# # Copyright 2024 The InfiniFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import logging import json import re from rag.utils.doc_store_conn import MatchTextExpr from rag.nlp import rag_tokenizer, term_weight, synonym class FulltextQueryer: def __init__(self): self.tw = term_weight.Dealer() self.syn = synonym.Dealer() self.query_fields = [ "title_tks^10", "title_sm_tks^5", "important_kwd^30", "important_tks^20", "question_tks^20", "content_ltks^2", "content_sm_ltks", ] @staticmethod def subSpecialChar(line): return re.sub(r"([:\{\}/\[\]\-\*\"\(\)\|\+~\^])", r"\\\1", line).strip() @staticmethod def isChinese(line): arr = re.split(r"[ \t]+", line) if len(arr) <= 3: return True e = 0 for t in arr: if not re.match(r"[a-zA-Z]+$", t): e += 1 return e * 1.0 / len(arr) >= 0.7 @staticmethod def rmWWW(txt): """ 移除文本中的WWW(WHAT、WHO、WHERE等疑问词)。 本函数通过一系列正则表达式模式来识别并替换文本中的疑问词，以简化文本或为后续处理做准备。 参数: - txt: 待处理的文本字符串。 返回: - 处理后的文本字符串，如果所有疑问词都被移除且文本为空，则返回原始文本。 """ patts = [ ( r"是*(什么样的|哪家|一下|那家|请问|啥样|咋样了|什么时候|何时|何地|何人|是否|是不是|多少|哪里|怎么|哪儿|怎么样|如何|哪些|是啥|啥是|啊|吗|呢|吧|咋|什么|有没有|呀|谁|哪位|哪个)是*", "", ), (r"(^| )(what|who|how|which|where|why)('re|'s)? ", " "), ( r"(^| )('s|'re|is|are|were|was|do|does|did|don't|doesn't|didn't|has|have|be|there|you|me|your|my|mine|just|please|may|i|should|would|wouldn't|will|won't|done|go|for|with|so|the|a|an|by|i'm|it's|he's|she's|they|they're|you're|as|by|on|in|at|up|out|down|of|to|or|and|if) ", " ", ), ] otxt = txt for r, p in patts: txt = re.sub(r, p, txt, flags=re.IGNORECASE) if not txt: txt = otxt return txt @staticmethod def add_space_between_eng_zh(txt): """ 在英文和中文之间添加空格。 该函数通过正则表达式匹配文本中英文和中文相邻的情况，并在它们之间插入空格。 这样做可以改善文本的可读性，特别是在混合使用英文和中文时。 参数: txt (str): 需要处理的文本字符串。 返回: str: 处理后的文本字符串，其中英文和中文之间添加了空格。 """ # (ENG/ENG+NUM) + ZH txt = re.sub(r"([A-Za-z]+[0-9]+)([\u4e00-\u9fa5]+)", r"\1 \2", txt) # ENG + ZH txt = re.sub(r"([A-Za-z])([\u4e00-\u9fa5]+)", r"\1 \2", txt) # ZH + (ENG/ENG+NUM) txt = re.sub(r"([\u4e00-\u9fa5]+)([A-Za-z]+[0-9]+)", r"\1 \2", txt) txt = re.sub(r"([\u4e00-\u9fa5]+)([A-Za-z])", r"\1 \2", txt) return txt def question(self, txt, tbl="qa", min_match: float = 0.6): """ 根据输入的文本生成查询表达式，用于在数据库中匹配相关问题。 参数: - txt (str): 输入的文本。 - tbl (str): 数据表名，默认为"qa"。 - min_match (float): 最小匹配度，默认为0.6。 返回: - MatchTextExpr: 生成的查询表达式对象。 - keywords (list): 提取的关键词列表。 """ txt = FulltextQueryer.add_space_between_eng_zh(txt) # 在英文和中文之间添加空格 # 使用正则表达式替换特殊字符为单个空格，并将文本转换为简体中文和小写 txt = re.sub( r"[ :|\r\n\t,，。？?/`!！&^%%()\[\]{}<>]+", " ", rag_tokenizer.tradi2simp(rag_tokenizer.strQ2B(txt.lower())), ).strip() otxt = txt txt = FulltextQueryer.rmWWW(txt) # 如果文本不是中文，则进行英文处理 if not self.isChinese(txt): txt = FulltextQueryer.rmWWW(txt) tks = rag_tokenizer.tokenize(txt).split() keywords = [t for t in tks if t] tks_w = self.tw.weights(tks, preprocess=False) tks_w = [(re.sub(r"[ \\\"'^]", "", tk), w) for tk, w in tks_w] tks_w = [(re.sub(r"^[a-z0-9]$", "", tk), w) for tk, w in tks_w if tk] tks_w = [(re.sub(r"^[\+-]", "", tk), w) for tk, w in tks_w if tk] tks_w = [(tk.strip(), w) for tk, w in tks_w if tk.strip()] syns = [] for tk, w in tks_w[:256]: syn = self.syn.lookup(tk) syn = rag_tokenizer.tokenize(" ".join(syn)).split() keywords.extend(syn) syn = ['"{}"^{:.4f}'.format(s, w / 4.0) for s in syn if s.strip()] syns.append(" ".join(syn)) q = ["({}^{:.4f}".format(tk, w) + " {})".format(syn) for (tk, w), syn in zip(tks_w, syns) if tk and not re.match(r"[.^+\(\)-]", tk)] for i in range(1, len(tks_w)): left, right = tks_w[i - 1][0].strip(), tks_w[i][0].strip() if not left or not right: continue q.append( '"%s %s"^%.4f' % ( tks_w[i - 1][0], tks_w[i][0], max(tks_w[i - 1][1], tks_w[i][1]) * 2, ) ) if not q: q.append(txt) query = " ".join(q) return MatchTextExpr(self.query_fields, query, 100), keywords def need_fine_grained_tokenize(tk): """ 判断是否需要对词进行细粒度分词。 参数: - tk (str): 待判断的词。 返回: - bool: 是否需要进行细粒度分词。 """ # 长度小于3的词不处理 if len(tk) < 3: return False # 匹配特定模式的词不处理（如数字、字母、符号组合） if re.match(r"[0-9a-z\.\+#_\*-]+$", tk): return False return True txt = FulltextQueryer.rmWWW(txt) qs, keywords = [], [] # 遍历文本分割后的前256个片段（防止处理过长文本） for tt in self.tw.split(txt)[:256]: # 注：这个split似乎是对英文设计，中文不起作用 if not tt: continue # 将当前片段加入关键词列表 keywords.append(tt) # 获取当前片段的权重 twts = self.tw.weights([tt]) # 查找同义词 syns = self.syn.lookup(tt) # 如果有同义词且关键词数量未超过32，将同义词加入关键词列表 if syns and len(keywords) < 32: keywords.extend(syns) # 调试日志：输出权重信息 logging.debug(json.dumps(twts, ensure_ascii=False)) # 初始化查询条件列表 tms = [] # 按权重降序排序处理每个token for tk, w in sorted(twts, key=lambda x: x[1] * -1): # 如果需要细粒度分词，则进行分词处理 sm = rag_tokenizer.fine_grained_tokenize(tk).split() if need_fine_grained_tokenize(tk) else [] # 对每个分词结果进行清洗： # 1. 去除标点符号和特殊字符 # 2. 使用subSpecialChar进一步处理 # 3. 过滤掉长度<=1的词 sm = [ re.sub( r"[ ,\./;'\[\]\\`~!@#$%\^&\*\(\)=\+_<>\?:\"\{\}\|，。；‘’【】、！￥……（）——《》？：“”-]+", "", m, ) for m in sm ] sm = [FulltextQueryer.subSpecialChar(m) for m in sm if len(m) > 1] sm = [m for m in sm if len(m) > 1] # 如果关键词数量未达上限，添加处理后的token和分词结果 if len(keywords) < 32: keywords.append(re.sub(r"[ \\\"']+", "", tk)) # 去除转义字符 keywords.extend(sm) # 添加分词结果 # 获取当前token的同义词并进行处理 tk_syns = self.syn.lookup(tk) tk_syns = [FulltextQueryer.subSpecialChar(s) for s in tk_syns] # 添加有效同义词到关键词列表 if len(keywords) < 32: keywords.extend([s for s in tk_syns if s]) # 对同义词进行分词处理，并为包含空格的同义词添加引号 tk_syns = [rag_tokenizer.fine_grained_tokenize(s) for s in tk_syns if s] tk_syns = [f'"{s}"' if s.find(" ") > 0 else s for s in tk_syns] # 关键词数量达到上限则停止处理 if len(keywords) >= 32: break # 处理当前token用于构建查询条件： # 1. 特殊字符处理 # 2. 为包含空格的token添加引号 # 3. 如果有同义词，构建OR条件并降低权重 # 4. 如果有分词结果，添加OR条件 tk = FulltextQueryer.subSpecialChar(tk) if tk.find(" ") > 0: tk = '"%s"' % tk if tk_syns: tk = f"({tk} OR (%s)^0.2)" % " ".join(tk_syns) if sm: tk = f'{tk} OR "%s" OR ("%s"~2)^0.5' % (" ".join(sm), " ".join(sm)) if tk.strip(): tms.append((tk, w)) # 将处理后的查询条件按权重组合成字符串 tms = " ".join([f"({t})^{w}" for t, w in tms]) # 如果有多个权重项，添加短语搜索条件（提高相邻词匹配的权重） if len(twts) > 1: tms += ' ("%s"~2)^1.5' % rag_tokenizer.tokenize(tt) # 处理同义词的查询条件 syns = " OR ".join(['"%s"' % rag_tokenizer.tokenize(FulltextQueryer.subSpecialChar(s)) for s in syns]) # 组合主查询条件和同义词条件 if syns and tms: tms = f"({tms})^5 OR ({syns})^0.7" # 将最终查询条件加入列表 qs.append(tms) # 处理所有查询条件 if qs: # 组合所有查询条件为OR关系 query = " OR ".join([f"({t})" for t in qs if t]) # 如果查询条件为空，使用原始文本 if not query: query = otxt # 返回匹配文本表达式和关键词 return MatchTextExpr(self.query_fields, query, 100, {"minimum_should_match": min_match}), keywords # 如果没有生成查询条件，只返回关键词 return None, keywords def hybrid_similarity(self, avec, bvecs, atks, btkss, tkweight=0.3, vtweight=0.7): from sklearn.metrics.pairwise import cosine_similarity as CosineSimilarity import numpy as np sims = CosineSimilarity([avec], bvecs) tksim = self.token_similarity(atks, btkss) if np.sum(sims[0]) == 0: return np.array(tksim), tksim, sims[0] return np.array(sims[0]) * vtweight + np.array(tksim) * tkweight, tksim, sims[0] def token_similarity(self, atks, btkss): def toDict(tks): d = {} if isinstance(tks, str): tks = tks.split() for t, c in self.tw.weights(tks, preprocess=False): if t not in d: d[t] = 0 d[t] += c return d atks = toDict(atks) btkss = [toDict(tks) for tks in btkss] return [self.similarity(atks, btks) for btks in btkss] def similarity(self, qtwt, dtwt): if isinstance(dtwt, type("")): dtwt = {t: w for t, w in self.tw.weights(self.tw.split(dtwt), preprocess=False)} if isinstance(qtwt, type("")): qtwt = {t: w for t, w in self.tw.weights(self.tw.split(qtwt), preprocess=False)} s = 1e-9 for k, v in qtwt.items(): if k in dtwt: s += v # * dtwt[k] q = 1e-9 for k, v in qtwt.items(): q += v return s / q def paragraph(self, content_tks: str, keywords: list = [], keywords_topn=30): if isinstance(content_tks, str): content_tks = [c.strip() for c in content_tks.strip() if c.strip()] tks_w = self.tw.weights(content_tks, preprocess=False) keywords = [f'"{k.strip()}"' for k in keywords] for tk, w in sorted(tks_w, key=lambda x: x[1] * -1)[:keywords_topn]: tk_syns = self.syn.lookup(tk) tk_syns = [FulltextQueryer.subSpecialChar(s) for s in tk_syns] tk_syns = [rag_tokenizer.fine_grained_tokenize(s) for s in tk_syns if s] tk_syns = [f'"{s}"' if s.find(" ") > 0 else s for s in tk_syns] tk = FulltextQueryer.subSpecialChar(tk) if tk.find(" ") > 0: tk = '"%s"' % tk if tk_syns: tk = f"({tk} OR (%s)^0.2)" % " ".join(tk_syns) if tk: keywords.append(f"{tk}^{w}") return MatchTextExpr(self.query_fields, " ".join(keywords), 100, {"minimum_should_match": min(3, len(keywords) / 10)})