Generative AI (GenAI) enables advanced AI use cases and innovation but also changes how the enterprise architecture looks like. Large Language Models (LLM), Vector Databases, and Retrieval Augmentation Generation (RAG) require new data integration patterns and data engineering best practices. Data streaming with Apache Kafka and Apache Flink play a key role to ingest and curate incoming data sets in real-time at scale, connecting various databases and analytics platforms, and decouple independent business units and data products. This blog post explores possible architectures, examples, and trade-offs between event streaming and traditional request-response APIs and databases.

(Originally posted on Kai Waehner’s blog: “Apache Kafka + Vector Database + LLM = Real-Time GenAI”… Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter)

Use Cases for Apache Kafka and GenAI

Generative AI (GenAI) is the next-generation AI engine for natural language processing (NLP), image generation, code optimization, and other tasks. It helps many projects in the real world for service desk automation, customer conversation with a chatbot, content moderation in social networks, and many other use cases.