Vector Databases: Unlocking New Dimensions in Similarity Search

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Explore how vector embeddings and LLMs transform search capabilities in streaming platforms and enhance user discovery and personalization.

Let’s Start with the Data Story…. At the core of every software application, from the simplest to the most complex, operating at scale to serve millions of users with low-latency requests, lies a foundational element: data. For over three decades, relational database management systems have been at the forefront of this domain.

Below, you can see the different types of files, which will be read and processed by many types of AI models and create vector embeddings. Example in Action Consider the process of transforming a comprehensive movie database, such as IMDB, into a format where each movie is represented by vector embeddings and stored in a vector database. This transformation allows the database to leverage the power of vector embeddings to significantly enhance the user search experience.

 

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