diff --git a/AI/WxMini/BigStart.py b/AI/WxMini/BigStart.py
new file mode 100644
index 00000000..2246e935
--- /dev/null
+++ b/AI/WxMini/BigStart.py
@@ -0,0 +1,190 @@
+import os
+import uuid
+import time
+import jieba
+from fastapi import FastAPI, Form, HTTPException
+from openai import AsyncOpenAI  # 使用异步客户端
+from gensim.models import KeyedVectors
+from contextlib import asynccontextmanager
+from TtsConfig import *
+from WxMini.OssUtil import upload_mp3_to_oss
+from WxMini.TtsUtil import TTS
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+from WxMini.Milvus.Config.MulvusConfig import *
+import asyncio  # 引入异步支持
+import logging  # 增加日志记录
+
+import jieba.analyse
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+# 提取用户输入的关键词
+def extract_keywords(text, topK=3):
+    """
+    提取用户输入的关键词
+    :param text: 用户输入的文本
+    :param topK: 返回的关键词数量
+    :return: 关键词列表
+    """
+    keywords = jieba.analyse.extract_tags(text, topK=topK)
+    return keywords
+
+# 初始化 Word2Vec 模型
+model_path = MS_MODEL_PATH
+model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
+logger.info(f"模型加载成功，词向量维度: {model.vector_size}")
+
+# 初始化 Milvus 连接池
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 将文本转换为嵌入向量
+def text_to_embedding(text):
+    words = jieba.lcut(text)  # 使用 jieba 分词
+    logger.info(f"文本: {text}, 分词结果: {words}")
+    embeddings = [model[word] for word in words if word in model]
+    logger.info(f"有效词向量数量: {len(embeddings)}")
+    if embeddings:
+        avg_embedding = sum(embeddings) / len(embeddings)
+        logger.info(f"生成的平均向量: {avg_embedding[:5]}...")  # 打印前 5 维
+        return avg_embedding
+    else:
+        logger.warning("未找到有效词，返回零向量")
+        return [0.0] * model.vector_size
+
+# 使用 Lifespan Events 处理应用启动和关闭逻辑
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # 应用启动时加载集合到内存
+    collection_manager.load_collection()
+    logger.info(f"集合 '{collection_name}' 已加载到内存。")
+    yield
+    # 应用关闭时释放连接池
+    milvus_pool.close()
+    logger.info("Milvus 连接池已关闭。")
+
+# 初始化 FastAPI 应用
+app = FastAPI(lifespan=lifespan)
+
+# 初始化异步 OpenAI 客户端
+client = AsyncOpenAI(
+    api_key=MODEL_API_KEY,
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
+
+@app.post("/reply")
+async def reply(session_id: str = Form(...), prompt: str = Form(...)):
+    """
+    接收用户输入的 prompt，调用大模型并返回结果
+    :param session_id: 用户会话 ID
+    :param prompt: 用户输入的 prompt
+    :return: 大模型的回复
+    """
+    try:
+        logger.info(f"收到用户输入: {prompt}")
+        # 从连接池中获取一个连接
+        connection = milvus_pool.get_connection()
+
+        # 将用户输入转换为嵌入向量
+        current_embedding = text_to_embedding(prompt)
+
+        # 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
+        }
+        start_time = time.time()
+        results = await asyncio.to_thread(  # 将阻塞操作放到线程池中执行
+            collection_manager.search,
+            data=current_embedding,  # 输入向量
+            search_params=search_params,  # 搜索参数
+            expr=f"session_id == '{session_id}'",  # 按 session_id 过滤
+            limit=5  # 返回 5 条结果
+        )
+        end_time = time.time()
+
+        # 构建历史交互提示词
+        history_prompt = ""
+        if results:
+            for hits in results:
+                for hit in hits:
+                    try:
+                        # 查询非向量字段
+                        record = await asyncio.to_thread(collection_manager.query_by_id, hit.id)
+                        if record:
+                            logger.info(f"查询到的记录: {record}")
+                            # 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:
+                        logger.error(f"查询失败: {e}")
+
+        logger.info(f"历史交互提示词: {history_prompt}")
+
+        # 调用大模型，将历史交互作为提示词
+        response = await client.chat.completions.create(  # 使用异步调用
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": "你是一个私人助理，负责回答用户的问题。请根据用户的历史对话和当前问题，提供准确且简洁的回答。不要提及你是通义千问或其他无关信息,也不可以回复与本次用户问题不相关的历史对话记录内容。"},
+                {"role": "user", "content": f"历史对话记录：{history_prompt},本次用户问题: {prompt}"}  # 将历史交互和当前输入一起发送
+            ],
+            max_tokens=500
+        )
+
+        # 提取生成的回复
+        if response.choices and response.choices[0].message.content:
+            result = response.choices[0].message.content.strip()
+            logger.info(f"大模型回复: {result}")
+
+            # 记录用户输入和大模型反馈到向量数据库
+            timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+            entities = [
+                [session_id],  # session_id
+                [prompt[:500]],  # user_input，截断到 500 字符
+                [result[:500]],  # model_response，截断到 500 字符
+                [timestamp],  # timestamp
+                [current_embedding]  # embedding
+            ]
+            await asyncio.to_thread(collection_manager.insert_data, entities)
+            logger.info("用户输入和大模型反馈已记录到向量数据库。")
+
+            # 调用 TTS 生成 MP3
+            uuid_str = str(uuid.uuid4())
+            tts_file = "audio/" + uuid_str + ".mp3"
+            t = TTS(tts_file)
+            await asyncio.to_thread(t.start, result)  # 将 TTS 生成放到线程池中执行
+            # 文件上传到 OSS
+            await asyncio.to_thread(upload_mp3_to_oss, tts_file, tts_file)
+            # 删除临时文件
+            try:
+                os.remove(tts_file)
+                logger.info(f"临时文件 {tts_file} 已删除")
+            except Exception as e:
+                logger.error(f"删除临时文件失败: {e}")
+            # 完整的 URL
+            url = 'https://ylt.oss-cn-hangzhou.aliyuncs.com/' + tts_file
+            return {
+                "success": True,
+                "url": url,
+                "search_time": end_time - start_time,  # 返回查询耗时
+                "response": result  # 返回大模型的回复
+            }
+        else:
+            raise HTTPException(status_code=500, detail="大模型未返回有效结果")
+    except Exception as e:
+        logger.error(f"调用大模型失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"调用大模型失败: {str(e)}")
+    finally:
+        # 释放连接
+        milvus_pool.release_connection(connection)
+
+# 运行 FastAPI 应用
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=5600)
\ No newline at end of file
diff --git a/AI/WxMini/Milvus/T1_create_collection.py b/AI/WxMini/Milvus/Test/T1_create_collection.py
similarity index 100%
rename from AI/WxMini/Milvus/T1_create_collection.py
rename to AI/WxMini/Milvus/Test/T1_create_collection.py
diff --git a/AI/WxMini/Milvus/T2_create_index.py b/AI/WxMini/Milvus/Test/T2_create_index.py
similarity index 100%
rename from AI/WxMini/Milvus/T2_create_index.py
rename to AI/WxMini/Milvus/Test/T2_create_index.py
diff --git a/AI/WxMini/Milvus/T3_insert_data.py b/AI/WxMini/Milvus/Test/T3_insert_data.py
similarity index 100%
rename from AI/WxMini/Milvus/T3_insert_data.py
rename to AI/WxMini/Milvus/Test/T3_insert_data.py
diff --git a/AI/WxMini/Milvus/T4_select_all_data.py b/AI/WxMini/Milvus/Test/T4_select_all_data.py
similarity index 100%
rename from AI/WxMini/Milvus/T4_select_all_data.py
rename to AI/WxMini/Milvus/Test/T4_select_all_data.py
diff --git a/AI/WxMini/Milvus/T5_search_near_data.py b/AI/WxMini/Milvus/Test/T5_search_near_data.py
similarity index 100%
rename from AI/WxMini/Milvus/T5_search_near_data.py
rename to AI/WxMini/Milvus/Test/T5_search_near_data.py
diff --git a/AI/WxMini/Milvus/Test/__init__.py b/AI/WxMini/Milvus/Test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/AI/WxMini/Milvus/X1_create_collection.py b/AI/WxMini/Milvus/X1_create_collection.py
index 48de3b46..5de6417d 100644
--- a/AI/WxMini/Milvus/X1_create_collection.py
+++ b/AI/WxMini/Milvus/X1_create_collection.py
@@ -23,8 +23,8 @@ if utility.has_collection(collection_name):
 fields = [
     FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),  # 主键字段，自动生成 ID
     FieldSchema(name="session_id", dtype=DataType.VARCHAR, max_length=64),  # 会话 ID
-    FieldSchema(name="user_input", dtype=DataType.VARCHAR, max_length=500),  # 用户问题
-    FieldSchema(name="model_response", dtype=DataType.VARCHAR, max_length=500),  # 大模型反馈结果
+    FieldSchema(name="user_input", dtype=DataType.VARCHAR, max_length=2048),  # 用户问题
+    FieldSchema(name="model_response", dtype=DataType.VARCHAR, max_length=2048),  # 大模型反馈结果
     FieldSchema(name="timestamp", dtype=DataType.VARCHAR, max_length=32),  # 时间
     FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=MS_DIMENSION)  # 向量字段，维度为 200
 ]