feat：新增代码注释
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@@ -16,25 +16,34 @@ from app.schemas.actions import ActionBatch, AddAction
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def can_generate_actions(state: QueryState) -> bool:
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# 没有阻塞原因时才允许生成动作，路由和节点入口复用同一套能力检查
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return action_block_reason(state) is None
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def action_block_reason(state: QueryState) -> str | None:
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# 路由和失败说明共用能力判断，避免支持范围不一致
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# 如果已有大屏规划，按规划检查资源、目标和操作是否受支持
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if state.get("screen_plan") is not None:
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return plan_block_reason(state)
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# 没有规划时，读取需求和范围，检查是否可以直接生成简单修改动作
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requirement = state["requirement"]
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scope = state["change_scope"]
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# 如果需求尚未澄清完整，不能猜测用户意图并生成动作
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if requirement.needs_clarification:
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return "需求尚未澄清完整，不能生成动作"
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# 如果需要规划或不是修改已有大屏，则必须先完成规划流程
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if scope.needs_planning or requirement.intent != "modify_screen":
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return "当前需求需要先完成规划，不能直接生成动作"
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# 如果仍需查询额外资源，当前直接操作流程无法满足需求
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if scope.resource_needs:
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return "尚未接入所需资源查询：" + "；".join(scope.resource_needs)
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# 如果没有具体变更，就没有可转换为动作的需求
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if not requirement.changes:
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return "需求中没有可执行的变更"
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# 收集不支持的目标范围与操作组合，防止只执行需求中受支持的部分
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# 直接操作仅支持下列已有节点修改和画布属性修改
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unsupported = [
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f"{change.target_scope}/{change.operation}"
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for change in requirement.changes
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@@ -45,8 +54,11 @@ def action_block_reason(state: QueryState) -> str | None:
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and change.operation in ("update", "resize", "update_canvas")
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)
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]
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# 如果存在不支持的操作，按出现顺序去重后返回原因，便于展示失败信息
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if unsupported:
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return "当前尚未支持的直接操作：" + "、".join(dict.fromkeys(unsupported))
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# 所有检查通过，返回 None 表示没有阻塞原因
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return None
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@@ -54,10 +66,14 @@ class ActionNode(BaseNode):
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name = "action_node"
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async def process(self, state: QueryState) -> QueryState:
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# 浅拷贝状态
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updated_state: QueryState = state.copy()
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# 如果已有规划但不满足执行条件，记录原因并直接结束本节点，避免无效模型调用
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if state.get("screen_plan") is not None and (
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reason := plan_block_reason(state)
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):
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# 清空旧动作及模拟结果，标记失败，防止后续误用上一轮生成内容
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updated_state.update(
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actions=None,
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validation_passed=False,
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@@ -67,21 +83,30 @@ class ActionNode(BaseNode):
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status="failed",
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)
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return updated_state
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# 如果未通过动作能力检查，抛出异常，阻止不受支持的需求进入生成流程
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if not can_generate_actions(state):
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raise ValueError(
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"ActionNode 当前只支持无需规划和资源的 move、resize、update、delete、update_canvas 需求"
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)
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# 将输入上下文、结构化需求和修改范围转成字典，作为本次模型输入
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payload = {
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"input_context": state["input_context"].model_dump(mode="json"),
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"requirement": state["requirement"].model_dump(mode="json"),
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"change_scope": state["change_scope"].model_dump(mode="json"),
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}
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# 如果已有规划，补充规划内容及新增组件需要的物料配置
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if state.get("screen_plan") is not None:
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payload["screen_plan"] = state["screen_plan"].model_dump(mode="json")
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payload["component_materials"] = plan_materials(state)
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# 如果本轮是动作修正，提供修正建议、上一轮动作和校验错误供模型参考
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if state.get("repair_plan") is not None:
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payload.update(
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repair_plan=state["repair_plan"].model_dump(mode="json"),
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# 上一轮可能在解析阶段就失败，没有动作时按空列表传入
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previous_actions=[
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action.model_dump(mode="json")
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for action in state.get("actions") or []
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@@ -90,9 +115,10 @@ class ActionNode(BaseNode):
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error.model_dump() for error in state.get("validation_errors", [])
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],
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)
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# 配置和网络错误继续抛出，仅模型输出错误进入内容修正循环
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llm_client = get_llm(ActionBatch)
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# 旧动作及错误已放入模型输入，现在清空输出状态，避免本轮失败后残留旧结果
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updated_state["actions"] = None
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updated_state["validation_passed"] = None
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updated_state["validation_errors"] = []
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@@ -100,7 +126,9 @@ class ActionNode(BaseNode):
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updated_state["failure_reason"] = None
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updated_state["status"] = "running"
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# 捕获输出结构和解析错误，转换成统一校验结果供后续修正流程使用
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try:
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# 异步调用模型，根据需求、规划及是否正在修正选择对应的系统规则
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result = await llm_client.ainvoke(
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[
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SystemMessage(
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@@ -110,22 +138,35 @@ class ActionNode(BaseNode):
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repairing=state.get("repair_plan") is not None,
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)
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),
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# 将本次具体资料放入用户消息，保留中文以便阅读和排查
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HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
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]
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)
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# 将模型对象转成字典，非模型返回值保持原样，统一交给后面的协议校验
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raw = result.model_dump() if isinstance(result, BaseModel) else result
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# 规划生成的新增节点只保留显式提供的字段，避免模型默认值覆盖物料默认配置
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if isinstance(result, ActionBatch) and state.get("screen_plan") is not None:
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# 将原动作对象与序列化结果逐项对应，只调整新增动作的节点数据
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for source, target in zip(result.actions, raw["actions"]):
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if isinstance(source, AddAction):
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target["node"] = source.node.model_dump(exclude_unset=True)
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# 统一校验整批动作的字段和结构，格式不合法时抛出 ValidationError
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batch = ActionBatch.model_validate(raw)
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# 如果已有规划，使用物料默认配置补全新增节点，并同步示例数据标记
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if state.get("screen_plan") is not None:
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batch = assemble_plan_actions(state, batch)
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except ValidationError as error:
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# 将结构校验异常转换为统一错误列表，返回状态以进入后续修正判断
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = schema_errors(error)
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return updated_state
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except OutputParserException:
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# 如果模型输出无法解析，记录生成错误，提示后续按 ActionBatch 协议重新生成
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = [
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ActionValidationError(
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@@ -135,7 +176,10 @@ class ActionNode(BaseNode):
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]
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return updated_state
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# 保存生成的动作，实际目标、规划一致性及模拟执行结果仍由后续校验节点检查
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updated_state["actions"] = batch.actions
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# 返回待校验状态，此时 validation_passed 仍为 None，不代表动作已执行或通过校验
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return updated_state
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