diff --git a/rag/benchmark.py b/rag/benchmark.py
index 490c031f97c..aea4ef99c5f 100644
--- a/rag/benchmark.py
+++ b/rag/benchmark.py
@@ -16,11 +16,15 @@
 import json
 import os
 from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
+from copy import deepcopy
+
 from api.db import LLMType
 from api.db.services.llm_service import LLMBundle
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.settings import retrievaler
 from api.utils import get_uuid
+from api.utils.file_utils import get_project_base_directory
 from rag.nlp import tokenize, search
 from rag.utils.es_conn import ELASTICSEARCH
 from ranx import evaluate
@@ -63,14 +67,34 @@ def embedding(self, docs, batch_size=16):
             d["q_%d_vec" % len(v)] = v
         return docs
 
+    @staticmethod
+    def init_kb(index_name):
+        idxnm = search.index_name(index_name)
+        if ELASTICSEARCH.indexExist(idxnm):
+            ELASTICSEARCH.deleteIdx(search.index_name(index_name))
+
+        return ELASTICSEARCH.createIdx(idxnm, json.load(
+            open(os.path.join(get_project_base_directory(), "conf", "mapping.json"), "r")))
+
     def ms_marco_index(self, file_path, index_name):
         qrels = defaultdict(dict)
         texts = defaultdict(dict)
         docs = []
         filelist = os.listdir(file_path)
+        self.init_kb(index_name)
+
+        max_workers = int(os.environ.get('MAX_WORKERS', 3))
+        exe = ThreadPoolExecutor(max_workers=max_workers)
+        threads = []
+
+        def slow_actions(es_docs, idx_nm):
+            es_docs = self.embedding(es_docs)
+            ELASTICSEARCH.bulk(es_docs, idx_nm)
+            return True
+
         for dir in filelist:
             data = pd.read_parquet(os.path.join(file_path, dir))
-            for i in tqdm(range(len(data)), colour="green", desc="Indexing:" + dir):
+            for i in tqdm(range(len(data)), colour="green", desc="Tokenizing:" + dir):
 
                 query = data.iloc[i]['query']
                 for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']):
@@ -82,12 +106,17 @@ def ms_marco_index(self, file_path, index_name):
                     texts[d["id"]] = text
                     qrels[query][d["id"]] = int(rel)
                 if len(docs) >= 32:
-                    docs = self.embedding(docs)
-                    ELASTICSEARCH.bulk(docs, search.index_name(index_name))
+                    threads.append(
+                        exe.submit(slow_actions, deepcopy(docs), search.index_name(index_name)))
                     docs = []
 
-        docs = self.embedding(docs)
-        ELASTICSEARCH.bulk(docs, search.index_name(index_name))
+        threads.append(
+            exe.submit(slow_actions, deepcopy(docs), search.index_name(index_name)))
+
+        for i in tqdm(range(len(threads)), colour="red", desc="Indexing:" + dir):
+            if not threads[i].result().output:
+                print("Indexing error...")
+
         return qrels, texts
 
     def trivia_qa_index(self, file_path, index_name):
diff --git a/rag/nlp/term_weight.py b/rag/nlp/term_weight.py
index 1b22dc9e176..f0be0527ffb 100644
--- a/rag/nlp/term_weight.py
+++ b/rag/nlp/term_weight.py
@@ -227,7 +227,7 @@ def idf(s, N): return math.log10(10 + ((N - s + 0.5) / (s + 0.5)))
             idf2 = np.array([idf(df(t), 1000000000) for t in tks])
             wts = (0.3 * idf1 + 0.7 * idf2) * \
                 np.array([ner(t) * postag(t) for t in tks])
-            tw = zip(tks, wts)
+            tw = list(zip(tks, wts))
         else:
             for tk in tks:
                 tt = self.tokenMerge(self.pretoken(tk, True))