155 lines
5.9 KiB
Python
155 lines
5.9 KiB
Python
"""基于当前大屏和澄清历史回答问题"""
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import json
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.output_parsers import StrOutputParser
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from langgraph.config import get_stream_writer
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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.qa import OFFER_EXTRACT_PROMPT, QA_SYSTEM_PROMPT
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from app.schemas.offer import ExtractedOffer, InformReply
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class QANode(BaseNode):
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name = "qa_node"
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async 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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try:
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# 图执行上下文中通过 custom 流逐段推送回答，独立调用节点时没有写入器
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writer = get_stream_writer()
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except RuntimeError:
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writer = None
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def emit(delta: str) -> None:
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if writer is not None:
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writer(
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{
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"type": "answer_delta",
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"message_id": state.get("message_id"),
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"delta": delta,
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}
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)
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content = state["input_context"].model_dump_json(exclude_unset=True)
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offer = None
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# 落地策略判定请求无法执行时，附上事实说明，由本节点如实告知用户
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if notice := state.get("notice"):
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content += f"\n\n系统说明：{notice}"
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# 回复中的替代修改需要结构化保存，下一轮用户同意时直接执行，因此不流式输出
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reply = await get_llm(InformReply).ainvoke(
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[SystemMessage(content=QA_SYSTEM_PROMPT), HumanMessage(content=content)]
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)
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answer, offer = reply.answer.strip(), reply.offer
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emit(answer)
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else:
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answer = await self._stream(content, emit)
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# 如果没有有效文本，抛出异常，避免将空回答标记为处理完成
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if not answer:
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raise ValueError("问答模型未返回文本回答")
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# 回答已流式输出，再提取其中等待用户确认的修改，下一轮同意时直接执行
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offer = await self._extract_offer(state["input_context"].prompt, answer)
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# 保存最终回答，供后续输出给用户
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updated_state["answer"] = answer
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# 保存等待用户确认的提议，没有提议时清空
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updated_state["offer"] = offer
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# 回答生成完成，问答分支在此结束
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updated_state["status"] = "completed"
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# 返回包含回答和完成状态的更新结果
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return updated_state
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async def _extract_offer(self, prompt: str, answer: str) -> str | None:
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try:
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result = await get_llm(ExtractedOffer).ainvoke(
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[
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SystemMessage(content=OFFER_EXTRACT_PROMPT),
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HumanMessage(
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content=json.dumps(
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{"user": prompt, "assistant": answer}, ensure_ascii=False
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)
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),
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]
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)
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except Exception as error:
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# 回答已交付，提取失败只影响下一轮能否直接执行，不让本轮失败
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self.logger.warning(f"提取回答中的提议失败: {error!r}")
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return None
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return result.offer
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@staticmethod
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async def _stream(content: str, emit) -> str:
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# 获取普通对话模型，问答只需文本回答，不绑定结构化输出协议
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llm_client = get_llm()
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parser = StrOutputParser()
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parts: list[str] = []
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# 异步流式调用模型，结合用户问题、当前大屏和澄清记录生成回答
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async for chunk in llm_client.astream(
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[
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# 系统消息提供稳定的问答规则和回答边界
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SystemMessage(content=QA_SYSTEM_PROMPT),
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# 输入上下文序列化为 JSON，只保留显式设置的字段
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HumanMessage(content=content),
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]
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):
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# 从每个片段提取文本，兼容文本内容块，工具调用等无文本片段会得到空串
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delta = parser.invoke(chunk)
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if delta:
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parts.append(delta)
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emit(delta)
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# 拼接完整回答，并去除首尾的空白
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return "".join(parts).strip()
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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.processor.nodes.input_node import InputNode
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async def main():
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# 直接测试问答，仅需 LLM 配置
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state: QueryState = {
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"request": {
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"prompt": "销售额统计的展示可以怎么优化？先给建议，不要修改",
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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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},
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"context": {"selected_node_ids": ["sales-chart"]},
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}
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}
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state = InputNode().process(state)
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node = QANode()
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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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