75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
"""NL2SQL 结果摘要和基础图表配置。"""
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from __future__ import annotations
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import json
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from typing import Any
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def build_chart_config(columns: list[str], rows: list[dict[str, Any]]) -> dict[str, Any] | None:
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"""识别简单的分类数值结果，返回安全的柱状图配置。"""
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if not rows or len(columns) < 2:
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return None
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numeric_column = next(
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(
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column
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for column in columns
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if all(isinstance(row.get(column), (int, float)) and not isinstance(row.get(column), bool) for row in rows)
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),
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None,
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)
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category_column = next((column for column in columns if column != numeric_column), None)
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if numeric_column is None or category_column is None:
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return None
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return {"type": "bar", "category": category_column, "value": numeric_column}
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async def summarize_result(
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question: str,
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columns: list[str],
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rows: list[dict[str, Any]],
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*,
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llm_client,
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max_prompt_rows: int = 20,
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) -> str:
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"""兼容旧调用方：返回面向用户的自然语言答案。"""
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return await render_answer(
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question,
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columns,
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rows,
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llm_client=llm_client,
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max_prompt_rows=max_prompt_rows,
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)
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async def render_answer(
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question: str,
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columns: list[str],
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rows: list[dict[str, Any]],
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*,
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llm_client,
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max_prompt_rows: int = 20,
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) -> str:
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"""将受控查询结果渲染为用户回答，不输出原始 JSON 或无关字段。"""
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fallback = f"查询完成，共返回 {len(rows)} 条记录。"
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prompt = (
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"请根据用户问题生成简洁、准确的中文业务回答。\n"
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"只使用查询结果中的必要字段回答问题，不要输出 JSON、SQL、Markdown 表格或内部字段名。\n"
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"如果有多条记录，优先做合计、数量或关键项概括；只有用户明确需要明细时才列出明细。\n"
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"不得补充查询结果中没有的数据，不要解释你的处理过程。\n"
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f"问题：{question}\n"
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f"列名：{json.dumps(columns, ensure_ascii=False)}\n"
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f"结果：{json.dumps(rows[:max_prompt_rows], ensure_ascii=False, default=str)}"
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)
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try:
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answer = await llm_client.chat(
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[
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{"role": "system", "content": "你是基金平台的数据回答助手。不要输出 JSON。"},
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{"role": "user", "content": prompt},
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],
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temperature=0,
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max_tokens=256,
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)
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except Exception: # noqa: BLE001 摘要失败回退固定文本
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return fallback
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return answer.strip() or fallback
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