diff --git a/rag/app/table.py b/rag/app/table.py
index 14facd169..450cd6280 100644
--- a/rag/app/table.py
+++ b/rag/app/table.py
@@ -20,6 +20,8 @@ from io import BytesIO
 from xpinyin import Pinyin
 import numpy as np
 import pandas as pd
+from collections import Counter
+
 # from openpyxl import load_workbook, Workbook
 from dateutil.parser import parse as datetime_parse
 
@@ -30,8 +32,7 @@ from deepdoc.parser import ExcelParser
 
 
 class Excel(ExcelParser):
-    def __call__(self, fnm, binary=None, from_page=0,
-                 to_page=10000000000, callback=None):
+    def __call__(self, fnm, binary=None, from_page=0, to_page=10000000000, callback=None):
         if not binary:
             wb = Excel._load_excel_to_workbook(fnm)
         else:
@@ -49,10 +50,7 @@ class Excel(ExcelParser):
                 continue
             headers = [cell.value for cell in rows[0]]
             missed = set([i for i, h in enumerate(headers) if h is None])
-            headers = [
-                cell.value for i,
-                cell in enumerate(
-                    rows[0]) if i not in missed]
+            headers = [cell.value for i, cell in enumerate(rows[0]) if i not in missed]
             if not headers:
                 continue
             data = []
@@ -62,9 +60,7 @@ class Excel(ExcelParser):
                     continue
                 if rn - 1 >= to_page:
                     break
-                row = [
-                    cell.value for ii,
-                    cell in enumerate(r) if ii not in missed]
+                row = [cell.value for ii, cell in enumerate(r) if ii not in missed]
                 if len(row) != len(headers):
                     fails.append(str(i))
                     continue
@@ -74,8 +70,7 @@ class Excel(ExcelParser):
                 continue
             res.append(pd.DataFrame(np.array(data), columns=headers))
 
-        callback(0.3, ("Extract records: {}~{}".format(from_page + 1, min(to_page, from_page + rn)) + (
-            f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
+        callback(0.3, ("Extract records: {}~{}".format(from_page + 1, min(to_page, from_page + rn)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
         return res
 
 
@@ -87,8 +82,7 @@ def trans_datatime(s):
 
 
 def trans_bool(s):
-    if re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√)$",
-                str(s).strip(), flags=re.IGNORECASE):
+    if re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√)$", str(s).strip(), flags=re.IGNORECASE):
         return "yes"
     if re.match(r"(false|no|否|⍻|×)$", str(s).strip(), flags=re.IGNORECASE):
         return "no"
@@ -97,8 +91,7 @@ def trans_bool(s):
 def column_data_type(arr):
     arr = list(arr)
     counts = {"int": 0, "float": 0, "text": 0, "datetime": 0, "bool": 0}
-    trans = {t: f for f, t in
-             [(int, "int"), (float, "float"), (trans_datatime, "datetime"), (trans_bool, "bool"), (str, "text")]}
+    trans = {t: f for f, t in [(int, "int"), (float, "float"), (trans_datatime, "datetime"), (trans_bool, "bool"), (str, "text")]}
     for a in arr:
         if a is None:
             continue
@@ -127,31 +120,25 @@ def column_data_type(arr):
     return arr, ty
 
 
-def chunk(filename, binary=None, from_page=0, to_page=10000000000,
-          lang="Chinese", callback=None, **kwargs):
+def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese", callback=None, **kwargs):
     """
-        Excel and csv(txt) format files are supported.
-        For csv or txt file, the delimiter between columns is TAB.
-        The first line must be column headers.
-        Column headers must be meaningful terms inorder to make our NLP model understanding.
-        It's good to enumerate some synonyms using slash '/' to separate, and even better to
-        enumerate values using brackets like 'gender/sex(male, female)'.
-        Here are some examples for headers:
-            1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL)
-            2. 姓名/名字\t电话/手机/微信\t最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）
+    Excel and csv(txt) format files are supported.
+    For csv or txt file, the delimiter between columns is TAB.
+    The first line must be column headers.
+    Column headers must be meaningful terms inorder to make our NLP model understanding.
+    It's good to enumerate some synonyms using slash '/' to separate, and even better to
+    enumerate values using brackets like 'gender/sex(male, female)'.
+    Here are some examples for headers:
+        1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL)
+        2. 姓名/名字\t电话/手机/微信\t最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）
 
-        Every row in table will be treated as a chunk.
+    Every row in table will be treated as a chunk.
     """
 
     if re.search(r"\.xlsx?$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         excel_parser = Excel()
-        dfs = excel_parser(
-            filename,
-            binary,
-            from_page=from_page,
-            to_page=to_page,
-            callback=callback)
+        dfs = excel_parser(filename, binary, from_page=from_page, to_page=to_page, callback=callback)
     elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
         txt = get_text(filename, binary)
@@ -170,40 +157,29 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
                 continue
             rows.append(row)
 
-        callback(0.3, ("Extract records: {}~{}".format(from_page, min(len(lines), to_page)) + (
-            f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
+        callback(0.3, ("Extract records: {}~{}".format(from_page, min(len(lines), to_page)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
 
         dfs = [pd.DataFrame(np.array(rows), columns=headers)]
 
     else:
-        raise NotImplementedError(
-            "file type not supported yet(excel, text, csv supported)")
+        raise NotImplementedError("file type not supported yet(excel, text, csv supported)")
 
     res = []
     PY = Pinyin()
-    fieds_map = {
-        "text": "_tks",
-        "int": "_long",
-        "keyword": "_kwd",
-        "float": "_flt",
-        "datetime": "_dt",
-        "bool": "_kwd"}
+    fieds_map = {"text": "_tks", "int": "_long", "keyword": "_kwd", "float": "_flt", "datetime": "_dt", "bool": "_kwd"}
     for df in dfs:
         for n in ["id", "_id", "index", "idx"]:
             if n in df.columns:
                 del df[n]
         clmns = df.columns.values
         if len(clmns) != len(set(clmns)):
-            duplicates = [col for col in clmns if list(clmns).count(col) > 1]
-            raise ValueError(f"Duplicate column names detected: {set(duplicates)}")
+            col_counts = Counter(clmns)
+            duplicates = [col for col, count in col_counts.items() if count > 1]
+            if duplicates:
+                raise ValueError(f"Duplicate column names detected: {duplicates}\nFrom: {clmns}")
+
         txts = list(copy.deepcopy(clmns))
-        py_clmns = [
-            PY.get_pinyins(
-                re.sub(
-                    r"(/.*|（[^（）]+?）|\([^()]+?\))",
-                    "",
-                    str(n)),
-                '_')[0] for n in clmns]
+        py_clmns = [PY.get_pinyins(re.sub(r"(/.*|（[^（）]+?）|\([^()]+?\))", "", str(n)), "_")[0] for n in clmns]
         clmn_tys = []
         for j in range(len(clmns)):
             cln, ty = column_data_type(df[clmns[j]])
@@ -211,15 +187,11 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
             df[clmns[j]] = cln
             if ty == "text":
                 txts.extend([str(c) for c in cln if c])
-        clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], str(clmns[i]).replace("_", " "))
-                     for i in range(len(clmns))]
+        clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], str(clmns[i]).replace("_", " ")) for i in range(len(clmns))]
 
         eng = lang.lower() == "english"  # is_english(txts)
         for ii, row in df.iterrows():
-            d = {
-                "docnm_kwd": filename,
-                "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
-            }
+            d = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))}
             row_txt = []
             for j in range(len(clmns)):
                 if row[clmns[j]] is None:
@@ -229,16 +201,14 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
                 if not isinstance(row[clmns[j]], pd.Series) and pd.isna(row[clmns[j]]):
                     continue
                 fld = clmns_map[j][0]
-                d[fld] = row[clmns[j]] if clmn_tys[j] != "text" else rag_tokenizer.tokenize(
-                    row[clmns[j]])
+                d[fld] = row[clmns[j]] if clmn_tys[j] != "text" else rag_tokenizer.tokenize(row[clmns[j]])
                 row_txt.append("{}:{}".format(clmns[j], row[clmns[j]]))
             if not row_txt:
                 continue
             tokenize(d, "; ".join(row_txt), eng)
             res.append(d)
 
-        KnowledgebaseService.update_parser_config(
-            kwargs["kb_id"], {"field_map": {k: v for k, v in clmns_map}})
+        KnowledgebaseService.update_parser_config(kwargs["kb_id"], {"field_map": {k: v for k, v in clmns_map}})
     callback(0.35, "")
 
     return res