mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-10-31 09:50:00 +00:00
217 lines
6.7 KiB
Python
217 lines
6.7 KiB
Python
# -*- coding: utf-8 -*-
|
|
import math
|
|
import json
|
|
import re
|
|
import os
|
|
import numpy as np
|
|
from rag.nlp import huqie
|
|
from api.utils.file_utils import get_project_base_directory
|
|
|
|
|
|
class Dealer:
|
|
def __init__(self):
|
|
self.stop_words = set(["请问",
|
|
"您",
|
|
"你",
|
|
"我",
|
|
"他",
|
|
"是",
|
|
"的",
|
|
"就",
|
|
"有",
|
|
"于",
|
|
"及",
|
|
"即",
|
|
"在",
|
|
"为",
|
|
"最",
|
|
"有",
|
|
"从",
|
|
"以",
|
|
"了",
|
|
"将",
|
|
"与",
|
|
"吗",
|
|
"吧",
|
|
"中",
|
|
"#",
|
|
"什么",
|
|
"怎么",
|
|
"哪个",
|
|
"哪些",
|
|
"啥",
|
|
"相关"])
|
|
|
|
def load_dict(fnm):
|
|
res = {}
|
|
f = open(fnm, "r")
|
|
while True:
|
|
l = f.readline()
|
|
if not l:
|
|
break
|
|
arr = l.replace("\n", "").split("\t")
|
|
if len(arr) < 2:
|
|
res[arr[0]] = 0
|
|
else:
|
|
res[arr[0]] = int(arr[1])
|
|
|
|
c = 0
|
|
for _, v in res.items():
|
|
c += v
|
|
if c == 0:
|
|
return set(res.keys())
|
|
return res
|
|
|
|
fnm = os.path.join(get_project_base_directory(), "rag/res")
|
|
self.ne, self.df = {}, {}
|
|
try:
|
|
self.ne = json.load(open(os.path.join(fnm, "ner.json"), "r"))
|
|
except Exception as e:
|
|
print("[WARNING] Load ner.json FAIL!")
|
|
try:
|
|
self.df = load_dict(os.path.join(fnm, "term.freq"))
|
|
except Exception as e:
|
|
print("[WARNING] Load term.freq FAIL!")
|
|
|
|
def pretoken(self, txt, num=False, stpwd=True):
|
|
patt = [
|
|
r"[~—\t @#%!<>,\.\?\":;'\{\}\[\]_=\(\)\|，。？》•●○↓《；‘’：“”【¥ 】…￥！、·（）×`&\\/「」\\]"
|
|
]
|
|
rewt = [
|
|
]
|
|
for p, r in rewt:
|
|
txt = re.sub(p, r, txt)
|
|
|
|
res = []
|
|
for t in huqie.qie(txt).split(" "):
|
|
tk = t
|
|
if (stpwd and tk in self.stop_words) or (
|
|
re.match(r"[0-9]$", tk) and not num):
|
|
continue
|
|
for p in patt:
|
|
if re.match(p, t):
|
|
tk = "#"
|
|
break
|
|
tk = re.sub(r"([\+\\-])", r"\\\1", tk)
|
|
if tk != "#" and tk:
|
|
res.append(tk)
|
|
return res
|
|
|
|
def tokenMerge(self, tks):
|
|
def oneTerm(t): return len(t) == 1 or re.match(r"[0-9a-z]{1,2}$", t)
|
|
|
|
res, i = [], 0
|
|
while i < len(tks):
|
|
j = i
|
|
if i == 0 and oneTerm(tks[i]) and len(
|
|
tks) > 1 and len(tks[i + 1]) > 1: # 多 工位
|
|
res.append(" ".join(tks[0:2]))
|
|
i = 2
|
|
continue
|
|
|
|
while j < len(
|
|
tks) and tks[j] and tks[j] not in self.stop_words and oneTerm(tks[j]):
|
|
j += 1
|
|
if j - i > 1:
|
|
if j - i < 5:
|
|
res.append(" ".join(tks[i:j]))
|
|
i = j
|
|
else:
|
|
res.append(" ".join(tks[i:i + 2]))
|
|
i = i + 2
|
|
else:
|
|
if len(tks[i]) > 0:
|
|
res.append(tks[i])
|
|
i += 1
|
|
return [t for t in res if t]
|
|
|
|
def ner(self, t):
|
|
if not self.ne:
|
|
return ""
|
|
res = self.ne.get(t, "")
|
|
if res:
|
|
return res
|
|
|
|
def split(self, txt):
|
|
tks = []
|
|
for t in re.sub(r"[ \t]+", " ", txt).split(" "):
|
|
if tks and re.match(r".*[a-zA-Z]$", tks[-1]) and \
|
|
re.match(r".*[a-zA-Z]$", t) and tks and \
|
|
self.ne.get(t, "") != "func" and self.ne.get(tks[-1], "") != "func":
|
|
tks[-1] = tks[-1] + " " + t
|
|
else:
|
|
tks.append(t)
|
|
return tks
|
|
|
|
def weights(self, tks):
|
|
def skill(t):
|
|
if t not in self.sk:
|
|
return 1
|
|
return 6
|
|
|
|
def ner(t):
|
|
if not self.ne or t not in self.ne:
|
|
return 1
|
|
m = {"toxic": 2, "func": 1, "corp": 3, "loca": 3, "sch": 3, "stock": 3,
|
|
"firstnm": 1}
|
|
return m[self.ne[t]]
|
|
|
|
def postag(t):
|
|
t = huqie.tag(t)
|
|
if t in set(["r", "c", "d"]):
|
|
return 0.3
|
|
if t in set(["ns", "nt"]):
|
|
return 3
|
|
if t in set(["n"]):
|
|
return 2
|
|
if re.match(r"[0-9-]+", t):
|
|
return 2
|
|
return 1
|
|
|
|
def freq(t):
|
|
if re.match(r"[0-9\. -]+$", t):
|
|
return 10000
|
|
s = huqie.freq(t)
|
|
if not s and re.match(r"[a-z\. -]+$", t):
|
|
return 10
|
|
if not s:
|
|
s = 0
|
|
|
|
if not s and len(t) >= 4:
|
|
s = [tt for tt in huqie.qieqie(t).split(" ") if len(tt) > 1]
|
|
if len(s) > 1:
|
|
s = np.min([freq(tt) for tt in s]) / 6.
|
|
else:
|
|
s = 0
|
|
|
|
return max(s, 10)
|
|
|
|
def df(t):
|
|
if re.match(r"[0-9\. -]+$", t):
|
|
return 100000
|
|
if t in self.df:
|
|
return self.df[t] + 3
|
|
elif re.match(r"[a-z\. -]+$", t):
|
|
return 3
|
|
elif len(t) >= 4:
|
|
s = [tt for tt in huqie.qieqie(t).split(" ") if len(tt) > 1]
|
|
if len(s) > 1:
|
|
return max(3, np.min([df(tt) for tt in s]) / 6.)
|
|
|
|
return 3
|
|
|
|
def idf(s, N): return math.log10(10 + ((N - s + 0.5) / (s + 0.5)))
|
|
|
|
tw = []
|
|
for tk in tks:
|
|
tt = self.tokenMerge(self.pretoken(tk, True))
|
|
idf1 = np.array([idf(freq(t), 10000000) for t in tt])
|
|
idf2 = np.array([idf(df(t), 1000000000) for t in tt])
|
|
wts = (0.3 * idf1 + 0.7 * idf2) * \
|
|
np.array([ner(t) * postag(t) for t in tt])
|
|
|
|
tw.extend(zip(tt, wts))
|
|
|
|
S = np.sum([s for _, s in tw])
|
|
return [(t, s / S) for t, s in tw]
|