Co-authored with Kenny Gorman, A heartfelt thank you to Nicolas Benhamou for his generous technical support and encouragement
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Over the last decade, we have significantly shifted towards real-time, data-centric applications. Whether in e-commerce recommendations, fraud detection, or IoT event analysis, users now expect immediate, contextually relevant responses powered by continuous data streams. MongoDB’s flexible document model and real-time capabilities make it a strong fit for these dynamic workloads, especially when paired with streaming solutions like Kafka.
This need for real-time AI is particularly relevant when generating on-the-fly vector embeddings, which are crucial for powering applications like semantic search, personalized recommendations, and generative AI assistants. Stream processing solutions allow embeddings to be updated as new data arrives, ensuring AI-driven insights remain fresh and accurate.
In this article, we’ll explore how MongoDB Stream Processing can power real-time embedding generation for AI applications, ensuring that models stay current with the latest product, user, and content updates.
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