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61 lines
1.9 KiB
61 lines
1.9 KiB
# D:\anaconda3\envs\py310\Scripts\pip.exe install pymilvus
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from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
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# 1. 连接 Milvus
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connections.connect("default", host="10.10.14.101", port="19530")
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# 2. 定义集合的字段和模式
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fields = [
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FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
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FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128)
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]
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schema = CollectionSchema(fields, description="Simple demo collection")
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# 3. 创建集合
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collection_name = "demo_collection"
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if utility.has_collection(collection_name):
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utility.drop_collection(collection_name) # 如果集合已存在，先删除
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collection = Collection(name=collection_name, schema=schema)
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# 4. 插入数据
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import random
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data = [
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[random.random() for _ in range(128)], # 第一个向量
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[random.random() for _ in range(128)], # 第二个向量
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[random.random() for _ in range(128)] # 第三个向量
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]
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entities = [data] # 插入的数据
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collection.insert(entities)
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# 5. 创建索引
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index_params = {
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"index_type": "IVF_FLAT", # 索引类型
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"metric_type": "L2", # 距离度量方式
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"params": {"nlist": 128} # 索引参数
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}
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collection.create_index("embedding", index_params)
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# 6. 加载集合到内存
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collection.load()
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# 7. 查询数据
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query_vector = [random.random() for _ in range(128)] # 查询向量
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search_params = {
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"metric_type": "L2",
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"params": {"nprobe": 10} # 查询参数
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}
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results = collection.search(
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data=[query_vector], # 查询向量
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anns_field="embedding", # 查询字段
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param=search_params,
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limit=2 # 返回最相似的 2 个结果
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)
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# 8. 输出查询结果
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for hits in results:
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for hit in hits:
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print(f"ID: {hit.id}, Distance: {hit.distance}")
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# 9. 关闭连接
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connections.disconnect("default") |