def is_string_match(answer, response, case_sensitive=False): """ 判断两个字符串是否匹配,支持模糊子串匹配 参数: answer: 预期字符串 response: 待检查字符串 case_sensitive: 是否区分大小写(默认不区分) 返回: bool: 是否匹配 """ if not case_sensitive: answer = answer.lower() response = response.lower() # 1. 完全一致 if answer == response: return True # 2. answer是response的子串 if answer in response: return True # 3. 模糊匹配:允许少量字符不匹配(简单实现) # 这里使用简单的子序列检查,可以根据需求替换为更复杂的模糊匹配算法 len_answer = len(answer) len_response = len(response) # answer比response长,肯定不是子串 if len_answer > len_response: return False # 简单子序列检查(允许中间有少量不匹配字符) i = j = 0 mismatch_count = 0 max_mismatch = max(1, len_answer // 4) # 允许25%的字符不匹配 while i < len_answer and j < len_response: if answer[i] == response[j]: i += 1 j += 1 else: j += 1 mismatch_count += 1 if mismatch_count > max_mismatch: return False return i == len_answer # 更强大的模糊匹配版本(使用difflib) from difflib import SequenceMatcher def fuzzy_string_match(answer, response, threshold=0.8, case_sensitive=False): """ 使用difflib的模糊匹配 参数: threshold: 相似度阈值(0-1) """ if not case_sensitive: answer = answer.lower() response = response.lower() # 完全匹配 if answer == response: return True # 子串匹配 if answer in response: return True # 模糊匹配 similarity = SequenceMatcher(None, answer, response).ratio() return similarity >= threshold # 在问答系统中的应用 # qa_pairs = [ # {"answer": "人工智能", "response": "AI(人工智能)是未来趋势"}, # {"answer": "Python", "response": "我们使用python编程"}, # {"answer": "机器学习", "response": "深度学习是机器学习的分支"}, # {"answer": "42", "response": "答案是42"}, # {"answer": "hello", "response": "hi there"} # ] # print("\n问答系统匹配结果:") # for pair in qa_pairs: # matched = fuzzy_string_match(pair["answer"], pair["response"]) # print(f"Answer: '{pair['answer']}' | Response: '{pair['response']}' → " # f"{'匹配' if matched else '不匹配'}")