Author: Francisco Morillo

We’re introducing a real-time vector embedding blueprint, which simplifies building real-time AI applications by automatically generating vector embeddings using Amazon Bedrock from streaming data in Amazon Managed Streaming for Apache Kafka (Amazon MSK) and indexing them in Amazon OpenSearch Service. In this post, we discuss the importance of real-time data for generative AI applications, typical architectural patterns for building Retrieval Augmented Generation (RAG) capabilities, and how to use real-time vector embedding blueprints for Amazon MSK to simplify your RAG architecture.

Apache Flink is an open source distributed processing engine, offering powerful programming interfaces for both stream and batch processing, with first-class support for stateful processing and event time semantics. Apache Flink supports multiple programming languages, Java, Python, Scala, SQL, and multiple APIs with different level of abstraction, which can be used interchangeably in the same […]

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.

Thousands of developers use Apache Flink to build streaming applications to transform and analyze data in real time. Apache Flink is an open source framework and engine for processing data streams. It’s highly available and scalable, delivering high throughput and low latency for the most demanding stream-processing applications. Monitoring and scaling your applications is critical […]