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@@ -3,7 +3,7 @@ from abc import ABC, abstractmethod
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from typing import List, Dict, Any, Optional
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from vanna.base import VannaBase
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# 导入配置参数
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-from app_config import REWRITE_QUESTION_ENABLED
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+from app_config import REWRITE_QUESTION_ENABLED, DISPLAY_SUMMARY_THINKING
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class BaseLLMChat(VannaBase, ABC):
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@@ -60,7 +60,7 @@ class BaseLLMChat(VannaBase, ABC):
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if initial_prompt is None:
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initial_prompt = f"You are a {self.dialect} expert. " + \
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- "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. "
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+ "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions."
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# 提取DDL内容（适配新的字典格式）
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ddl_content_list = []
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@@ -381,6 +381,99 @@ class BaseLLMChat(VannaBase, ABC):
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# 如果合并失败，返回新问题
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return new_question
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+ def generate_summary(self, question: str, df, **kwargs) -> str:
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+ """
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+ 覆盖父类的 generate_summary 方法，添加中文思考和回答指令
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+
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+ Args:
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+ question (str): 用户提出的问题
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+ df: 查询结果的 DataFrame
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+ **kwargs: 其他参数
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+
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+ Returns:
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+ str: 数据摘要
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+ """
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+ try:
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+ # 导入 pandas 用于 DataFrame 处理
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+ import pandas as pd
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+
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+ # 确保 df 是 pandas DataFrame
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+ if not isinstance(df, pd.DataFrame):
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+ print(f"[WARNING] df 不是 pandas DataFrame，类型: {type(df)}")
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+ return "无法生成摘要：数据格式不正确"
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+
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+ if df.empty:
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+ return "查询结果为空，无数据可供摘要。"
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+
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+ print(f"[DEBUG] 生成摘要 - 问题: {question}")
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+ print(f"[DEBUG] DataFrame 形状: {df.shape}")
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+
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+ # 构建包含中文指令的系统消息
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+ system_content = (
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+ f"你是一个专业的数据分析助手。用户提出了问题：'{question}'\n\n"
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+ f"以下是查询结果的 pandas DataFrame 数据：\n{df.to_markdown()}\n\n"
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+ "请用中文进行思考和分析，并用中文回答。"
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+ )
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+
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+ # 构建用户消息，强调中文思考和回答
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+ user_content = (
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+ "请基于用户提出的问题，简要总结这些数据。要求：\n"
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+ "1. 只进行简要总结，不要添加额外的解释\n"
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+ "2. 如果数据中有数字，请保留适当的精度\n"
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+ )
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+
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+ message_log = [
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+ self.system_message(system_content),
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+ self.user_message(user_content)
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+ ]
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+
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+ summary = self.submit_prompt(message_log, **kwargs)
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+
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+ # 检查是否需要隐藏 thinking 内容
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+ display_thinking = kwargs.get("display_summary_thinking", DISPLAY_SUMMARY_THINKING)
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+
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+ if not display_thinking:
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+ # 移除 <think></think> 标签及其内容
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+ original_summary = summary
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+ summary = self._remove_thinking_content(summary)
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+ print(f"[DEBUG] 隐藏thinking内容 - 原始长度: {len(original_summary)}, 处理后长度: {len(summary)}")
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+
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+ print(f"[DEBUG] 生成的摘要: {summary[:100]}...")
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+ return summary
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+
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+ except Exception as e:
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+ print(f"[ERROR] 生成摘要失败: {str(e)}")
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+ import traceback
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+ print(f"[ERROR] 详细错误信息: {traceback.format_exc()}")
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+ return f"生成摘要时出现错误：{str(e)}"
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+
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+ def _remove_thinking_content(self, text: str) -> str:
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+ """
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+ 移除文本中的 <think></think> 标签及其内容
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+
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+ Args:
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+ text (str): 包含可能的 thinking 标签的文本
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+
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+ Returns:
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+ str: 移除 thinking 内容后的文本
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+ """
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+ if not text:
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+ return text
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+
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+ import re
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+
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+ # 移除 <think>...</think> 标签及其内容（支持多行）
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+ # 使用 re.DOTALL 标志使 . 匹配包括换行符在内的任何字符
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+ cleaned_text = re.sub(r'<think>.*?</think>\s*', '', text, flags=re.DOTALL | re.IGNORECASE)
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+
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+ # 移除可能的多余空行
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+ cleaned_text = re.sub(r'\n\s*\n\s*\n', '\n\n', cleaned_text)
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+
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+ # 去除开头和结尾的空白字符
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+ cleaned_text = cleaned_text.strip()
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+
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+ return cleaned_text
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+
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@abstractmethod
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def submit_prompt(self, prompt, **kwargs) -> str:
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"""
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wangxq