229 lines
10 KiB
Python
229 lines
10 KiB
Python
"""校验动作范围，再按顺序修改大屏 JSON 副本，检查整批动作能否执行"""
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from pydantic import BaseModel, ValidationError
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from app.actions.plan import validate_plan_actions, validate_plan_result
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from app.actions.registry import apply_action
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from app.processor.base import BaseNode
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from app.processor.state import QueryState
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from app.schemas.action_validation import ActionValidationError, schema_errors
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from app.schemas.actions import ActionBatch, AddAction, UpdateCanvasAction
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class ValidatorNode(BaseNode):
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name = "validator_node"
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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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# 校验失败仍可能进入修正循环，最终失败由 Fallback 标记
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updated_state["status"] = "running"
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# 获取本轮待校验的动作，先检查结构，再检查范围和模拟执行结果
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actions = state["actions"]
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try:
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# 重新校验模型内容，避免赋值后绕过字段校验
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batch = ActionBatch.model_validate(
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{
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"actions": [
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# 将动作模型转回字典，确保重新触发字段校验，字典输入则保持原样
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action.model_dump() if isinstance(action, BaseModel) else action
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for action in actions
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]
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if isinstance(actions, list)
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else actions
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}
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)
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except ValidationError as error:
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# 将结构异常转换成统一错误列表，供后续修正流程定位问题
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errors = schema_errors(error)
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# 标记本轮未通过并清空旧模拟快照，避免后续读取过期结果
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = errors
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updated_state["simulated_screen"] = None
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return updated_state
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# 获取可选的结构化需求，没有需求和规划时仅做结构校验与独立模拟执行
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requirement = state.get("requirement")
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# 如果已有规划，检查动作是否覆盖规划目标，以及是否修改了规划外的内容
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if state.get("screen_plan") is not None:
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errors = validate_plan_actions(state, batch)
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# 如果规划动作检查失败，记录错误并返回，不继续模拟执行
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if errors:
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = errors
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updated_state["simulated_screen"] = None
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return updated_state
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elif requirement is not None:
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# 此分支没有规划，只校验简单修改，新增动作需要走上面的规划分支
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if any(isinstance(action, AddAction) for action in batch.actions):
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# 为每个新增动作记录下标和范围错误，便于修正时逐项定位
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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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action_index=index,
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code="scope_error",
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message="新增动作尚未接入需求范围校验，当前仅支持独立模拟执行",
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)
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for index, action in enumerate(batch.actions)
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if isinstance(action, AddAction)
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]
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updated_state["simulated_screen"] = None
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return updated_state
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# 将需求允许的节点操作表示为三元组：作用域、目标 ID、操作名称
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allowed = {
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("nodes", target, change.operation)
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for change in requirement.changes
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if change.target_scope == "nodes"
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for target in change.target_ids
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}
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# 将需求中的画布更新和尺寸调整统一映射为 update_canvas 动作
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allowed.update(
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("canvas", "", "update_canvas")
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for change in requirement.changes
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if change.target_scope == "canvas"
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and change.operation in ("update", "resize", "update_canvas")
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)
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# 画布没有组件 ID，使用独立作用域避免与同名组件混淆
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targets = [
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("canvas", "", action.type)
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if isinstance(action, UpdateCanvasAction)
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else ("nodes", action.target_id, action.type)
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for action in batch.actions
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]
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# 去重后的实际操作集合用于检查遗漏，原 targets 列表保留动作顺序和下标
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actual = set(targets)
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# 逐项找出需求未允许的目标或操作，防止模型扩大修改范围
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errors = [
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ActionValidationError(
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action_index=index,
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code="scope_error",
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message=f"动作超出需求范围: {' / '.join(target)}",
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)
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for index, target in enumerate(targets)
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if target not in allowed
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]
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# 如果需求允许的目标操作没有对应动作，记录遗漏，避免只执行部分需求
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if missing := allowed - actual:
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errors.append(
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ActionValidationError(
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code="scope_error",
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message=f"动作遗漏了需求中的目标或操作: {sorted(missing)}",
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)
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)
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# 如果存在越界或遗漏，返回全部范围错误，并清空旧模拟结果
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if errors:
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = errors
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updated_state["simulated_screen"] = None
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return updated_state
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# 在副本上试执行，不改变用户传入的大屏，也不操作前端画布
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screen = state["input_context"].screen.model_copy(deep=True)
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# 按动作顺序逐项执行，每一步都使用上一步更新后的大屏副本
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for index, action in enumerate(batch.actions):
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try:
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# 根据动作类型调用对应处理逻辑，检查目标和参数并返回更新后的大屏
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screen = apply_action(screen, action)
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except ValueError as error:
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# 首个失败即停止，后续动作可能依赖前序结果，不返回部分成功快照
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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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action_index=index, code="execution_error", message=str(error)
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)
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]
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updated_state["simulated_screen"] = None
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return updated_state
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# 如果已有规划，还需核对整批执行后的大屏是否满足规划，不能只看单步执行成功
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if state.get("screen_plan") is not None:
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errors = validate_plan_result(state, screen)
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# 如果最终结果与规划不符，记录错误并丢弃模拟快照，交给后续修正流程
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if errors:
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = errors
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updated_state["simulated_screen"] = None
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return updated_state
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# 全部检查通过后，保存重新校验过的动作对象，统一后续读取的数据结构
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updated_state["actions"] = batch.actions
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# 标记整批动作校验通过，并清空之前可能残留的错误
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updated_state["validation_passed"] = True
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updated_state["validation_errors"] = []
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# 保存整批动作执行后的 JSON，供后续检查，不代表前端已经完成更新
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updated_state["simulated_screen"] = screen
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# 仅表示后端动作已通过校验，前端执行结果需另行反馈
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updated_state["status"] = "completed"
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# 返回动作、校验结果和完整模拟快照，供后续输出使用
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return updated_state
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if __name__ == "__main__":
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import json
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from app.processor.nodes.input_node import InputNode
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from app.schemas.actions import MoveAction, ResizeAction, UpdateAction
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# 1. 创建 state，不需要调用模型
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state = InputNode().process(
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{
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"request": {
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"prompt": "移动、调整尺寸并修改标题",
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"screen": {
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"canvas": {"width": 1920, "height": 1080},
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"nodes": [
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{
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"id": "sales-chart",
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"type": "chart",
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"layout": {"x": 100, "y": 200, "width": 400, "height": 300},
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"props": {"title": "销售额"},
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}
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],
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},
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}
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}
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)
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state["actions"] = [
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MoveAction(target_id="sales-chart", delta_x=50),
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ResizeAction(target_id="sales-chart", width=500),
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UpdateAction(
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target_id="sales-chart", section="props", path=["title"], value="月度销售额"
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),
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]
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# 2. 执行节点并打印完整 state
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node = ValidatorNode()
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final_state = node.process(state)
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print(
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json.dumps(
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final_state,
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ensure_ascii=False,
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indent=2,
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default=lambda value: value.model_dump(mode="json"),
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)
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)
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# 3. 在第二个动作制造错误，验证整批失败且不返回部分结果
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failed_state = {
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**final_state,
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"actions": [
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state["actions"][0],
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MoveAction(target_id="missing", delta_x=50),
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],
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}
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failed_result = node.process(failed_state)
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print("失败校验:", failed_result["validation_passed"])
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print("错误:", [error.model_dump() for error in failed_result["validation_errors"]])
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print("失败快照:", failed_result["simulated_screen"])
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