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向量数据库人工智能时代的创新数据管理.pptx

上传人: 王** 编号:171101 2024-07-23 20页 17.24MB

1、Vector Databases:Innovating Data Management in the AI EraFill out the session evaluation and enter 325 years in ITPast:Software Developer,DBA,TPM,Cloud ArchitectCurrently Global Practice Leader at Datavail(Lead Cloud Migration&Modernizations,Site Reliability Engineering,and NoSQL practices)10+years

2、of experience with CloudMMichaelAgarwalDirector&Global Practice Leader(SRE,Cloud&NoSQL Databases)DatavailConnect or follow me on LinkedIn:https:/ at a GlanceDelivering a superior approach to leveraging data through application of a tech-enabled global delivery model&deep specialization in databases,

3、data management,and application services.$25MInvestedin IP that improves the service experience and drives efficiency15+Yearsbuilding and operating mission critical data and application systems1,200+E5Datavails Superpowers.Net App Modernization Application Modernization,Strategy&Implementation Cloud

4、 Native Development(Serverless/Containers/Kubernetes)DevOps Strategy and Implementation Sprint Based App Dev as a Service for Cloud Native Apps Managed ServicesAnalytics Data Estate Modernization Analytics Roadmaps Data Integrations Visualization CoE Data Governance/MDM Data Architectures-Business O

5、utcomes Analytics Managed Services AI/MLDatabases Data Estate Strategy/Planning Database Migrations to Cloud DB Modernization Strategy and Implementation Database Upgrades and Performance Tuning High Availability and Disaster Recovery Database Managed ServicesOracle Workloads Legacy Migrations to Cl

6、oud:EBS,JDE,PeopleSoft,Hyperion,Essbase,Weblogic Oracle Database and Oracle Tech Stack Migrations&Modernizations AWS Architecture&Performance Tuning Oracle Application and Database Managed ServicesCloud PlatformsAgenda1.What are LLMs and RAG?2.What are Vector Databases and Why is an Efficient Vector

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本文主要介绍了在AI时代,向量数据库如何革新数据管理,以及向量搜索功能的重要性。作者Michael Agarwal是Datavail的导演和全球实践领导者,拥有超过25年的IT经验,专注于云迁移和现代化、网站可靠性工程和NoSQL实践。文章讨论了向量数据库的概念,它们是高维空间的数学表示,用于计算机处理搜索和检索任务。向量搜索是基于相似性的搜索,使用kNN算法和近似最近邻(ANN)算法,对于存储数百万或数十亿向量的数据库至关重要。文章还提到了MySQL、MariaDB和PostgreSQL等数据库中向量搜索的功能,以及Pinecone和Weaviate等新型向量数据库。最后,作者以Azure Search为例,展示了一个业务文档的向量搜索案例。
请问您能详细介绍一下Vector Databases在AI时代的数据管理中的作用吗? 在MySQL和MongoDB中,Vector Search是如何实现和应用的?能否给出具体的例子? 您能否讲解一下如何使用Azure AI服务来处理和分析业务文档,以及这个过程可能遇到的挑战和解决方案?
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