123 lines
5.2 KiB
Python
123 lines
5.2 KiB
Python
"""整理澄清问题，等待用户补充"""
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import json
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from langchain_core.exceptions import OutputParserException
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from langchain_core.messages import HumanMessage, SystemMessage
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from pydantic import ValidationError
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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.state import QueryState
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from app.prompt.clarify import CLARIFY_SYSTEM_PROMPT
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from app.schemas.clarification import Clarification
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class ClarifyNode(BaseNode):
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name = "clarify_node"
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async def process(self, state: QueryState) -> QueryState:
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# 浅拷贝状态，保存澄清问题和等待状态时不直接修改传入 state 的顶层字段
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updated_state: QueryState = state.copy()
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# 获取上游确认的意图，区分意图不明确和具体需求缺少信息两种澄清场景
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intent = state["intent"]
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# 整理意图和输入上下文，让模型结合用户原话、大屏及已有澄清记录生成问题
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payload = {
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"intent": intent,
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"input_context": state["input_context"].model_dump(mode="json"),
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}
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# 如果意图已明确，补充需求节点的结果，围绕尚缺的信息提问
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if intent != "unclear":
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# 以需求节点的问题为依据补齐选项，不重新判断意图
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payload["requirement"] = state["requirement"].model_dump(mode="json")
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llm_client = get_llm(Clarification)
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# 系统消息提供稳定的澄清规则，用户消息承载本次需要分析的具体资料
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messages = [
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SystemMessage(content=CLARIFY_SYSTEM_PROMPT),
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HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
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]
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# 最多调用两次，仅在输出结构或解析失败时重试一次，其他异常继续向上抛出
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for attempt in range(2):
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try:
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# 异步生成澄清问题和候选项，成功后立即退出重试循环
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clarification = await llm_client.ainvoke(messages)
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break
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except (ValidationError, OutputParserException):
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# 如果首次生成格式不合法，追加明确的格式要求，再调用一次模型
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if attempt == 0:
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# 要求重新生成完整问题，避免截断选项改变原意
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messages.append(
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HumanMessage(
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content=(
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"上次输出未通过 Clarification 格式校验，请重新生成完整结果："
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"question 为非空问题，options 为空或含 2–3 项，"
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"每项包含唯一 id、非空 label 和 description，最多一个 recommended=true"
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)
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)
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)
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# 进入下一次尝试，使用包含格式纠正要求的消息列表重新生成
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continue
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# 意图不明确时不读取需求，避免使用尚未生成或不适用的需求结果
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requirement = state.get("requirement") if intent != "unclear" else None
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# 优先使用需求节点已有的澄清问题，没有可用问题时使用通用询问
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question = (
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requirement.clarification_question if requirement else None
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) or "你希望我对当前大屏做什么？可以描述想修改的内容，或想了解的问题"
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# 只构建问题，不提供候选项，让用户自由补充，避免继续依赖模型格式修正
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clarification = Clarification(question=question)
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# 保存模型生成或兜底构建的澄清内容，供后续暂停流程展示给用户
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updated_state["clarification"] = clarification
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# 在进入 interrupt 前保存等待状态，暂停中的节点尚不会返回更新
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updated_state["status"] = "waiting_for_input"
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# 返回澄清内容和等待状态，实际暂停由后续调用 interrupt 的节点完成
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return updated_state
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if __name__ == "__main__":
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import asyncio
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import json
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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.observability.tracing import ainvoke_screen, flush_tracing
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from app.processor.graph import query_app
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async def main():
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# 修改请求，通过完整流程测试澄清分支
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state: QueryState = {
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"request": {
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"prompt": "这个图不太对",
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"screen": {"canvas": {"width": 1920, "height": 1080}, "nodes": []},
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}
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}
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try:
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final_state = await ainvoke_screen(
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query_app, state, thread_id="clarify-test"
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)
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finally:
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await close_decision_engine()
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flush_tracing()
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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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default=str,
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)
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)
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asyncio.run(main())
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