96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
"""输入解析与校验，可直接运行本文件修改示例进行调试"""
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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.input_context import InputContext
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class InputNode(BaseNode):
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"""纯输入节点，不依赖模型配置；校验失败保留 ValidationError"""
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name = "input_node"
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def process(self, state: QueryState) -> QueryState:
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reply = state.get("clarification_reply")
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if reply is not None:
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data = state["input_context"].model_dump(mode="json")
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turn = {
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"question": state["clarification"].question,
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"answer": reply.answer,
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}
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if reply.option_id is not None:
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option = next(
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option
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for option in state["clarification"].options
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if option.id == reply.option_id
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)
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turn["selected_option"] = option.model_dump(mode="json")
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data["clarification_history"].append(turn)
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if reply.screen is not None:
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data["screen"] = reply.screen.model_dump(mode="json")
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# 新快照不沿用可能已经失效的选中节点
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if reply.context is None:
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data["context"]["selected_node_ids"] = []
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data["context"]["screen_revision"] = None
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if reply.context is not None:
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data["context"] = reply.context.model_dump(mode="json")
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context = InputContext.model_validate(data)
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# 显式清空旧推导结果，LangGraph 的状态合并不会删除省略字段
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return {
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**state,
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"input_context": context,
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"clarification_reply": None,
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"intent_decision": None,
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"intent": None,
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"requirement": None,
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"clarification": None,
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"status": "running",
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}
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# 深拷贝隔离原始请求，后续修改上下文不会改变提交时的快照
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context = InputContext.model_validate(state["request"]).model_copy(deep=True)
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updated_state: QueryState = {**state, "input_context": context}
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return updated_state
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if __name__ == "__main__":
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import json
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# 1. 创建 state：可将 screen 替换成前端的完整大屏 JSON
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state: QueryState = {
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"request": {
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"prompt": "把销售额统计向右移动 50px",
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"screen": {
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"canvas": {
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"width": 1920,
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"height": 1080,
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"backgroundColor": "#ffffff10",
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},
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"nodes": [
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{
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"id": "sales-chart",
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"type": "bar-chart",
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"name": "销售额统计",
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"layout": {"x": 26, "y": 34.8, "width": 400, "height": 260},
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"props": {"option": {"title": {"text": "销售额统计"}}},
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}
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],
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"dataSource": [],
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},
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"context": {"selected_node_ids": ["sales-chart"]},
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"attachments": [],
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}
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}
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# 2. 执行节点
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node = InputNode()
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final_state = node.process(state)
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# 3. 打印完整状态
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printable_state = {
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**final_state,
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"input_context": final_state["input_context"].model_dump(
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mode="json", exclude_unset=True
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),
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}
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print(json.dumps(printable_state, ensure_ascii=False, indent=2))
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