Milvus 安装

Milvus is an open-source vector database that brings search to GenAI applications.

Milvus was selected as the vector database of choice (over Chroma and Pinecone). Milvus is an open-source vector database designed specifically for similarity search on massive datasets of high-dimensional vectors.

Milvus supports Python, Java, C++.

Milvus 2

HELP: Run Milvus in Docker

• Preparations

1. Milvus 2.0.0
2. Python 3 (3.7.1 or later)
3. PyMilvus 2.0.0

Docker Inst

• 拉取并保存镜像

docker pull milvusdb/milvus:v2.4.5

• 下载脚本文件

wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh
bash standalone_embed.sh start | stop | delete

• 路径

在 standalone_embed.sh 目录下创建 volumes 目录，存放数据文件。

# docker ps 
0cc9050d3dc4   milvusdb/milvus:v2.4.5               "/tini -- milvus run…"   4 minutes ago   Up 4 minutes (healthy)   0.0.0.0:2379->2379/tcp, :::2379->2379/tcp, 0.0.0.0:9091->9091/tcp, :::9091->9091/tcp, 0.0.0.0:19530->19530/tcp, :::19530->19530/tcp   milvus-standalone

# docker logs 0cc9050d3dc4
[2024/08/15 07:48:30.255 +00:00] [INFO] [distance/calc_distance_amd64.go:14] ["Hook avx for go simd distance computation"]
2024/08/15 07:48:30 maxprocs: Leaving GOMAXPROCS=6: CPU quota undefined

    __  _________ _   ____  ______    
   /  |/  /  _/ /| | / / / / / __/    
  / /|_/ // // /_| |/ / /_/ /\ \    
 /_/  /_/___/____/___/\____/___/     

Welcome to use Milvus!
Version:   v2.4.5
Built:     Wed Jun 19 02:47:56 UTC 2024
GitCommit: 60695bdb
GoVersion: go version go1.20.7 linux/amd64

TotalMem: 12537851904
UsedMem: 26251264

open pid file: /run/milvus/standalone.pid
lock pid file: /run/milvus/standalone.pid

V2 Sample

Run Milvus using Python

Milvus 1

Docker Inst

docker pull milvusdb/milvus:cpu-latest

# 设置配置文件和工作目录
/u01/milvus/conf
  : conf      # server_config.yaml
  : db        # 索引与向量存储
  : logs      # 日志
  : wal       # 预写式日志

docker run -td --name mymilvus -e "TZ=Asia/Shanghai" -p 19530:19530 -p 19121:19121 \
 -v /u01/milvus/db:/var/lib/milvus/db \
 -v /u01/milvus/wal:/var/lib/milvus/wal \
 -v /u01/milvus/logs:/var/lib/milvus/logs \
 -v /u01/milvus/conf:/var/lib/milvus/conf \
 milvusdb/milvus:cpu-latest

# docker ps
CONTAINER ID   IMAGE                        COMMAND                  CREATED         STATUS          PORTS                                                                                          NAMES
f8e350986ef4   milvusdb/milvus:cpu-latest   "/tini -- /var/lib/m…"   4 seconds ago   Up 3 seconds   0.0.0.0:19121->19121/tcp, :::19121->19121/tcp,  0.0.0.0:19530->19530/tcp, :::19530->19530/tcp   milvusdev
322f00c39f82   registry                     "/entrypoint.sh /etc…"   2 weeks ago     Up 13 days     0.0.0.0:8000->5000/tcp, :::8000->5000/ tcp                                                      uhry

# docker logs f8e350986ef4
    __  _________ _   ____  ______    
   /  |/  /  _/ /| | / / / / / __/    
  / /|_/ // // /_| |/ / /_/ /\ \    
 /_/  /_/___/____/___/\____/___/     

Welcome to use Milvus!
Milvus Release version: v1.1.1, built at 2021-06-15 14:51.05, with OpenBLAS library.
You are using Milvus CPU edition
Last commit id: 330cc61bede475c4a7a71841d54e633586cea829

Loading configuration from: /var/lib/milvus/conf/server_config.yaml
NOTICE: You are using SQLite as the meta data management. We recommend change it to MySQL.
Supported CPU instruction sets: avx2, sse4_2
FAISS hook AVX2
Milvus server started successfully!

V1 Sample

milvus 版本 1.x 与 2.0 不兼容，pymilvus 也如此，且低版本的 pymilvus 不可安装在 python 高版本上，如 11。下面使用 pymilvus 1.1.0，安装在 python3.6 上。

# -*- coding: utf-8 -*-
import numpy as np
from milvus import Milvus, MetricType

HOST='192.168.0.242'
PORT=19530

milvus = Milvus(host=HOST, port=PORT)

# create table
num_vec = 5000
vec_dim = 768
collection_name = "demo1"
collection_param = {
'collection_name': collection_name,
'dimension': vec_dim,
'index_file_size': 32,
'metric_type': MetricType.IP
}
milvus.create_collection(collection_param)

# Generate random data
vectors_array = np.random.rand(num_vec, vec_dim)

# Insert DB
status, ids = milvus.insert(collection_name=collection_name, records=vectors_array)   # 返回状态和这一组向量的ID
milvus.flush([collection_name])

print(milvus.get_collection_stats(collection_name))

# QUERY
query_vec_array = np.random.rand(1, vec_dim)
status, results = milvus.search(collection_name=collection_name, query_records=query_vec_array, top_k=5)
print(status)
print(results)

# Drop Table
# status = milvus.drop_collection(collection_name)

milvus.close()