AWS Machine Learning Blog
In this post, we dive deep into the vector database options available as part of Amazon Bedrock Knowledge Bases and the applicable use cases, and look at working code examples. Clean up When you’re done with this solution, clean up the resources you created: Amazon Bedrock knowledge bases for OpenSearch Serverless and Aurora OpenSearch Serverless collection Aurora DB instance S3 bucket SageMaker Studio domain Amazon Bedrock service role SageMaker Studio domain role Conclusion In this post, we provided a high-level introduction to generative AI use cases and the use of RAG workflows to augment your organization’s internal or external knowledge stores. We discussed the importance of vector databases and RAG architectures to enable similarity search and why dense vector representations are beneficial. We also went over Amazon Bedrock Knowledge Bases, which provides common APIs, industry-leading governance, observability, and security to enable vector databases using different options like AWS native and partner products through AWS Marketplace. We also dived deep into a few of the vector database options with code examples to explain the implementation steps. Try out the code examples in this post to implement your own RAG solution using Amazon Bedrock Knowledge Bases, and share your feedback and questions in the comments section. About the Authors Vishwa Gupta is a Senior Data Architect with AWS Professional Services. He helps customers implement generative AI, machine learning, and analytics solutions. Outside of work, he enjoys spending time with family, traveling, and trying new foods. Isaac Privitera is a Principal Data Scientist with the AWS Generative AI Innovation Center, where he develops bespoke generative AI-based solutions to address customers’ business problems. His primary focus lies in building responsible AI systems, using techniques such as RAG, multi-agent systems, and model fine-tuning. When not immersed in the world of AI, Isaac can be found on the golf course, enjoying a football game, or hiking trails with his loyal canine companion, Barry. Abhishek Madan is a Senior GenAI Strategist with the AWS Generative AI Innovation Center. He helps internal teams and customers in scaling generative AI, machine learning, and analytics solutions. Outside of work, he enjoys playing adventure sports and spending time with family. Ginni Malik is a Senior Data & ML Engineer with AWS Professional Services. She assists customers by architecting enterprise data lake and ML solutions to scale their data analytics in the cloud. Satish Sarapuri is a Sr. Data Architect, Data Lake at AWS. He helps enterprise-level customers build high-performance, highly available, cost-effective, resilient, and secure generative AI, data mesh, data lake, and analytics platform solutions on AWS through which customers can make data-driven decisions to gain impactful outcomes for their business, and helps them on their digital and data transformation journey. In his spare time, he enjoys spending time with his family and playing tennis.
Published: 2024-10-11T19:07:32
