Refa: token similarity calculations. (#6614)

### What problem does this PR solve?

#6507

### Type of change

- [x] Performance Improvement

rag/nlp/query.py

 
 import logging
 import json
+import math
 import re
-from rag.utils.doc_store_conn import MatchTextExpr
+from collections import defaultdict
 
+from rag.utils.doc_store_conn import MatchTextExpr
 from rag.nlp import rag_tokenizer, term_weight, synonym
 
 
 
     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 = defaultdict(int)
+            wts = self.tw.weights(tks, preprocess=False)
+            for i, (t, c) in enumerate(wts):
                 d[t] += c
             return d
 
         s = 1e-9
         for k, v in qtwt.items():
             if k in dtwt:
-                s += v  # * dtwt[k]
+                s += v * dtwt[k]
         q = 1e-9
         for k, v in qtwt.items():
-            q += v
-        return s / q
+            q += v * v
+        return math.sqrt(3. * (s / q / math.log10( len(dtwt.keys()) + 512 )))
 
     def paragraph(self, content_tks: str, keywords: list = [], keywords_topn=30):
         if isinstance(content_tks, str):

rag/nlp/search.py

 #
 import logging
 import re
+from collections import OrderedDict
 from dataclasses import dataclass
 
 from rag.settings import TAG_FLD, PAGERANK_FLD
                 sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]]
         ins_tw = []
         for i in sres.ids:
-            content_ltks = sres.field[i][cfield].split()
+            content_ltks = list(OrderedDict.fromkeys(sres.field[i][cfield].split()))
             title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t]
             question_tks = [t for t in sres.field[i].get("question_tks", "").split() if t]
             important_kwd = sres.field[i].get("important_kwd", [])