The Trade-Offs of Vector Databases for AI

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Prashant: I'm both fascinated and frustrated by the current state of marketing in vector databases. When combined with LLM's, large language models like chat GPT, you could say any sort of language model layered on top of a vector database can be used to build some very, very interesting applications. One of those interesting applications is querying your data by a natural language. Rather than writing my query by hand or constructing the query being taken from the ground up, can I just talk in natural language and have the database kind of respond to that query as well? Prashant: It makes sense to talk about the use cases once we go through some of these key trade-offs

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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