The Importance of Optimizing Your Database for Indexing Speed

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The goal of indexing is to design efficient data structures and store the vectors in a way that they can be queried efficiently and at scale. The query stage involves you taking the user input, transforming that into a vector just like you did your raw data. Depending on the size of your data, this could take hours or even days. It's not unheard of through fear of indexing periods of the order of days.

There’s so much talk (and hype) these days about vector databases. We thought it would be timely and practical to have someone on the show that has been hands on with the various options and actually tried to build applications leveraging vector search. Prashanth Rao is a real practitioner that has spent and huge amount of time exploring the expanding set of vector database offerings. After introducing vector database and giving us a mental model of how they fit in with other datastores, Prashanth digs into the trade offs as related to indices, hosting options, embedding vs. query optimization, and more.

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