File size: 10,165 Bytes
b9fe2b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
#
# 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):
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
def question(self, txt, tbl="qa", min_match: float = 0.6):
txt = re.sub(
r"[ :|\r\n\t,，。？?/`!！&^%%()\[\]{}<>]+",
" ",
rag_tokenizer.tradi2simp(rag_tokenizer.strQ2B(txt.lower())),
).strip()
txt = FulltextQueryer.rmWWW(txt)
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.) 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):
if len(tk) < 3:
return False
if re.match(r"[0-9a-z\.\+#_\*-]+$", tk):
return False
return True
txt = FulltextQueryer.rmWWW(txt)
qs, keywords = [], []
for tt in self.tw.split(txt)[:256]: # .split():
if not tt:
continue
keywords.append(tt)
twts = self.tw.weights([tt])
syns = self.syn.lookup(tt)
if syns and len(keywords) < 32:
keywords.extend(syns)
logging.debug(json.dumps(twts, ensure_ascii=False))
tms = []
for tk, w in sorted(twts, key=lambda x: x[1] * -1):
sm = (
rag_tokenizer.fine_grained_tokenize(tk).split()
if need_fine_grained_tokenize(tk)
else []
)
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]
if len(keywords) < 32:
keywords.append(re.sub(r"[ \\\"']+", "", tk))
keywords.extend(sm)
tk_syns = self.syn.lookup(tk)
tk_syns = [FulltextQueryer.subSpecialChar(s) for s in tk_syns]
if len(keywords) < 32:
keywords.extend([s for s in tk_syns if s])
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]
if len(keywords) >= 32:
break
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))
tms = " ".join([f"({t})^{w}" for t, w in tms])
if len(twts) > 1:
tms += ' ("%s"~2)^1.5' % rag_tokenizer.tokenize(tt)
syns = " OR ".join(
[
'"%s"'
% rag_tokenizer.tokenize(FulltextQueryer.subSpecialChar(s))
for s in syns
]
)
if syns and tms:
tms = f"({tms})^5 OR ({syns})^0.7"
qs.append(tms)
if qs:
query = " OR ".join([f"({t})" for t in qs if t])
return MatchTextExpr(
self.query_fields, query, 100, {"minimum_should_match": min_match}
), keywords
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)})
|