You can use any sort of natural language text as input. The system transforms the input text into a multidimensional vector that roughly represents the semantic

You can use any sort of natural language text as input. The system transforms the input text into a multidimensional vector that roughly represents the semantic meaning of the input. If you do this for many different inputs, you can get representations for each of them and store them in a database alongside the inputs. From there, you can use traditional methods to search for nearby vectors to efficiently search by semantic meaning. One earlier related example is word2vec from 2013. This tool transforms individual words into embeddings, but is conceptually similar. Wikipedia has a decent overview that might be helpful: https://en.wikipedia.org/wiki/Word2vec https://en.wikipedia.org/wiki/Word2vec That work demonstrated the utility of transforming meaning into vector space for search but also for basic semantic reasoning. For example, you could perform an operation like "brother - man + woman" on the embedding and the result would be an embedding very close to "sister".