Putting LLMs into production is a monumental task – vector databases could light the way
Last month MongoDB announced its public preview of Vector Search among the updates to its developer platform of its Atlas database-as-a-service. The move means document database MongoDB joins Cassandra, PostgreSQL and SingleStore among the systems supporting similar features as the interest in putting large language models into production gathers pace.
Anticipating the boom in this form of analysis of text and other data, a group of vendors have developed specialist databases with architectures designed specifically for the task. The question is whether it is better to employ a database or use new features of a system already familiar to developers and enterprises, with a home marked out in the technology stack.
"It's quite straightforward to include these high-dimensional vectors inside of your documents," he said."As you look to add semantic search as a capability to your application and other new use cases around LLMs and chat bots arise, [you can] take that same data you were storing inside of your MongoDB deployment. You can embed or vectorize it, and add that vector to the individual documents and then create an index on it.
"While vector search was once a niche industry requirement, these new products only fit those niche requirements. Nowadays, as vector search has become a mainstream requirement, mainstream databases are incorporating vector search as a feature for their developers."
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