I primarily use Amazon OpenSearch Service for log management and data storage. It's used to store third-party data and manage large volumes of query data across various services, including AWS Lambda and Kubernetes.
I use it for database. For RAG, you need a vector store to store embeddings. To store the vectors, you need embedding models to convert the data into vectors. You then need to store those vectors in any vector store. Popular ones are like Chroma DB. As a new alternative, I selected OpenSearch, which falls under the whole AWS infrastructure. So to bring our full architecture into AWS, I use OpenSearch as a service as my vector store.
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Application Performance Monitoring (APM) and Observability help improve the efficiency of applications by providing visibility and insights into system performance.
Application Performance Monitoring and Observability involve tracking and analyzing the performance of applications and infrastructure. APM focuses on detecting and diagnosing performance issues, while Observability emphasizes gaining insight into the internal state of systems. By combining these approaches, IT teams can ensure...
I primarily use Amazon OpenSearch Service for log management and data storage. It's used to store third-party data and manage large volumes of query data across various services, including AWS Lambda and Kubernetes.
I use it for database. For RAG, you need a vector store to store embeddings. To store the vectors, you need embedding models to convert the data into vectors. You then need to store those vectors in any vector store. Popular ones are like Chroma DB. As a new alternative, I selected OpenSearch, which falls under the whole AWS infrastructure. So to bring our full architecture into AWS, I use OpenSearch as a service as my vector store.
We use the solution as a login platform. We have a lot of microservices, and we get log records from there, which we host on Amazon OpenSearch.