删除合并脚本，添加会话转工单脚本逻辑
This commit is contained in:
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"""
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综合评判工具
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此模块结合了答案正确性评判和检索内容相关性评分功能，可以同时：
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1. 评判问题的新旧回答是否正确
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2. 比较新旧回答的差异
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3. 评估检索内容与问题的相关性
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用法示例:
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judge = CombinedJudge()
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judge.process()
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"""
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import pandas as pd
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from urllib.parse import unquote
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from rag2_0.tool.WikijsTool import WikijsTool
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from rag2_0.tool.html_to_md import convert_html_to_md
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from rag2_0.tool.ModelTool import OpenAiLLM
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from dotenv import load_dotenv
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import os
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from tqdm import tqdm
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from rag2_0.dify.dify_tool import DifyTool
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import json
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from pydantic import BaseModel, Field
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from langchain.output_parsers import PydanticOutputParser
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import concurrent.futures
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from threading import Lock
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load_dotenv()
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class ContentSource(BaseModel):
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score:int = Field(description="相关性分数")
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reason:str = Field(description="评分理由")
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class CombinedJudge:
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"""
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综合评判工具类
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结合了答案正确性评判和检索内容相关性评分功能
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"""
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def __init__(self, wiki_excel_path="/data/QueryRewrite/data/excel/部分提问_软件名称明确.xlsx",
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answer_excel_path="/data/QueryRewrite/data/excel/dify问答_对比结果.xlsx",
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output_path="/data/QueryRewrite/data/excel/dify问答__综合评判结果.xlsx",
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dify_appid="ccf92b97-2789-4a3f-90e0-135a869a37c5",
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max_workers=10):
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"""
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初始化综合评判工具
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参数:
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wiki_excel_path (str): Wiki Excel文件路径
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answer_excel_path (str): 答案对比Excel文件路径
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output_path (str): 输出Excel文件路径
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dify_appid (str): Dify应用ID
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max_workers (int): 最大工作线程数
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"""
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self.wiki_excel_path = wiki_excel_path
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self.answer_excel_path = answer_excel_path
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self.output_path = output_path
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self.dify_appid = dify_appid
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self.max_workers = max_workers
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self.content_source_parser = PydanticOutputParser(pydantic_object=ContentSource)
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self.results_lock = Lock()
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# 读取Excel文件
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if os.path.exists(wiki_excel_path):
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self.wiki_excel = pd.read_excel(self.wiki_excel_path)
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else:
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self.wiki_excel = None
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self.answer_excel = pd.read_excel(self.answer_excel_path)
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# 初始化LLM
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self.api_key = os.getenv("OPENAI_API_KEY")
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self.base_url = os.getenv("OPENAI_API_BASE")
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self.model = os.getenv("LLM_MODEL_NAME")
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if not all([self.api_key, self.base_url, self.model]):
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raise ValueError("请设置 OPENAI_API_KEY, OPENAI_API_BASE, 和 LLM_MODEL_NAME 环境变量")
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self.llm = OpenAiLLM(api_key=self.api_key, base_url=self.base_url, model=self.model)
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def find_wiki_link(self, query) -> str | None:
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"""
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根据查询（对应wiki_excel中的新提问列）找出对应的词条链接
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参数:
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query (str): 查询内容，对应wiki_excel中的新提问列
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返回:
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str: 对应的词条链接，如果没有找到则返回None
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"""
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# 确保query不为空
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if not query or pd.isna(query):
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return None
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if self.wiki_excel is None:
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return None
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# 在"新提问"列中查找匹配的行
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matched_rows = self.wiki_excel[self.wiki_excel['新提问'] == query]
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# 如果找到了匹配的行，返回对应的词条链接
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if not matched_rows.empty:
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return matched_rows.iloc[0]['对应词条链接']
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# 如果没有完全匹配，尝试部分匹配
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# 去除软件名称部分（如果有）
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query_parts = query.split(',', 1)
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if len(query_parts) > 1:
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clean_query = query_parts[1].strip()
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# 在"提问"列中查找包含清理后查询的行
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for idx, row in self.wiki_excel.iterrows():
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if pd.notna(row['提问']) and clean_query in row['提问']:
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return row['对应词条链接']
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return None
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def get_wiki_content(self, link) -> str:
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"""
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获取词条链接的内容
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参数:
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link (str): 词条链接
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返回:
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str: 链接内容，如果获取失败则返回错误信息
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"""
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try:
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if not link or pd.isna(link):
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return "链接为空或无效"
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# 移除域名部分,只保留路径
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path = link.split('/', 3)[-1]
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decoded_path = unquote(path)
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path_parts = decoded_path.split('/')
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doc_path = "/".join(path_parts[1:])
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wiki_doc = WikijsTool.get_all_doc_by_path(path=doc_path, path_is_dir=False)
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html_content = WikijsTool.query_doc_info(wiki_doc[0]["id"]).get('content')
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if not html_content:
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return "获取内容失败"
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options = {"heading_style": '', "keep_inline_images_in": ["figure", "img"], "escape_asterisks": True}
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new_content = (html_content.replace("h6>", "h7>")
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.replace("h5>", "h6>")
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.replace("h4>", "h5>")
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.replace("h3>", "h4>")
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.replace("h2>", "h3>")
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.replace("h1>", "h2>"))
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# 将HTML内容转换为Markdown
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markdown_content = convert_html_to_md(new_content, "", **options)
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markdown_content = f"# {path_parts[-1]}\n\n{markdown_content}"
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return markdown_content
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except Exception as e:
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raise RuntimeError(f"获取词条内容失败: {str(e)}") from e
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def get_wiki_title(self, link) -> str | None:
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"""
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获取词条标题
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参数:
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link (str): 词条链接
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返回:
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str: 词条标题，如果获取失败则返回None
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"""
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try:
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if not link or pd.isna(link):
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return None
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# 移除域名部分,只保留路径
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path = link.split('/', 3)[-1]
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decoded_path = unquote(path)
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path_parts = decoded_path.split('/')
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return path_parts[-1]
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except Exception as e:
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raise RuntimeError(f"获取词条内容失败: {str(e)}") from e
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def create_correctness_prompt(self, standard_answer: str, answer_to_check: str) -> str:
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"""
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创建用于评判答案正确性的prompt
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参数:
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standard_answer (str): 标准答案
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answer_to_check (str): 需要检查的答案
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返回:
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str: 格式化的prompt
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"""
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return f"""请作为一个专业的答案评判专家，评估以下回答与标准答案的匹配程度。
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标准答案：
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{standard_answer}
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待评估的回答：
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{answer_to_check}
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请仔细分析两个答案的内容，并给出你的判断。只需要回答"正确"或"错误"，不需要其他解释。
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如果待评估的回答与标准答案在核心内容和关键信息(步骤)上一致，即使表达方式不同，也应判定为"正确"。
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如果待评估的回答存在明显的错误信息或重要信息缺失，应判定为"错误"。
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请严格按以下格式输出：【正确】或【错误】："""
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def judge_answer(self, standard_answer: str, answer: str) -> bool | None:
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"""
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调用LLM判断回答是否正确
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参数:
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standard_answer (str): 标准答案（来自Wiki）
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answer (str): 需评判的回答
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返回:
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bool | None: 判断结果，True表示正确，False表示错误，None表示判断失败
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"""
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prompt = self.create_correctness_prompt(standard_answer, answer)
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try:
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response = self.llm.invoke(user_prompt=prompt, need_retry=True)
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return "正确" in response.content
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except Exception as e:
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return None
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def judge_by_standard_answer(self, standard_answer: str, old_answer: str, new_answer: str) -> str | None:
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"""
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综合判断新旧回答的正确性
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参数:
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standard_answer (str): 标准答案（来自Wiki）
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old_answer (str): 旧流程的回答
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new_answer (str): 新流程的回答
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返回:
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str | None: 包含新旧回答判断结果的字符串，None表示判断失败
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"""
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old_result = self.judge_answer(standard_answer, old_answer)
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new_result = self.judge_answer(standard_answer, new_answer)
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if old_result is None or new_result is None:
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return None
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if new_result and old_result:
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return "新旧答案均正确"
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elif new_result and not old_result:
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return "新答案正确"
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elif not new_result and old_result:
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return "旧答案正确"
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else:
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return "新旧答案均错误"
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def judge_answer_diff(self, old_answer: str, new_answer: str) -> str | None:
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"""
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判断新旧回答是否存在较大差异
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参数:
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old_answer (str): 旧流程的回答
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new_answer (str): 新流程的回答
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返回:
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str | None: 差异判断结果，None表示判断失败
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"""
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prompt = f"""请判断以下两个回答是否存在较大差异:
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旧回答: {old_answer}
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新回答: {new_answer}
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主要是主要步骤、主要信息、或者主要主体的差异
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请仅回答"存在较大差异"或"差异较小"。"""
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try:
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response = self.llm.invoke(user_prompt=prompt, need_retry=True)
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return "缺乏标准答案无法判断准确性,但答案差异较大" if "存在较大差异" in response.content else "缺乏标准答案无法判断准确性,但答案基本相同"
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except Exception as e:
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return None
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def calculate_score(self, query:str, content:str) -> int:
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"""
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使用LLM判断query与content之间的相关性分数
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参数:
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query (str): 用户问题
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content (str): 检索内容
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返回:
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int: 相关性分数，1-10分，10代表完全相关，1代表完全不相关；-1表示评分失败
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"""
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try:
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prompt = f"""你是一个专业的信息相关性评估助手。请根据以下标准对用户query和检索内容的相关性进行1-10评分（10=完全相关，1=完全不相关），并按指定格式输出JSON结果。
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【评分标准】
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10分：完全契合，主题/意图完全一致且涵盖所有关键信息
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8-9分：高度相关，核心要素匹配但存在少量信息缺失
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6-7分：部分相关，涉及相同主题但存在重要信息缺失
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4-5分：弱相关，仅次要信息点匹配
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1-3分：完全不相关或信息冲突
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【评估维度】
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1. 主题一致性：核心主题/意图的匹配程度
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2. 内容覆盖度：是否涵盖query的关键要素
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3. 信息准确性：是否存在矛盾/错误信息
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4. 细节丰富度：是否提供query要求的详细信息
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【输出格式】
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{{
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"score": 评分,
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"reason": "简明扼要的评分理由（中文）"
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}}
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【示例】
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query: "新冠疫苗的常见副作用"
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内容: "辉瑞疫苗常见反应包括注射部位疼痛（84.1%）、疲劳（62.9%）"
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输出: {{"score":8,"reason":"主题完全匹配，涵盖主要副作用但未提及发热等常见反应"}}
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现在评估：
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query: "{query}"
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content: "{content}"
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"""
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response = self.llm.invoke(user_prompt=prompt, need_retry=True)
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# 解析JSON响应
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try:
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parsed_output = self.content_source_parser.parse(response.content)
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return parsed_output.score
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except Exception as e:
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return -1
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except Exception as e:
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return -1
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def get_retrieve_info(self, query:str, outputs:dict) -> tuple:
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"""
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获取检索信息并计算分数
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参数:
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query (str): 用户问题
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outputs (dict): 检索输出结果
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返回:
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tuple: (检索内容列表, 最高分, 最低分, 平均分)
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"""
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max_score = 0
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min_score = 10
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total_score = 0
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valid_scores = 0
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retrieve_content = []
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for result in outputs["result"]:
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content = result["content"].strip()
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score = self.calculate_score(query=query, content=content)
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if score != -1:
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max_score = max(max_score, score)
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min_score = min(min_score, score)
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total_score += score
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valid_scores += 1
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content_title = content.split("\n")[0]
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if content_title:
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retrieve_content.append(content_title + f"--得分({score}分)")
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avg_score = total_score / valid_scores if valid_scores > 0 else 0
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return retrieve_content, max_score, min_score, avg_score
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def process_single_question(self, row):
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"""
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处理单个问题的评判
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参数:
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row: DataFrame中的一行数据
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返回:
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dict: 包含处理结果的字典
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"""
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query = row["问题"]
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old_answer = row["旧流程答案"]
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new_answer = row["新流程答案"]
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# 获取词条链接和标准答案
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wiki_url = self.find_wiki_link(query)
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standard_answer = ""
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answer_title = ""
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try:
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if wiki_url and not pd.isna(wiki_url):
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standard_answer = self.get_wiki_content(wiki_url)
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answer_title = self.get_wiki_title(wiki_url)
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except Exception as e:
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print(f"处理问题 '{query}' 获取标准答案时发生错误: {str(e)}")
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# 判断答案正确性
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if standard_answer:
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judge_result = self.judge_by_standard_answer(standard_answer, old_answer, new_answer)
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else:
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judge_result = self.judge_answer_diff(old_answer, new_answer)
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if judge_result is None:
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judge_result = ""
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# 获取检索内容评分
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retrieve_content = []
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max_score = 0
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min_score = 0
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avg_score = 0
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rewrite_query = ""
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try:
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message_info = DifyTool.get_message_debug_info(appid=self.dify_appid, query=query)
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for workflow_node in message_info["workflow_node_executions_info"]:
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if workflow_node["title"] == "知识检索结果后处理":
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outputs = json.loads(workflow_node["outputs"])
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retrieve_content, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
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elif workflow_node["title"] == "问题优化结果解析":
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outputs = json.loads(workflow_node["outputs"])
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rewrite_query = outputs["optimize_query"]
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except Exception as e:
|
||||
print(f"处理问题 '{query}' 获取检索内容时发生错误: {str(e)}")
|
||||
|
||||
# 返回结果
|
||||
return {
|
||||
"问题": query,
|
||||
"问题改写": rewrite_query,
|
||||
"旧流程答案": old_answer,
|
||||
"新流程答案": new_answer,
|
||||
"回答判断": judge_result,
|
||||
"答案词条": answer_title if answer_title else "",
|
||||
"检索得到词条": "\n".join(retrieve_content) if retrieve_content else "未检索知识库",
|
||||
}
|
||||
|
||||
def process(self):
|
||||
"""
|
||||
多线程处理所有问题并进行综合评判
|
||||
|
||||
读取Excel文件中的问题和答案，使用多线程进行评判，并将结果保存到输出Excel文件
|
||||
"""
|
||||
# 创建结果列表
|
||||
results = []
|
||||
|
||||
# 创建进度条
|
||||
with tqdm(total=len(self.answer_excel), desc="处理问题中") as pbar:
|
||||
# 使用线程池执行任务
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
|
||||
# 提交所有任务
|
||||
future_to_row = {executor.submit(self.process_single_question, row): idx
|
||||
for idx, row in self.answer_excel.iterrows()}
|
||||
|
||||
# 处理完成的任务
|
||||
for future in concurrent.futures.as_completed(future_to_row):
|
||||
idx = future_to_row[future]
|
||||
try:
|
||||
result = future.result()
|
||||
with self.results_lock:
|
||||
results.append(result)
|
||||
except Exception as e:
|
||||
print(f"处理第 {idx} 行时发生错误: {str(e)}")
|
||||
finally:
|
||||
pbar.update(1)
|
||||
|
||||
# 将结果转换为DataFrame并保存
|
||||
results_df = pd.DataFrame(results)
|
||||
results_df.to_excel(self.output_path, index=False)
|
||||
print(f"处理完成，共处理 {len(results)} 条记录，结果已保存至 {self.output_path}")
|
||||
|
||||
# 测试函数
|
||||
if __name__ == "__main__":
|
||||
# 创建综合评判工具实例
|
||||
judge = CombinedJudge(max_workers=30)
|
||||
# 执行处理
|
||||
judge.process()
|
||||
Reference in New Issue
Block a user