Author: DATAVERSITY via YouTube
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Vector databases are a type of database that use graph embeddings to represent and compare data, making them ideal for fuzzy match problems. Graph embeddings are created using machine learning algorithms and compress the attributes of data into a low-level representation. The process of creating a new embedding vector is called encoding.

Download slides here: https://www.dataversity.net/adv-webinar-2024-what-the-another-database-model-vector-databases-explained/

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