轻量级milvus安装和应用示例-CSDN博客

轻量级milvus适合需要向量库,但不方便使用k8s或docker,有将milvus集成到python中的场景。

1 安装milvus

pip install milvus -i https://pypi.tuna.tsinghua.edu.cn/simple

2 启动milvus

1)终端启动

# 终端启动
milvus-server
# 后台启动
nohup milvus-server > run.log &

2)在python模块中启动

from milvus import default_server
from pymilvus import connections, utility
# Start your milvus server

default_server.start()
# Now you can connect with localhost and the given port
# Port is defined by default_server.listen_portconnections.connect(host='127.0.0.1', port=default_server.listen_port)# Check if the server is ready.print(utility.get_server_version())
# Stop your milvus server

default_server.stop()

3 测试milvus

走完上述步骤后,就可以测试和正常使用milvus了。

具体操作参考milvus client操作简单示例-CSDN博客

1)测试服务启动和连接

from milvus import default_server
from pymilvus import connections

# 配置数据存储路径(避免使用临时目录)
default_server.set_base_dir("milvus_data")  

# 启动服务(默认端口19530)
default_server.start()  

# 连接客户端
connections.connect("default", host="127.0.0.1", port=default_server.listen_port)

2)定义client和collection

from pymilvus import MilvusClient, DataType

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

3)插入数据

data = [
    {"id": i, "vector": vectors[i], "text": docs[i], "subject": "history"}
    for i in range(len(vectors))

res = client.insert(collection_name="demo_collection", data=data)

print(res)

4)执行检索

# 执行搜索

query_vectors = [xxx] # 这里query向量已计算好,采用和准备数据是一样模型参数计算
res = client.search(
    collection_name="demo_collection",  # target collection
    data=query_vectors,  # query vectors
    limit=2,  # number of returned entities
    output_fields=["text", "subject"],  # specifies fields to be returned
)
print(res)

reference

---

基于Milvus Lite的轻量级向量数据库实战指南

https://juejin.cn/post/7528313969151033390

Milvus Lite 已交卷!轻量版 Milvus,主打就是一个轻便、无负担

https://zhuanlan.zhihu.com/p/635690303

milvus client操作简单示例

https://blog.csdn.net/liliang199/article/details/149904178


原网址: 访问
创建于: 2025-08-27 22:20:45
目录: default
标签: 无

请先后发表评论
  • 最新评论
  • 总共0条评论