OpenAI::Embedding
Inherits JSON::Serializable / Struct / Value / Object
Embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar.
Constructors
new(pull : JSON::PullParser)
Sourcenew(*, __pull_for_json_serializable pull : JSON::PullParser)
SourceInstance methods
embedding
The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.