From 99fa38f1fef26214add48f41f9a47320eefdf705 Mon Sep 17 00:00:00 2001
From: HuangHai <10402852@qq.com>
Date: Sat, 22 Mar 2025 14:30:02 +0800
Subject: [PATCH] 'commit'

---
 AI/WxMini/TestMilvusNearWord.py | 94 +++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 AI/WxMini/TestMilvusNearWord.py

diff --git a/AI/WxMini/TestMilvusNearWord.py b/AI/WxMini/TestMilvusNearWord.py
new file mode 100644
index 00000000..a8e9f567
--- /dev/null
+++ b/AI/WxMini/TestMilvusNearWord.py
@@ -0,0 +1,94 @@
+from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
+import numpy as np
+import time
+
+# 1. 连接 Milvus
+connections.connect("default", host="10.10.14.101", port="19530")
+
+# 2. 定义集合的字段和模式
+fields = [
+    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
+    FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=500),  # 存储对话文本
+    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128)  # 假设嵌入向量维度为 128
+]
+schema = CollectionSchema(fields, description="Conversation history collection")
+
+# 3. 创建集合
+collection_name = "conversation_history"
+if utility.has_collection(collection_name):
+    utility.drop_collection(collection_name)  # 如果集合已存在，先删除
+
+collection = Collection(name=collection_name, schema=schema)
+
+# 4. 创建索引
+index_params = {
+    "index_type": "IVF_FLAT",  # 索引类型
+    "metric_type": "L2",       # 距离度量方式
+    "params": {"nlist": 256}   # 增加 nlist 的值
+}
+collection.create_index("embedding", index_params)
+
+# 5. 加载集合
+collection.load()
+
+# 6. 模拟多次对话
+# 假设历史对话的嵌入向量已经生成
+history = [
+    {"text": "我今天心情不太好，因为工作压力很大。", "embedding": np.random.random(128).tolist()},
+    {"text": "我最近在学习 Python，感觉很有趣。", "embedding": np.random.random(128).tolist()},
+    {"text": "我打算周末去爬山，放松一下。", "embedding": np.random.random(128).tolist()},
+    {"text": "我昨天看了一部很棒的电影，推荐给你。", "embedding": np.random.random(128).tolist()}
+]
+
+# 将历史对话插入 Milvus
+for item in history:
+    insert_result = collection.insert([[item["text"]], [item["embedding"]]])
+    print(f"插入结果: {insert_result}")
+
+# 刷新集合
+collection.flush()
+
+# 调试：查询集合中的所有数据
+print("集合中的数据:")
+result = collection.query(expr="", output_fields=["id", "text"], limit=10)
+if result:
+    for item in result:
+        print(f"ID: {item['id']}, Text: {item['text']}")
+else:
+    print("集合中没有数据，请检查数据插入步骤。")
+
+# 7. 模拟当前对话
+# 假设当前对话的嵌入向量已经生成
+current_text = "我最近工作压力很大，想找个方式放松一下。"
+current_embedding = np.random.random(128).tolist()
+
+# 8. 查询与当前对话最相关的历史对话
+search_params = {
+    "metric_type": "L2",
+    "params": {"nprobe": 100}  # 增加 nprobe 的值
+}
+start_time = time.time()
+results = collection.search(
+    data=[current_embedding],  # 查询向量
+    anns_field="embedding",    # 查询字段
+    param=search_params,
+    limit=2  # 返回最相似的 2 个结果
+)
+end_time = time.time()
+
+# 9. 输出查询结果
+print("当前对话:", current_text)
+print("最相关的历史对话:")
+if results:
+    for hits in results:
+        for hit in hits:
+            text = collection.query(expr=f"id == {hit.id}", output_fields=["text"])[0]["text"]
+            print(f"- {text} (距离: {hit.distance})")
+else:
+    print("未找到相关历史对话，请检查查询参数或数据。")
+
+# 输出查询耗时
+print(f"查询耗时: {end_time - start_time:.4f} 秒")
+
+# 10. 关闭连接
+connections.disconnect("default")
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