From d2243aa45724fdb63ac24eb20d641128ed56a57b Mon Sep 17 00:00:00 2001
From: HuangHai <10402852@qq.com>
Date: Mon, 24 Mar 2025 11:26:20 +0800
Subject: [PATCH] 'commit'

---
 .../Milvus/Utils/MilvusCollectionManager.py   | 19 +++++
 AI/WxMini/Milvus/X1_create_collection.py      | 42 ++++++++++
 AI/WxMini/Milvus/X2_create_index.py           | 28 +++++++
 AI/WxMini/Milvus/X3_insert_data.py            | 61 ++++++++++++++
 AI/WxMini/Milvus/X4_select_all_data.py        | 56 +++++++++++++
 AI/WxMini/Milvus/X5_search_near_data.py       | 83 +++++++++++++++++++
 6 files changed, 289 insertions(+)
 create mode 100644 AI/WxMini/Milvus/X1_create_collection.py
 create mode 100644 AI/WxMini/Milvus/X2_create_index.py
 create mode 100644 AI/WxMini/Milvus/X3_insert_data.py
 create mode 100644 AI/WxMini/Milvus/X4_select_all_data.py
 create mode 100644 AI/WxMini/Milvus/X5_search_near_data.py

diff --git a/AI/WxMini/Milvus/Utils/MilvusCollectionManager.py b/AI/WxMini/Milvus/Utils/MilvusCollectionManager.py
index 8894b477..eefad47e 100644
--- a/AI/WxMini/Milvus/Utils/MilvusCollectionManager.py
+++ b/AI/WxMini/Milvus/Utils/MilvusCollectionManager.py
@@ -62,6 +62,25 @@ class MilvusCollectionManager:
         self.collection.load()
         print("集合已加载到内存。")
 
+    def query_by_id(self, id):
+        """
+        根据 ID 查询非向量字段
+        :param id: 记录的 ID
+        :return: 包含非向量字段的字典
+        """
+        try:
+            # 使用 Milvus 的 query 方法查询指定 ID 的记录
+            results = self.collection.query(
+                expr=f"id == {id}",  # 查询条件
+                output_fields=["id", "session_id", "user_input", "model_response", "timestamp"]  # 返回的字段
+            )
+            if results:
+                return results[0]  # 返回第一条记录
+            else:
+                return None
+        except Exception as e:
+            print(f"查询失败: {e}")
+            return None
     def search(self, query_embedding, search_params, limit=2):
         """
         查询数据
diff --git a/AI/WxMini/Milvus/X1_create_collection.py b/AI/WxMini/Milvus/X1_create_collection.py
new file mode 100644
index 00000000..48de3b46
--- /dev/null
+++ b/AI/WxMini/Milvus/X1_create_collection.py
@@ -0,0 +1,42 @@
+from pymilvus import FieldSchema, DataType, utility
+from WxMini.Milvus.Config.MulvusConfig import *
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+
+# 1. 使用连接池管理 Milvus 连接
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 2. 从连接池中获取一个连接
+connection = milvus_pool.get_connection()
+
+# 3. 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 4. 判断集合是否存在，存在则删除
+if utility.has_collection(collection_name):
+    print(f"集合 '{collection_name}' 已存在，正在删除...")
+    utility.drop_collection(collection_name)
+    print(f"集合 '{collection_name}' 已删除。")
+
+# 5. 定义集合的字段和模式
+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="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
+]
+schema_description = "Chat records collection with session ID, user input, model response, and timestamp"
+
+# 6. 创建集合
+print(f"正在创建集合 '{collection_name}'...")
+collection_manager.create_collection(fields, schema_description)
+print(f"集合 '{collection_name}' 创建成功。")
+
+# 7. 释放连接
+milvus_pool.release_connection(connection)
+
+# 8. 关闭连接池
+milvus_pool.close()
\ No newline at end of file
diff --git a/AI/WxMini/Milvus/X2_create_index.py b/AI/WxMini/Milvus/X2_create_index.py
new file mode 100644
index 00000000..3d06a886
--- /dev/null
+++ b/AI/WxMini/Milvus/X2_create_index.py
@@ -0,0 +1,28 @@
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+from WxMini.Milvus.Config.MulvusConfig import *
+
+# 1. 使用连接池管理 Milvus 连接
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 2. 从连接池中获取一个连接
+connection = milvus_pool.get_connection()
+
+# 3. 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 4. 创建索引
+index_params = {
+    "index_type": "IVF_FLAT",  # 使用 IVF_FLAT 索引类型
+    "metric_type": "L2",       # 使用 L2 距离度量方式
+    "params": {"nlist": 128}   # 设置 IVF_FLAT 的 nlist 参数
+}
+collection_manager.create_index("embedding", index_params)  # 为 embedding 字段创建索引
+print(f"集合 '{collection_name}' 的 'embedding' 字段索引创建成功。")
+
+# 5. 释放连接
+milvus_pool.release_connection(connection)
+
+# 6. 关闭连接池
+milvus_pool.close()
\ No newline at end of file
diff --git a/AI/WxMini/Milvus/X3_insert_data.py b/AI/WxMini/Milvus/X3_insert_data.py
new file mode 100644
index 00000000..d6665500
--- /dev/null
+++ b/AI/WxMini/Milvus/X3_insert_data.py
@@ -0,0 +1,61 @@
+from WxMini.Milvus.Config.MulvusConfig import *
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+from gensim.models import KeyedVectors
+import jieba
+import time
+
+# 1. 加载预训练的 Word2Vec 模型
+model_path = MS_MODEL_PATH
+model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
+print(f"模型加载成功，词向量维度: {model.vector_size}")
+
+# 功能：将文本转换为嵌入向量
+def text_to_embedding(text):
+    words = jieba.lcut(text)  # 使用 jieba 分词
+    print(f"文本: {text}, 分词结果: {words}")
+    embeddings = [model[word] for word in words if word in model]
+    print(f"有效词向量数量: {len(embeddings)}")
+    if embeddings:
+        avg_embedding = sum(embeddings) / len(embeddings)
+        print(f"生成的平均向量: {avg_embedding[:5]}...")  # 打印前 5 维
+        return avg_embedding
+    else:
+        print("未找到有效词，返回零向量")
+        return [0.0] * model.vector_size
+
+# 2. 使用连接池管理 Milvus 连接
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 3. 从连接池中获取一个连接
+connection = milvus_pool.get_connection()
+
+# 4. 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 5. 输入一个用户问题
+user_input = input("请输入一句话：")  # 例如：“我今天心情不太好”
+model_response = "我没听懂，能再说一遍吗？"  # 大模型的固定回复
+timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())  # 当前时间
+session_id = "session_001"  # 会话 ID（可以根据需要动态生成）
+
+# 6. 将用户问题转换为嵌入向量
+user_embedding = text_to_embedding(user_input)
+
+# 7. 插入数据，确保字段顺序与集合定义一致
+entities = [
+    [session_id],  # session_id
+    [user_input],  # user_input
+    [model_response],  # model_response
+    [timestamp],  # timestamp
+    [user_embedding]  # embedding
+]
+collection_manager.insert_data(entities)
+print("数据插入成功。")
+
+# 8. 释放连接
+milvus_pool.release_connection(connection)
+
+# 9. 关闭连接池
+milvus_pool.close()
\ No newline at end of file
diff --git a/AI/WxMini/Milvus/X4_select_all_data.py b/AI/WxMini/Milvus/X4_select_all_data.py
new file mode 100644
index 00000000..290c89ec
--- /dev/null
+++ b/AI/WxMini/Milvus/X4_select_all_data.py
@@ -0,0 +1,56 @@
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+from WxMini.Milvus.Config.MulvusConfig import *
+
+# 1. 使用连接池管理 Milvus 连接
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 2. 从连接池中获取一个连接
+connection = milvus_pool.get_connection()
+
+# 3. 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 4. 加载集合到内存
+collection_manager.load_collection()
+print(f"集合 '{collection_name}' 已加载到内存。")
+
+# 5. 查询所有数据
+try:
+    # 使用 Milvus 的 query 方法查询所有数据
+    results = collection_manager.collection.query(
+        expr="",  # 空表达式表示查询所有数据
+        output_fields=["id", "session_id", "user_input", "model_response", "timestamp", "embedding"],  # 指定返回的字段
+        limit=1000  # 设置最大返回记录数
+    )
+    print("查询结果：")
+    if results:
+        for result in results:
+            try:
+                # 获取字段值
+                session_id = result["session_id"]
+                user_input = result["user_input"]
+                model_response = result["model_response"]
+                timestamp = result["timestamp"]
+                embedding = result["embedding"]
+                # 打印结果
+                print(f"ID: {result['id']}")
+                print(f"会话 ID: {session_id}")
+                print(f"用户问题: {user_input}")
+                print(f"大模型回复: {model_response}")
+                print(f"时间: {timestamp}")
+                print(f"向量: {embedding[:5]}...")  # 只打印前 5 维向量
+                print("-" * 40)  # 分隔线
+            except Exception as e:
+                print(f"查询失败: {e}")
+    else:
+        print("未找到相关数据，请检查查询参数或数据。")
+except Exception as e:
+    print(f"查询失败: {e}")
+
+# 6. 释放连接
+milvus_pool.release_connection(connection)
+
+# 7. 关闭连接池
+milvus_pool.close()
\ No newline at end of file
diff --git a/AI/WxMini/Milvus/X5_search_near_data.py b/AI/WxMini/Milvus/X5_search_near_data.py
new file mode 100644
index 00000000..2f4e5233
--- /dev/null
+++ b/AI/WxMini/Milvus/X5_search_near_data.py
@@ -0,0 +1,83 @@
+import time
+import jieba  # 导入 jieba 分词库
+from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
+from WxMini.Milvus.Utils.MilvusConnectionPool import *
+from WxMini.Milvus.Config.MulvusConfig import *
+from gensim.models import KeyedVectors
+
+# 1. 加载预训练的 Word2Vec 模型
+model_path = MS_MODEL_PATH  # 替换为你的 Word2Vec 模型路径
+model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
+print(f"模型加载成功，词向量维度: {model.vector_size}")
+
+# 将文本转换为嵌入向量
+def text_to_embedding(text):
+    words = jieba.lcut(text)  # 使用 jieba 分词
+    print(f"文本: {text}, 分词结果: {words}")
+    embeddings = [model[word] for word in words if word in model]
+    print(f"有效词向量数量: {len(embeddings)}")
+    if embeddings:
+        avg_embedding = sum(embeddings) / len(embeddings)
+        print(f"生成的平均向量: {avg_embedding[:5]}...")  # 打印前 5 维
+        return avg_embedding
+    else:
+        print("未找到有效词，返回零向量")
+        return [0.0] * model.vector_size
+
+# 2. 使用连接池管理 Milvus 连接
+milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
+
+# 3. 从连接池中获取一个连接
+connection = milvus_pool.get_connection()
+
+# 4. 初始化集合管理器
+collection_name = MS_COLLECTION_NAME
+collection_manager = MilvusCollectionManager(collection_name)
+
+# 5. 加载集合到内存
+collection_manager.load_collection()
+print(f"集合 '{collection_name}' 已加载到内存。")
+
+# 6. 输入一句话
+input_text = input("请输入一句话：")  # 例如：“我今天心情不太好”
+
+# 7. 将文本转换为嵌入向量
+current_embedding = text_to_embedding(input_text)
+
+# 8. 查询与当前对话最相关的历史对话
+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 = collection_manager.search(current_embedding, search_params, limit=2)  # 返回 2 条结果
+end_time = time.time()
+
+# 9. 输出查询结果
+print("最相关的历史对话:")
+if results:
+    for hits in results:
+        for hit in hits:
+            try:
+                # 查询非向量字段
+                record = collection_manager.query_by_id(hit.id)
+                print(f"ID: {hit.id}")
+                print(f"会话 ID: {record['session_id']}")
+                print(f"用户问题: {record['user_input']}")
+                print(f"大模型回复: {record['model_response']}")
+                print(f"时间: {record['timestamp']}")
+                print(f"距离: {hit.distance}")
+                print("-" * 40)  # 分隔线
+            except Exception as e:
+                print(f"查询失败: {e}")
+else:
+    print("未找到相关历史对话，请检查查询参数或数据。")
+
+# 10. 输出查询耗时
+print(f"查询耗时: {end_time - start_time:.4f} 秒")
+
+# 11. 释放连接
+milvus_pool.release_connection(connection)
+
+# 12. 关闭连接池
+milvus_pool.close()
\ No newline at end of file