app.processor.nodes.component_resource_node import ComponentResourceNode
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from app.processor.nodes.design_node import DesignNode
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from app.processor.nodes.fallback_node import FallbackNode
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from app.processor.nodes.input_node import InputNode
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from app.processor.nodes.intent_node import IntentNode
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from app.processor.nodes.qa_node import QANode
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from app.processor.nodes.reflection_node import ReflectionNode
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from app.processor.nodes.requirement_node import RequirementNode
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from app.processor.nodes.scope_node import ScopeNode
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from app.processor.nodes.validator_node import ValidatorNode
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from app.processor.nodes.screen_agent_node import ScreenAgentNode
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from app.processor.nodes.wait_for_user_node import WaitForUserNode
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from app.processor.state import QueryState
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@@ -30,19 +21,12 @@ def build_checkpoint_serde() -> JsonPlusSerializer:
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allowed_msgpack_modules=[
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("app.schemas.input_context", "InputContext"),
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("app.schemas.decision", "DecisionResponse"),
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("app.schemas.requirement", "Requirement"),
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("app.schemas.change_scope", "ChangeScope"),
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("app.schemas.screen_plan", "ScreenPlan"),
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("app.schemas.component_resource", "ComponentResult"),
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("app.schemas.actions.add", "AddAction"),
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("app.schemas.actions.delete", "DeleteAction"),
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("app.schemas.actions.move", "MoveAction"),
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("app.schemas.actions.resize", "ResizeAction"),
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("app.schemas.actions.update", "UpdateAction"),
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("app.schemas.actions.update_canvas", "UpdateCanvasAction"),
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("app.schemas.action_validation", "ActionValidationError"),
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("app.schemas.action_repair", "ActionRepairPlan"),
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("app.schemas.screen", "Screen"),
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("app.schemas.clarification", "Clarification"),
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("app.schemas.clarification_reply", "ClarificationReply"),
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]
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@@ -50,221 +34,53 @@ def build_checkpoint_serde() -> JsonPlusSerializer:
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def route_after_intent(state: QueryState) -> str:
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# 按最终意图分流：问答进入 QA，意图不明进入澄清，新建或修改进入需求理解
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return state["intent"]
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return "qa_node" if state["intent"] == "qa" else "screen_agent_node"
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def route_after_requirement(state: QueryState) -> str:
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# 需求输出校验失败直接收尾，其余按落地策略的决定分流
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if state["status"] == "failed":
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return "fallback_node"
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outcome = state["requirement_outcome"]
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if outcome == "inform":
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# 目标不存在时直接告知用户，不执行也不追问
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return "qa_node"
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if outcome == "clarify":
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# 候选选项已由策略生成时直接等待用户，否则由澄清节点组织问题
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return "wait_for_user_node" if state.get("clarification") else "clarify_node"
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return "scope_node"
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def route_after_agent(state: QueryState) -> str:
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return "wait_for_user_node" if state["status"] == "waiting_for_input" else END
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def route_after_wait(state: QueryState) -> str:
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# 用户补充后回到输入节点合并上下文，取消则结束流程
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# 用户回答后回到输入节点合并上下文，取消则结束
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return "input_node" if state["status"] == "running" else END
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# TODO 目前“资源”只有物料目录，数据源和 RAG 尚未准备好
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# 接入后在 Requirement 中增加资源需求，与 needs_planning 作为两个独立开关：
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# 规划 -> 按需查资源 -> design；无需规划但有资源需求 -> 查资源 -> action
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# 并让 component_resource_node 按资源需求决定查什么，而不是固定全量目录
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# 接入时把 capabilities.RESOURCE_QUERY_SUPPORTED 改为 True；在此之前 DesignNode
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# 会清空资源需求并改用物料默认配置和示例数据，不因资源需求失败
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def route_after_scope(state: QueryState) -> str:
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# 已判定无法直接执行，失败收尾
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if state["status"] == "failed":
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return "fallback_node"
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# 需要规划时先获取组件能力，供规划节点选择可用组件
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if state["change_scope"].needs_planning:
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return "component_resource_node"
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# 已支持的简单修改直接生成动作，尚不支持的需求进入失败收尾
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return "action_node" if can_generate_actions(state) else "fallback_node"
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def build_graph(checkpointer=None):
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# 最大重试次数
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max_retries = settings.max_action_retries
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def route_failure(state: QueryState) -> str:
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# 上限检查，放在 Reflection 前，避免最后一次无效模型调用
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return (
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"reflection_node"
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if state.get("retry_count", 0) < max_retries
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else "fallback_node"
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)
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def route_after_action(state: QueryState) -> str:
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# 如果动作节点已明确标记失败，直接收尾，不再进入内容修正
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if state.get("status") == "failed":
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return "fallback_node"
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# validation_passed 为 False：动作生成时解析或结构校验失败，未达重试上限则进入修正分析，已达上限则失败收尾
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# validation_passed 为 None：动作已生成但尚未模拟执行，进入 validator_node 检查动作范围和执行结果
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# 必须用 is False 区分失败与尚未校验，不能将 None 也当作失败
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return (
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route_failure(state)
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if state["validation_passed"] is False
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else "validator_node"
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)
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def route_after_validation(state: QueryState) -> str:
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# 整批动作通过校验则结束，否则根据剩余重试次数决定修正或失败收尾
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return END if state["validation_passed"] else route_failure(state)
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def route_after_reflection(state: QueryState) -> str:
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# 获取失败分析产生的修正建议，生成建议失败时该字段可能为空
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plan = state.get("repair_plan")
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# 只有存在建议且明确允许重试时才重新生成动作，其余情况统一失败收尾
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return "action_node" if plan is not None and plan.retryable else "fallback_node"
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# 使用 QueryState 定义节点之间共享的状态结构
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workflow = StateGraph(QueryState)
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# 为本次构建的图创建节点实例，字典键同时作为注册名称和路由目标
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nodes = {
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"input_node": InputNode(),
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"intent_node": IntentNode(),
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"requirement_node": RequirementNode(),
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"clarify_node": ClarifyNode(),
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"wait_for_user_node": WaitForUserNode(),
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"qa_node": QANode(),
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"scope_node": ScopeNode(),
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"design_node": DesignNode(),
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"component_resource_node": ComponentResourceNode(),
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"action_node": ActionNode(),
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"validator_node": ValidatorNode(),
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"reflection_node": ReflectionNode(),
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"fallback_node": FallbackNode(),
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"screen_agent_node": ScreenAgentNode(),
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"wait_for_user_node": WaitForUserNode(),
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}
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# 注册节点，异步节点使用 BaseNode 的异步入口
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for node_name, node in nodes.items():
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# 根据 process 是否为异步函数选择调用入口，保留 BaseNode 的统一执行封装
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# 异步节点使用 BaseNode 的异步入口，保留统一的日志和进度封装
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action = node._acall if inspect.iscoroutinefunction(node.process) else node
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# 将节点名称与实际调用对象绑定，供后面的边引用
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workflow.add_node(node_name, action)
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# 首次执行从输入节点开始，完成上下文校验后再判断意图
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workflow.set_entry_point("input_node")
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# 输入处理完成后固定进入意图判断
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workflow.add_edge("input_node", "intent_node")
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# 新建和修改进入需求理解，意图不明进入澄清
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workflow.add_conditional_edges(
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source="intent_node",
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path=route_after_intent,
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path_map={
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"create_screen": "requirement_node",
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"modify_screen": "requirement_node",
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"qa": "qa_node",
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"unclear": "clarify_node",
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},
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"intent_node",
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route_after_intent,
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{"qa_node": "qa_node", "screen_agent_node": "screen_agent_node"},
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)
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# 需求信息不足时提问，信息完整时继续判断修改范围
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workflow.add_conditional_edges(
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source="requirement_node",
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path=route_after_requirement,
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path_map={
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"clarify_node": "clarify_node",
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"wait_for_user_node": "wait_for_user_node",
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"qa_node": "qa_node",
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"scope_node": "scope_node",
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"fallback_node": "fallback_node",
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},
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)
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# 先保存澄清问题和等待状态，再进入调用 interrupt 的节点暂停执行
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workflow.add_edge("clarify_node", "wait_for_user_node")
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# 问答生成文本后直接结束，不进入规划和动作流程
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workflow.add_edge("qa_node", END)
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# 先读取组件能力再规划，规划后的业务数据查询后续接入
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workflow.add_conditional_edges(
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"scope_node",
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route_after_scope,
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{
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"component_resource_node": "component_resource_node",
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"action_node": "action_node",
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"fallback_node": "fallback_node",
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},
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"screen_agent_node",
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route_after_agent,
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{"wait_for_user_node": "wait_for_user_node", END: END},
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)
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workflow.add_conditional_edges(
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"wait_for_user_node", route_after_wait, {"input_node": "input_node", END: END}
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)
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# 组件目录查询成功才允许设计，未找到或不支持时进入失败收尾
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workflow.add_conditional_edges(
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"component_resource_node",
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lambda state: state["component_resources"].status == "resolved",
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{True: "design_node", False: "fallback_node"},
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)
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# 设计（规划和动作一次生成）与动作生成的结果走同样的去向：校验、修正分析或失败收尾
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# 修正时规划已定，由 action_node 只重新生成动作
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for node_name in ("design_node", "action_node"):
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workflow.add_conditional_edges(
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node_name,
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route_after_action,
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{
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"validator_node": "validator_node",
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"reflection_node": "reflection_node",
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"fallback_node": "fallback_node",
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},
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)
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# 校验成功结束，失败则在重试上限内分析修正原因
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workflow.add_conditional_edges(
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"validator_node",
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route_after_validation,
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{
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END: END,
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"reflection_node": "reflection_node",
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"fallback_node": "fallback_node",
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},
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)
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# 修正分析允许重试时回到动作生成，形成生成、校验、分析的循环
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workflow.add_conditional_edges(
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"reflection_node",
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route_after_reflection,
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{
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"action_node": "action_node",
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"fallback_node": "fallback_node",
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},
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)
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# 失败节点写入最终失败状态后，由回答节点向用户说明原因
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workflow.add_edge("fallback_node", "qa_node")
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# 用户补充后重新合并输入并判断意图，取消则直接结束
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workflow.add_conditional_edges(
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"wait_for_user_node",
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route_after_wait,
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{"input_node": "input_node", END: END},
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)
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# 仅在调用方未提供检查点存储时创建默认实现，外部传入的持久化存储（如 MongoDB）直接复用
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# 调用方未提供检查点存储时使用进程内存，支持暂停恢复，进程退出后不保留
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if checkpointer is None:
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# 将检查点保存在当前进程内存中，支持暂停恢复，但进程退出后不会保留
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checkpointer = InMemorySaver(serde=build_checkpoint_serde())
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# 编译节点和边，并绑定检查点存储，供执行时按 thread_id 保存和恢复状态
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return workflow.compile(checkpointer=checkpointer).with_config(
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{
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# 随修正上限增加图执行步数预算，每轮预留分析、生成、校验三步，至少允许 25 步
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"recursion_limit": max(25, 12 + 3 * max_retries),
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}
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)
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return workflow.compile(checkpointer=checkpointer)
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# 模块加载时创建共享图实例，复用该实例的调用也共用其检查点存储
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