diff --git a/AI/WxMini/X_Start.py b/AI/WxMini/X_Start.py
index 55df224c..defadd3a 100644
--- a/AI/WxMini/X_Start.py
+++ b/AI/WxMini/X_Start.py
@@ -26,27 +26,6 @@ def extract_keywords(text, topK=3):
     keywords = jieba.analyse.extract_tags(text, topK=topK)
     return keywords
 
-# 构建查询条件
-def build_query_expr(session_id, keywords):
-    """
-    构建查询条件
-    :param session_id: 用户会话 ID
-    :param keywords: 关键词列表
-    :return: 查询条件表达式
-    """
-    # 基础条件：过滤 session_id
-    expr = f"session_id == '{session_id}'"
-    # 添加关键词条件
-    if keywords:
-        keyword_conditions = []
-        for keyword in keywords:
-            if len(keyword) > 1:  # 过滤过短的关键词
-                # 使用前缀匹配
-                keyword_conditions.append(f"user_input like '{keyword}%'")
-        if keyword_conditions:
-            expr += " and (" + " or ".join(keyword_conditions) + ")"
-    return expr
-
 # 初始化 Word2Vec 模型
 model_path = MS_MODEL_PATH
 model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
@@ -93,9 +72,6 @@ client = OpenAI(
     base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 )
 
-# 设置相似度阈值
-SIMILARITY_THRESHOLD = 0.5  # 距离小于 0.5 的结果被认为是高相似度
-
 @app.post("/reply")
 async def reply(session_id: str = Form(...), prompt: str = Form(...)):
     """
@@ -111,15 +87,7 @@ async def reply(session_id: str = Form(...), prompt: str = Form(...)):
         # 将用户输入转换为嵌入向量
         current_embedding = text_to_embedding(prompt)
 
-        # 提取用户输入的关键词
-        keywords = extract_keywords(prompt)
-        print(f"提取的关键词: {keywords}")
-
-        # 构建查询条件
-        expr = build_query_expr(session_id, keywords)
-        print(f"查询条件: {expr}")
-
-        # 查询与当前对话最相关的五条历史交互
+        # 查询与当前对话最相关的历史交互
         search_params = {
             "metric_type": "L2",  # 使用 L2 距离度量方式
             "params": {"nprobe": MS_NPROBE}  # 设置 IVF_FLAT 的 nprobe 参数
@@ -128,7 +96,6 @@ async def reply(session_id: str = Form(...), prompt: str = Form(...)):
         results = collection_manager.search(
             data=current_embedding,  # 输入向量
             search_params=search_params,  # 搜索参数
-            expr=expr,  # 查询条件
             limit=5  # 返回 5 条结果
         )
         end_time = time.time()
@@ -139,18 +106,12 @@ async def reply(session_id: str = Form(...), prompt: str = Form(...)):
             for hits in results:
                 for hit in hits:
                     try:
-                        # 过滤低相似度的结果
-                        if hit.distance > SIMILARITY_THRESHOLD:
-                            print(f"跳过低相似度结果，距离: {hit.distance}")
-                            continue
-
                         # 查询非向量字段
                         record = collection_manager.query_by_id(hit.id)
                         if record:
                             print(f"查询到的记录: {record}")
-                            # 只添加与当前问题高度相关的历史交互
-                            if any(keyword in record['user_input'] for keyword in keywords):
-                                history_prompt += f"用户: {record['user_input']}\n大模型: {record['model_response']}\n"
+                            # 添加历史交互
+                            history_prompt += f"用户: {record['user_input']}\n大模型: {record['model_response']}\n"
                     except Exception as e:
                         print(f"查询失败: {e}")
 
diff --git a/AI/WxMini/audio/1cd4d103-4c34-4370-8b12-6d73b4bd1f00.mp3 b/AI/WxMini/audio/1cd4d103-4c34-4370-8b12-6d73b4bd1f00.mp3
new file mode 100644
index 00000000..e69de29b
diff --git a/AI/WxMini/audio/2a732773-6265-455c-8009-f65dd67230b6.mp3 b/AI/WxMini/audio/2a732773-6265-455c-8009-f65dd67230b6.mp3
new file mode 100644
index 00000000..e69de29b