Terminology

Guide to the terminology used in Relevance AI

TerminologyDefinition
VectorsAKA embeddings, 1D arrays, latent space vectors.
Vectorizers/models/EncodersTurns data into vectors (e.g. Word2Vec turns words into vectors).
Vector Similarity SearchNearest neighbor search, distance search.
DatasetIndex, Table (a dataset is made up of multiple documents)
Documents(AKA JSON, item, dictionary, data row) - a document can contain vectors and other important information .
FieldA field is a key in a Python dictionary {"key" : 0.123}.
ValueA value is a value in a Python dictionary {"description" : value}.
Insert(AKA index a dataset) is the process of inserting a dataset to Relevance AI platform.