企业网站用什么建站最快,建筑网站推荐知乎,vi系统整套设计,用wordpress建一个网站吗特性 Milvus由Go(63.4%),Python(17.0%),C(16.6%),Shell(1.3%)等语言开发开发#xff0c;支持python#xff0c;go#xff0c;java接口(C,Rust,c#等语言还在开发中)#xff0c;支持单机、集群部署#xff0c;支持CPU、GPU运算。Milvus 中的所有搜索和查询操作都在内存中执行…特性 Milvus由Go(63.4%),Python(17.0%),C(16.6%),Shell(1.3%)等语言开发开发支持pythongojava接口(C,Rust,c#等语言还在开发中)支持单机、集群部署支持CPU、GPU运算。Milvus 中的所有搜索和查询操作都在内存中执行。,当前支持的Dimensions of a vector的最大值为32,768。其他限制。
使用步骤 M i l v u s 和之前讨论的 f a i s s , u s e a r c h 的不同在于使用前需要先安装服务端的 M i l v u s 否则会有以下错误 \color{red} Milvus和之前讨论的faiss,usearch的不同在于使用前需要先安装服务端的Milvus否则会有以下错误 Milvus和之前讨论的faiss,usearch的不同在于使用前需要先安装服务端的Milvus否则会有以下错误pymilvus.exceptions.MilvusException: MilvusException: (code2, messageFail connecting to server on 127.0.0.1:19530. Timeout)
安装Milvus根据你的操作系统和需求选择适合的安装方式可以是Docker容器、二进制文件或源代码编译安装。 dokcer
curl -fsSL https://get.docker.com | bash -s docker --mirror Aliyunsudo chmod x ./docker-compose-linux-x86_64sudo cp ./docker-compose-linux-x86_64 /usr/bin/docker-composedocker-compose versionlink Docker的快速使用 docker 中使用gpu Docker Compose: 集合管理Docker的工具安装
安装
wget https://github.com/milvus-io/milvus/releases/download/v2.3.4/milvus-standalone-docker-compose.yml -O docker-compose.yml sudo docker compose up -d
$ sudo docker compose up -d
[] Running 23/23✔ standalone 7 layers [⣿⣿⣿⣿⣿⣿⣿] 0B/0B Pulled 13.8s ✔ d5fd17ec1767 Pull complete 2.4s ✔ 7ab813dbf013 Pull complete 2.6s ✔ 971f9356e3f1 Pull complete 4.1s ✔ 278f4560205e Pull complete 4.2s ✔ b83f734869d9 Pull complete 10.0s ✔ 1f27396f6efc Pull complete 10.1s ✔ fe556ec02776 Pull complete 10.1s ✔ etcd 7 layers [⣿⣿⣿⣿⣿⣿⣿] 0B/0B Pulled 15.8s ✔ dbba69284b27 Pull complete 10.6s ✔ 270b322b3c62 Pull complete 10.7s ✔ 7c21e2da1038 Pull complete 10.8s ✔ cb4f77bfee6c Pull complete 10.8s ✔ e5485096ca5d Pull complete 10.8s ✔ 3ea3736f61e1 Pull complete 10.9s ✔ 1e815a2c4f55 Pull complete 10.9s ✔ minio 6 layers [⣿⣿⣿⣿⣿⣿] 0B/0B Pulled 14.1s ✔ c7e856e03741 Pull complete 6.6s ✔ c1ff217ec952 Pull complete 6.6s ✔ b12cc8972a67 Pull complete 6.6s ✔ 4324e307ea00 Pull complete 6.9s ✔ 152089595ebc Pull complete 6.9s ✔ 05f217fb8612 Pull complete 10.3s
[] Building 0.0s (0/0)
[] Running 4/4✔ Network milvus Created 0.1s ✔ Container milvus-minio Started 4.6s ✔ Container milvus-etcd Started 3.5s ✔ Container milvus-standalone Started $ sudo docker compose ps
NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
milvus-etcd quay.io/coreos/etcd:v3.5.5 etcd -advertise-cli… etcd About a minute ago Up About a minute (healthy) 2379-2380/tcp
milvus-minio minio/minio:RELEASE.2023-03-20T20-16-18Z /usr/bin/docker-ent… minio About a minute ago Up About a minute (healthy) 0.0.0.0:9000-9001-9000-9001/tcp, :::9000-9001-9000-9001/tcp
milvus-standalone milvusdb/milvus:v2.3.4 /tini -- milvus run… standalone About a minute ago Up About a minute (healthy) 0.0.0.0:9091-9091/tcp, :::9091-9091/tcp, 0.0.0.0:19530-19530/tcp, :::19530-19530/tcp测试链接
docker port milvus-standalone 19530/tcp // docker port 命令用于查看正在运行的容器中某个端口的映射情况
$ sudo docker port milvus-standalone 19530/tcp
0.0.0.0:19530
[::]:19530停止 Milvus服务 要停止 Milvus 单机版请运行 sudo docker compose down 如需在停止 Milvus 后删除数据请执行以下命令 sudo rm -rf volumes
客户端使用
安装
$ pip3 install pymilvus # https://github.com/milvus-io/pymilvus使用
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility# --------------------------------------------------------------------------------------------
# 服务器地址信息
HOST 127.0.0.1
PORT 19530
# 向量信息
DIM 200 # dimension
COLLECTION_NAME test
# --------------------------------------------------------------------------------------------
# 创建 Milvus 集合可参考https://milvus.io/docs/create_collection.md
def create_milvus_collection(collection_name, dim):# 是否已存在同名集合if utility.has_collection(collection_name):utility.drop_collection(collection_name)# 如果存在则删除已有集合# 定义集合的字段信息。注为了降低数据插入的复杂度Milvus 允许你为每个标量字段指定一个默认值不包括主键字段fields [FieldSchema(namepath, dtypeDataType.VARCHAR, description图像路径, max_length500, is_primaryTrue, auto_idFalse),# 存储图像路径的 path 字段FieldSchema(nameembedding, dtypeDataType.FLOAT_VECTOR, description图像嵌入向量, dimdim)# 存储图像嵌入向量的 embedding 字段]# 创建集合的模式schema CollectionSchema(fieldsfields, description集合描述信息)# 使用架构创建集合到这一步创建的集合就能使用了collection Collection(namecollection_name, schemaschema)# 定义用于创建索引的参数以下示例构建一个 10 聚类IVF_FLAT索引其中欧几里得距离 L2 作为相似度指标index_params {metric_type:L2,index_type:IVF_FLAT,params:{nlist:10}}# 在 embedding 字段上使用指定参数创建索引collection.create_index(field_nameembedding, index_paramsindex_params)# 返回创建好的集合对象return collection# 发起连接
connections.connect(hostHOST, portPORT)# 创建 collection
collection create_milvus_collection(COLLECTION_NAME, DIM)
print(fA new collection created: {COLLECTION_NAME})
# 或者直接连接已有collection collection Collection(book)import random
data [ [str(i) for i in range(2000)], [[random.random() for _ in range(200)] for _ in range(2000)], # None,]
print(len(data))
mr collection.insert(data)search_params {metric_type: L2, offset: 0, ignore_growing: False, params: {nprobe: 10}
}collection.load()
results collection.search(data[[random.random() for _ in range(200)]], anns_fieldembedding, # Name of the field to search on.paramsearch_params,limit10,exprNone,# 用于筛选属性的布尔表达式。有关更多信息请参见布尔表达式规则。https://milvus.io/docs/boolean.mdoutput_fields[embedding],#要返回的字段的名称。Milvus 支持返回向量字段。可选 # consistency_levelStrong # 搜索的一致性级别可选
)print(results[0].ids)
print(results[0].distances)
hit results[0][0]
print(hit.entity.get(embedding)) # 需要指定output_fields# [537, 1228, 389, 1527, 395, 190, 1221, 555, 1789, 886]
# [25.513811111450195, 26.030805587768555, 26.122865676879883, 26.59450912475586, 26.952003479003906, 27.123659133911133, 27.264328002929688, 27.28336524963379, 27.417621612548828, 27.71729278564453]
# [0.15461023, 0.30096045, 0.26865703, 0.25927073, 0.33812553, 0.54217076, 0.15246719, 0.731632, 0.45709008, 0.79914236, 0.9088526, 0.02686498, 0.42263803, 0.69333476, 0.39840952, 0.6991515, 0.5305877, 0.6620755, 0.5817265, 0.21614578, 0.8906462, 0.64077824, 0.09763326, 0.8131759, 0.31869066, 0.7435266, 0.727443, 0.6023419, 0.665456, 0.3228657, 0.10494679, 0.7091096, 0.3667962, 0.3149366, 0.15853179, 0.24909244, 0.23726037, 0.17990382, 0.3514512, 0.116617575, 0.5656539, 0.36453706, 0.7430549, 0.5163423, 0.17115992, 0.3062062, 0.9076736, 0.5650338, 0.43389124, 0.6029854, 0.3382137, 0.38251325, 0.7953752, 0.19413383, 0.21625121, 0.04543528, 0.97489053, 0.76131046, 0.17360009, 0.32513952, 0.7822587, 0.99820197, 0.97119784, 0.11839666, 0.004737074, 0.18586244, 0.21051529, 0.5463567, 0.28732273, 0.59985745, 0.35132825, 0.17821868, 0.08039577, 0.22121702, 0.51074564, 0.9789643, 0.91906327, 0.3212936, 0.9785981, 0.70479745, 0.77640325, 0.03191031, 0.12803258, 0.8522966, 0.48946765, 0.8437068, 0.17805281, 0.3471558, 0.7912329, 0.19458486, 0.9588124, 0.5400154, 0.3107983, 0.08004966, 0.40348408, 0.8400167, 0.255088, 0.29406822, 0.69000036, 0.7577903, 0.6970145, 0.99666446, 0.5368813, 0.25070563, 0.10906121, 0.6366669, 0.75897807, 0.2470287, 0.83007634, 0.17270081, 0.37081972, 0.5600866, 0.47211888, 0.48388532, 0.09467795, 0.43837216, 0.3848784, 0.33862317, 0.5992313, 0.49879825, 0.21382369, 0.4665225, 0.20776376, 0.41195828, 0.77341104, 0.41533098, 0.1488313, 0.29170626, 0.90135145, 0.9490258, 0.5797127, 0.046041798, 0.032213394, 0.9823944, 0.22410004, 0.01474563, 0.54565424, 0.84022516, 0.3146623, 0.60868996, 0.8468924, 0.5047047, 0.44784358, 0.76461, 0.39477462, 0.4341565, 0.04060842, 0.7913311, 0.3800782, 0.76624304, 0.27977547, 0.5467395, 0.7406536, 0.051075574, 0.859247, 0.16734485, 0.55351096, 0.77330744, 0.21997604, 0.6573193, 0.47392654, 0.22703278, 0.21453229, 0.5354482, 0.68723947, 0.3444063, 0.19725236, 0.63618726, 0.20056139, 0.41761643, 0.3148263, 0.0072599854, 0.14207017, 0.96439177, 0.727712, 0.61615413, 0.67021996, 0.73491627, 0.64917046, 0.6545984, 0.6521858, 0.86778504, 0.65002567, 0.65721965, 0.57199746, 0.27476418, 0.5959397, 0.17169125, 0.30866027, 0.6539025, 0.83966345, 0.18539791, 0.64870465, 0.9470506, 0.6794907, 0.75711423, 0.88191146, 0.075844504, 0.9600152, 0.38191438]相关项目
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osschat
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