Starting today, we embark on a brief series dedicated to vector databases. Our objective is to provide a comprehensive overview, exploring their definition, the reasons behind their emergence, and shedding light on some of their internal workings.
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In this post, we will explore the concept of vector databases and elucidate why they are highly suited and even essential in certain scenarios.
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In this post, we will explore the tf-idf algorithm and observe how it facilitates the embedding of documents and words into vectors.
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In this concluding post, we will delve into several algorithms aimed at efficiently computing similarities.
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