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https://aws.amazon.com/blogs/database/accelerate-generative-ai-workloads-on-amazon-aurora-with-optimized-reads-and-pgvector

Improve the performance of generative AI workloads on Amazon Aurora with Optimized Reads and pgvector | Amazon Web Services

Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in […]



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Improve the performance of generative AI workloads on Amazon Aurora with Optimized Reads and pgvector | Amazon Web Services

https://aws.amazon.com/blogs/database/accelerate-generative-ai-workloads-on-amazon-aurora-with-optimized-reads-and-pgvector

Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in […]



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https://aws.amazon.com/blogs/database/accelerate-generative-ai-workloads-on-amazon-aurora-with-optimized-reads-and-pgvector

Improve the performance of generative AI workloads on Amazon Aurora with Optimized Reads and pgvector | Amazon Web Services

Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in […]

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      Generative AI has increased the possibilities for businesses to build applications that require searching and comparison of unstructured data types such as text, images, and video. Embeddings, or vectors, capture the meaning and context of this unstructured data in a machine-readable form, which is the basis for how similarity comparisons can be made directly in […]
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