143 lines
5.8 KiB
Python
143 lines
5.8 KiB
Python
"""判断需求涉及的范围、规划和资源需求"""
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import json
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from langchain_core.messages import HumanMessage, SystemMessage
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from app.clients.llm import get_llm
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from app.processor.base import BaseNode
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from app.processor.nodes.action_node import action_block_reason
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from app.processor.state import QueryState
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from app.prompt.scope import SCOPE_SYSTEM_PROMPT
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from app.schemas.change_scope import ChangeScope
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class ScopeNode(BaseNode):
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name = "scope_node"
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async def process(self, state: QueryState) -> QueryState:
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try:
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return await self._generate(state)
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except ValueError as error:
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# 结构化输出解析失败或引用校验不通过（含 ValidationError、OutputParserException）
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return self.fail_on_invalid_output(
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state, error, "未能确定需要修改的范围，请描述得更具体一些"
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)
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async def _generate(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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# 获取需求节点提取的结构化需求，作为范围判断的依据
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requirement = state["requirement"]
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# 获取当前大屏和用户输入，帮助模型结合已有组件判断影响范围
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context = state["input_context"]
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llm_client = get_llm(ChangeScope)
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# 异步调用模型，判断涉及哪些组件、是否需要规划以及需要补充哪些资源
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scope = await llm_client.ainvoke(
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[
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# 系统消息提供稳定的范围、规划和资源判断规则
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SystemMessage(content=SCOPE_SYSTEM_PROMPT),
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# 用户消息承载本次结构化需求和输入上下文，序列化为 JSON 供模型读取
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HumanMessage(
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content=json.dumps(
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{
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"requirement": requirement.model_dump(mode="json"),
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"input_context": context.model_dump(
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mode="json", exclude_unset=True
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),
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},
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ensure_ascii=False,
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)
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),
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]
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)
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# 收集当前大屏中真实存在的节点 ID，用于检查模型是否引用了不存在的组件
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existing_ids = {node.id for node in context.screen.nodes}
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# 如果范围中的目标 ID 不在当前大屏中，则抛出异常并列出无效 ID
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if unknown_ids := set(scope.target_ids) - existing_ids:
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raise ValueError(f"范围引用了不存在的节点: {sorted(unknown_ids)}")
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# 汇总需求中明确指定的目标 ID，使用集合去除多项变更之间重复引用的节点
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required_ids = {
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node_id for change in requirement.changes for node_id in change.target_ids
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}
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# 如果需求指定的节点未被纳入范围，则抛出异常，避免后续处理遗漏用户要求
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if missing_ids := required_ids - set(scope.target_ids):
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raise ValueError(f"范围遗漏了需求中的节点: {sorted(missing_ids)}")
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# 保存校验后的范围，下面的动作能力检查也需要读取该结果
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updated_state["change_scope"] = scope
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# 需要规划时先交给后续规划流程，不在此处判断能否直接生成动作
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# 无需规划时检查资源和操作能力，无法执行则记录阻塞原因，通过则返回 None
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reason = None if scope.needs_planning else action_block_reason(updated_state)
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# 写入本次检查结果，没有阻塞原因时同时清空旧的失败说明
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updated_state["failure_reason"] = reason
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# 有阻塞原因时标记失败，否则保持运行状态，继续后续处理
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updated_state["status"] = "failed" if reason else "running"
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# 返回包含范围、失败原因和处理状态的更新结果
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return updated_state
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if __name__ == "__main__":
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import asyncio
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from pydantic import BaseModel
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from app.processor.nodes.input_node import InputNode
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from app.schemas.requirement import Requirement
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async def main():
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# 直接提供已理解的需求，单独测试范围判断，仅需 LLM 配置
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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，其他不变",
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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": "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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}
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],
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},
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}
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}
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state = InputNode().process(state)
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state["requirement"] = Requirement(
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intent="modify_screen",
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goal="销售额统计向右移动 50px",
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changes=[
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{
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"target_scope": "nodes",
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"target_ids": ["sales-chart"],
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"operation": "move",
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"parameters": {"delta_x": 50},
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}
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],
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needs_clarification=False,
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constraints=["其他不变"],
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)
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node = ScopeNode()
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final_state = await node.process(state)
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print(
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json.dumps(
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{
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key: value.model_dump(mode="json")
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if isinstance(value, BaseModel)
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else value
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for key, value in final_state.items()
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},
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ensure_ascii=False,
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indent=2,
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)
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)
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asyncio.run(main())
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