milvus/README_CN.md

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# 欢迎来到 Milvus
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## Milvus 是什么
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Milvus 是一款全球领先的开源向量数据库,赋能 AI 应用和向量相似度搜索,加速非结构化数据检索。用户在任何部署环境中均可获得始终如一的用户体验。
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Milvus 2.0 是一款云原生向量数据库,采用存储与计算分离的架构设计。该重构版本的所有组件均为无状态组件,极大地增强了系统弹性和灵活性。更多系统架构细节,参考 [Milvus 系统架构](https://milvus.io/cn/docs/v2.0.0/architecture_overview.md)。
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Milvus 基于 [Apache 2.0 License](https://github.com/milvus-io/milvus/blob/master/LICENSE) 协议发布,于 2019 年 10 月正式开源,是 [LF AI & Data 基金会](https://lfaidata.foundation/) 的毕业项目。
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## 产品亮点
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<details>
<summary><b>针对万亿级向量的毫秒级搜索</b></summary>
完成万亿条向量数据搜索的平均延迟以毫秒计。
</details>
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<details>
<summary><b>简化的非结构化数据管理</b></summary>
<li>一整套专为数据科学工作流设计的 API。</li><li>无论是笔记本、本地集群还是云服务器,始终如一的跨平台用户体验。</li><li>可以在任何场景下实现实时搜索与分析。</li>
</details>
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<details>
<summary><b>稳定可靠的用户体验</b></summary>
Milvus 具有故障转移和故障恢复的机制,即使服务中断,也能确保数据和应用的业务连续性。
</details>
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<details>
<summary><b>高度可扩展,弹性伸缩</b></summary>
组件级别的高扩展性,支持精准扩展。
</details>
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<details>
<summary><b>混合查询</b></summary>
除了向量以外Milvus还支持布尔值、整型、浮点等数据类型。在 Milvus 中,一个 collection 可以包含多个字段来代表数据特征或属性。Milvus 还支持在向量相似度检索过程中进行标量字段过滤。
</details>
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<details>
<summary><b>基于 Lambda 架构的流批一体式数据存储</b></summary>
Milvus 在存储数据时支持流处理和批处理两种方式,兼顾了流处理的时效性和批处理的效率。统一的对外接口使得向量相似度查询更为便捷。
</details>
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<details>
<summary><b>广受社区支持和业界认可</b></summary>
Milvus 项目在 GitHub 上获星超 6000拥有逾 1000 家企业用户还有活跃的开源社区。Milvus 由 <a href="https://lfaidata.foundation/">LF AI & Data 基金会</a> 背书,是该基金会的毕业项目。
</details>
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## 快速开始
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### 开始使用 Milvus
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- [Standalone Quick Start Guide](https://milvus.io/cn/docs/v2.0.0/install_standalone-docker.md)
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- [Cluster Quick Start Guide](https://milvus.io/cn/docs/v2.0.0/install_cluster-docker.md)
### 开发 Milvus
```bash
# Clone github repository.
$ git clone https://github.com/milvus-io/milvus.git
# Install third-party dependencies.
$ cd milvus/
$ ./scripts/install_deps.sh
# Compile Milvus standalone.
$ make milvus
```
获取更多内容,请阅读 [开发者文档](https://github.com/milvus-io/milvus/blob/master/DEVELOPMENT.md)
> **注意** 主分支用于 Milvus v2.0 代码开发。Milvus v1.0 于 2021 年 3 月 9 日发布,是 Milvus 的首个长期支持LTS版本。如需使用 Milvus 1.0,请切换至 [1.0 分支](https://github.com/milvus-io/milvus/tree/1.0)。
## Milvus 2.0:功能增加、性能升级
详见 [Milvus 2.0 vs. 1.x](https://github.com/milvus-io/milvus/blob/master/milvus20vs1x.md) 。
## 入门指南
### 应用场景
<table>
<tr>
<td width="30%">
<a href="https://zilliz.com/milvus-demos">
<img src="https://zilliz-cms.s3.us-west-2.amazonaws.com/image_search_59a64e4f22.gif" />
</a>
</td>
<td width="30%">
<a href="https://zilliz.com/milvus-demos">
<img src="https://zilliz-cms.s3.us-west-2.amazonaws.com/qa_df5ee7bd83.gif" />
</a>
</td>
<td width="30%">
<a href="https://zilliz.com/milvus-demos">
<img src="https://zilliz-cms.s3.us-west-2.amazonaws.com/mole_search_76f8340572.gif" />
</a>
</td>
</tr>
<tr>
<th>
<a href="https://zilliz.com/milvus-demos">以图搜图系统</a>
</th>
<th>
<a href="https://zilliz.com/milvus-demos">智能问答机器人</a>
</th>
<th>
<a href="https://zilliz.com/milvus-demos">分子式检索系统</a>
</th>
</tr>
</table>
#### 以图搜图系统
从海量图片中快速检索最相似图片。
#### 智能问答机器人
交互式智能问答机器人帮助用户节省时间和用人成本。
#### 分子式检索系统
迅速检索相似化学分子式。
## 训练营
Milvus [训练营](https://github.com/milvus-io/bootcamp)能够帮助你了解向量数据库的操作及各种应用场景。通过 Milvus 训练营探索如何进行 Milvus 性能测评,搭建智能问答机器人、推荐系统、以图搜图系统、分子式检索系统等。
## 贡献代码
欢迎向 Milvus 社区贡献你的代码。代码贡献流程或提交补丁等相关信息详见 [代码贡献准则](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md)。参考 [社区仓库](https://github.com/milvus-io/community) 了解社区管理准则并获取更多社区资源。
### All contributors
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## Milvus 文档
获取更多有关安装、开发、部署和管理的指南,请查看 [Milvus 文档](https://milvus.io/cn/docs/home).
### SDK
- [PyMilvus-ORM](https://github.com/milvus-io/pymilvus-orm)
## 社区
欢迎加入 [Slack](https://join.slack.com/t/milvusio/shared_invite/zt-e0u4qu3k-bI2GDNys3ZqX1YCJ9OM~GQ) 频道分享你的建议与问题。你也可以通过 [FAQ](https://milvus.io/cn/docs/v1.0.0/performance_faq.md) 页面,查看常见问题及解答。
订阅 Milvus 邮件:
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- [Technical Steering Committee](https://lists.lfai.foundation/g/milvus-tsc)
- [Technical Discussions](https://lists.lfai.foundation/g/milvus-technical-discuss)
- [Announcement](https://lists.lfai.foundation/g/milvus-announce)
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关注 Milvus 社交媒体:
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- [知乎](https://zilliz.atlassian.net/wiki/spaces/TC/pages/251658753/CN%2BTranslation%2BWhat%2Bis%2BMilvus#)
- [CSDN](http://zilliz.blog.csdn.net/)
- [Bilibili](http://space.bilibili.com/478166626)
- Zilliz 技术交流微信群
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<img src="https://zilliz-cms.s3.us-west-2.amazonaws.com/wechat_2abac21f5a.png" alt="Wechat QR Code">
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## 加入我们
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Zilliz 是 Milvus 项目的幕后公司。我们正在 [招聘](https://app.mokahr.com/apply/zilliz/37974#/) 算法、开发和全栈工程师。欢迎加入我们,让我们携手构建下一代的开源数据基础软件。
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## 引用
如需在研究论文中使用 Milvus欢迎添加以下引用
```
@inproceedings{2021milvus,
title={Milvus: A Purpose-Built Vector Data Management System},
author={Wang, Jianguo and Yi, Xiaomeng and Guo, Rentong and Jin, Hai and Xu, Peng and Li, Shengjun and Wang, Xiangyu and Guo, Xiangzhou and Li, Chengming and Xu, Xiaohai and others},
booktitle={Proceedings of the 2021 International Conference on Management of Data},
pages={2614--2627},
year={2021}
}
```
## 特别感谢
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Milvus 采用了以下依赖库:
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- 感谢 [FAISS](https://github.com/facebookresearch/faiss) 相似性检索库。
- 感谢开源键值存储 [etcd](https://github.com/coreos/etcd)。
- 感谢分布式信息发布/订阅平台 [Pulsar](https://github.com/apache/pulsar)。
- 感谢存储引擎 [RocksDB](https://github.com/facebook/rocksdb)。
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