97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
"""基于当前大屏、上传的数据和会话历史流式回答问题
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由问答 agent 执行：会话里有数据文件时先查询再回答，否则只调用一次模型；只把模型输出的文字推送给前端
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"""
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import json
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from langchain.agents.middleware.model_call_limit import ModelCallLimitExceededError
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from langchain_core.messages import AIMessageChunk, HumanMessage
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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.agent.qa_agent import QAContext, get_qa_agent
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from app.processor.base import BaseNode
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from app.processor.history import history_messages
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from app.processor.state import QueryState, run_data
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from app.processor.summary import data_file_summary
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# 模型在一次回答里分几次输出文字（例如查询前后各说一段）时，段落之间空一行
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PARAGRAPH_BREAK = "\n\n"
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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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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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context = state["input_context"]
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payload = context.model_dump(
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mode="json", exclude_unset=True, exclude={"history", "data_files"}
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)
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if context.data_files:
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# 只给表名、行数和列名，文件路径和列概况不进提示词，需要时由 describe_data 查看
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payload["data_files"] = [
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data_file_summary(item) for item in context.data_files
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]
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agent = get_qa_agent(with_data=bool(context.data_files))
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parser = StrOutputParser()
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parts: list[str] = []
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last_message_id = None
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try:
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async for chunk, metadata in agent.astream(
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{
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"messages": [
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# 会话历史作为此前的对话消息，本次请求是最后一条用户消息
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*history_messages(context.history),
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HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
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]
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},
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context=QAContext(data=run_data()),
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stream_mode="messages",
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):
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# 工具结果、工具调用和中间件插入的消息都不推送
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if metadata.get("langgraph_node") != "model" or not isinstance(
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chunk, AIMessageChunk
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):
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continue
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if not (delta := parser.invoke(chunk)):
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continue
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if parts and chunk.id != last_message_id:
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delta = PARAGRAPH_BREAK + delta
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last_message_id = chunk.id
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parts.append(delta)
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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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except ModelCallLimitExceededError:
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self.logger.warning("问答 agent 超出模型调用上限（MAX_QA_STEPS）")
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updated_state["status"] = "failed"
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updated_state["answer"] = (
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"查询步骤超出上限，这次没有完成，可以缩小范围或换个问法再试"
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)
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return updated_state
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# 推送了什么就保存什么，会话历史和前端看到的回答一致
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answer = "".join(parts).strip()
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# 没有有效文本时报错，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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updated_state["answer"] = answer
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updated_state["status"] = "completed"
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return updated_state
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