diff --git a/examples/st_game/memory/retrieve.py b/examples/st_game/memory/retrieve.py
index d35418b77..6ff507037 100644
--- a/examples/st_game/memory/retrieve.py
+++ b/examples/st_game/memory/retrieve.py
@@ -5,8 +5,8 @@
 import datetime
 from numpy import dot
 from numpy.linalg import norm
-from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
-from utils.utils import embedding_tools
+from ..memory.agent_memory import AgentMemory, BasicMemory
+from ..utils.utils import get_embedding
 
 
 def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str,
@@ -73,7 +73,7 @@ def extract_relevance(query, score_list):
     """
     抽取相关性
     """
-    query_embedding = embedding_tools(query)
+    query_embedding = get_embedding(query)
     # 进行
     for i in range(len(score_list)):
         result = cos_sim(score_list[i]["memory"].embedding_key, query_embedding)
diff --git a/examples/st_game/utils/utils.py b/examples/st_game/utils/utils.py
index e6b29a667..5cd110e9f 100644
--- a/examples/st_game/utils/utils.py
+++ b/examples/st_game/utils/utils.py
@@ -27,16 +27,7 @@ def write_json_file(json_file: str, data: list, encoding=None):
         json.dump(data, fout, ensure_ascii=False, indent=4)
 
 
-def embedding_tools(query):
-    embedding_result = openai.Embedding.create(
-        model="text-embedding-ada-002",
-        input=query
-    )
-    embedding_key = embedding_result['data'][0]["embedding"]
-    return embedding_key
-
-
-def read_csv_to_list(curr_file: str, header=False, strip_trail=True): 
+def read_csv_to_list(curr_file: str, header=False, strip_trail=True):
     """
     Reads in a csv file to a list of list. If header is True, it returns a 
     tuple with (header row, all rows)
@@ -45,39 +36,38 @@ def read_csv_to_list(curr_file: str, header=False, strip_trail=True):
     RETURNS: 
       List of list where the component lists are the rows of the file. 
     """
-    if not header: 
+    if not header:
         analysis_list = []
-        with open(curr_file) as f_analysis_file: 
+        with open(curr_file) as f_analysis_file:
             data_reader = csv.reader(f_analysis_file, delimiter=",")
-        for count, row in enumerate(data_reader): 
-            if strip_trail: 
+        for count, row in enumerate(data_reader):
+            if strip_trail:
                 row = [i.strip() for i in row]
             analysis_list += [row]
         return analysis_list
-    else: 
+    else:
         analysis_list = []
-        with open(curr_file) as f_analysis_file: 
+        with open(curr_file) as f_analysis_file:
             data_reader = csv.reader(f_analysis_file, delimiter=",")
-        for count, row in enumerate(data_reader): 
-            if strip_trail: 
+        for count, row in enumerate(data_reader):
+            if strip_trail:
                 row = [i.strip() for i in row]
             analysis_list += [row]
         return analysis_list[0], analysis_list[1:]
 
 
-def get_embedding(text, model: str="text-embedding-ada-002"):
+def get_embedding(text, model: str = "text-embedding-ada-002"):
     text = text.replace("\n", " ")
-    if not text: 
+    if not text:
         text = "this is blank"
     return openai.Embedding.create(
         input=[text], model=model)['data'][0]['embedding']
 
 
-def generate_poig_score(scratch, event_type, description): 
-    if "is idle" in description: 
+def generate_poig_score(scratch, event_type, description):
+    if "is idle" in description:
         return 1
-    if event_type == "action": 
+    if event_type == "action":
         return get_poignancy_action(scratch, description)[0]
-    elif event_type == "chat": 
+    elif event_type == "chat":
         return get_poignancy_chat(scratch, description)[0]
-