高性能云原生向量数据库Milvus-源码站

项目概述

Milvus是一款高性能的云原生向量数据库，专为可扩展的近似最近邻搜索而设计，能够高效处理大规模向量数据，广泛应用于图像搜索和相似度检索等场景。

项目地址

https://github.com/milvus-io/milvus

项目页面预览

milvus-io/milvus preview

关键指标

Stars：42252
主要语言：Go
License：Apache License 2.0
最近更新：2026-01-15T13:04:35Z
默认分支：master

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安装部署要点（README 精选）

Quickstart

$ pip install -U pymilvus

This installs pymilvus, the Python SDK for Milvus. Use MilvusClient to create a client:

from pymilvus import MilvusClient

You can also try Milvus Lite for quickstart by installing pymilvus[milvus-lite]. To create a local vector database, simply instantiate a client with a local file name for persisting data:

python client = MilvusClient("milvus_demo.db")

You can also specify the credentials to connect to your deployed Milvus server or Zilliz Cloud:

python client = MilvusClient( uri="<endpoint_of_self_hosted_milvus_or_zilliz_cloud>", token="<username_and_password_or_zilliz_cloud_api_key>")

With the client, you can create collection:

client.create_collection(
    collection_name="demo_collection",
    dimension=768,  # The vectors we will use in this demo have 768 dimensions
)

Ingest data:

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

Perform vector search:

query_vectors = embedding_fn.encode_queries(["Who is Alan Turing?", "What is AI?"])
res = client.search(
    collection_name="demo_collection",  # target collection
    data=query_vectors,  # a list of one or more query vectors, supports batch
    limit=2,  # how many results to return (topK)
    output_fields=["vector", "text", "subject"],  # what fields to return
)

常用命令（从 README 提取）

Go: >= 1.21
  CMake: >= 3.26.4 && CMake < 4
  GCC: 9.5
  Python: > 3.8 and  <= 3.11

Go: >= 1.21
  CMake: >= 3.26.4 && CMake < 4
  llvm: >= 15
  Python: > 3.8 and  <= 3.11

Go: >= 1.21 (Arch=ARM64)
  CMake: >= 3.26.4 && CMake < 4
  llvm: >= 15
  Python: > 3.8 and  <= 3.11