Putting LLMs Into Production Is A Monumental Task - Vector Databases Could Light The Way
The Register, Tuesday, July 11,2023
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 (LLMs) into production gathers pace.
LLMs have received a great deal of hype in the last six months, with OpenAI's GPT 4.0 sucking up the lion's share of media airtime. The idea is to extract some meaning - in the form of natural language question answering from a corpus of text. The relationships between words, sentences and other textual units are represented as multi-dimensional vectors (sometimes running into hundreds of dimensions), which are then resolved to find the most likely association.
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.