139 lines
6.1 KiB
Python
139 lines
6.1 KiB
Python
"""沿用意图判断，提取具体需求"""
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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.outcome_policy import decide_outcome
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from app.processor.state import QueryState
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from app.prompt.requirement import REQUIREMENT_SYSTEM_PROMPT, build_requirement_input
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from app.schemas.requirement import Requirement
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class RequirementNode(BaseNode):
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name = "requirement_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, "未能可靠理解你的需求，请换个说法再试"
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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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# 获取输入节点已校验的上下文，包含用户需求、当前大屏和澄清记录
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context = state["input_context"]
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# 获取意图节点经过置信度判断的最终意图，本节点沿用该结果
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intent = state["intent"]
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llm_client = get_llm(Requirement)
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# 异步调用模型，结合当前上下文提取目标、变更、约束及需要澄清的信息
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llm_res = await llm_client.ainvoke(
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[
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# 系统消息提供稳定的需求提取规则和输出边界
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SystemMessage(content=REQUIREMENT_SYSTEM_PROMPT),
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# 用户消息承载本次需求、大屏、澄清记录和已确认的意图等具体资料
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HumanMessage(
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content=build_requirement_input(
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context, intent, accepted_offer=state.get("accepted_offer")
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)
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),
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]
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)
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# 用上游意图覆盖模型返回的 intent，避免需求提取阶段重新分类
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# 覆盖后重新校验字段一致性，例如 unclear 必须要求澄清，qa 不能包含变更
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requirement = Requirement.model_validate(
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{**llm_res.model_dump(), "intent": intent}
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)
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# 收集当前大屏中真实存在的节点 ID，用于校验模型提取的变更目标
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node_ids = {node.id for node in context.screen.nodes}
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# 逐项检查需求变更，防止后续节点基于不存在的组件生成方案或动作
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for change in requirement.changes:
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# 用集合差集找出不在当前大屏中的目标 ID，非空时保存到 unknown_ids
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if unknown_ids := set(change.target_ids) - node_ids:
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# 如果存在无效目标，立即抛出异常，并排序输出 ID 以便定位问题
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raise ValueError(f"需求引用了不存在的节点: {sorted(unknown_ids)}")
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# 模型只负责理解需求，本轮执行、澄清还是直接回复统一由落地策略根据真实组件决定
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decision = decide_outcome(requirement, context.screen)
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# 保存落地后的需求，唯一候选已改写为明确目标，供后续澄清或范围判断使用
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updated_state["requirement"] = decision.requirement
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updated_state["requirement_outcome"] = decision.outcome
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updated_state["notice"] = decision.notice
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if decision.clarification is not None:
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# 选项来自真实组件，无需再由澄清节点调用模型
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updated_state["clarification"] = decision.clarification
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updated_state["status"] = "waiting_for_input"
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# 返回更新后的状态，由图继续执行后续节点
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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.clients.decision import close_decision_engine
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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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async def main():
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# 1. 修改这里的请求，测试具体需求提取
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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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"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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}
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}
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# 2. 依次执行输入、意图判断和需求理解
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try:
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# 先将原始请求校验为统一的输入上下文
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state = InputNode().process(state)
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# 调用 Jev 判断意图，为需求提取提供已确认的分类结果
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state = await IntentNode().process(state)
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# 基于上下文和意图调用模型，提取结构化需求
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node = RequirementNode()
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final_state = await node.process(state)
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finally:
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# 无论处理成功还是抛出异常，都关闭示例使用的 Jev 连接
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await close_decision_engine()
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# 3. 打印完整 state
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# 将状态中的 Pydantic 模型转成字典，其他值保持原样，以便输出 JSON
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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", exclude_unset=True)
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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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# 启动事件循环，执行上面的异步调试流程
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asyncio.run(main())
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