Milvus 安装
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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
使用 Helm Charts 离线安装 Milvus
下载资源
- 在本地添加和更新 Milvus Helm 存储库。
helm repo add milvus https://milvus-io.github.io/milvus-helm/ helm repo update
- 独立部署
helm template my-release --set cluster.enabled=false --set etcd.replicaCount=1 --set minio.mode=standalone --set pulsar.enabled=false milvus/milvus > milvus_manifest.yaml
- 集群部署
helm template my-release milvus/milvus > milvus_manifest.yaml
- 自定义
wget https://raw.githubusercontent.com/milvus-io/milvus-helm/master/charts/milvus/values.yaml # 修改 helm template -f values.yaml my-release milvus/milvus > milvus_manifest.yaml
- 下载需求和脚本文件
wget https://raw.githubusercontent.com/milvus-io/milvus/master/deployments/offline/requirements.txt wget https://raw.githubusercontent.com/milvus-io/milvus/master/deployments/offline/save_image.py
- 拉取并保存镜像
pip3 install -r requirements.txt python3 save_image.py --manifest milvus_manifest.yaml # 需要安装 pyyaml, docker
- 加载镜像
cd images/for image in $(find . -type f -name "*.tar.gz") ; do gunzip -c $image | docker load --input; done
Helm Inst
kubectl apply -f milvus_manifest.yaml # kubectl delete -f milvus_manifest.yaml
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()