Vector Embeddings

Vector embeddings are numerical representations of data (such as words, images, or other objects) in the form of vectors in a continuous, high-dimensional space. These embeddings capture the semantic meaning or features of the data, enabling machines to “understand” relationships, and similarities and differences between different items. In the context of machine learning and natural language processing, vector embeddings help transform complex data into a format that models can process more efficiently for tasks such as search, recommendation systems, and clustering. 

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Related terms: generative AI

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On the Onehouse website:

• Vector Embeddings in the Lakehouse: Bridging AI and Data Lake Technologies
• Managing AI Vector Embeddings with Onehouse
• Onehouse Infrastructure Solution: Managing Vector Embeddings for GenAI on the Lakehouse