OpenAI.Embeddings

OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are most commonly used for:
Search (where results are ranked by relevance to a query string)
Clustering (where text strings are grouped by similarity)
Recommendations (where items with related text strings are recommended)
Anomaly detection (where outliers with little relatedness are identified)
Diversity measurement (where similarity distributions are analyzed)
Classification (where text strings are classified by their most similar label)
An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

See https://beta.openai.com/docs/guides/embeddings

create : Input -> TaskInput (Error String) Output

https://beta.openai.com/docs/api-reference/embeddings/create

create
    { model = OpenAI.ModelID.TextEmbeddingAda002
    , input = "The food was delicious and the waiter..."
    , user = Nothing
    }
    |> OpenAI.withConfig cfg
    |> Http.task
-- > Task.succeed
-- >     { data =
-- >         [ { embedding = [0.0023064255,-0.009327292,...,-0.0028842222 ]
-- >           , index = 0
-- >           , object = "embedding"
-- >           }
-- >         ]
-- >     , model = Custom "text-embedding-ada-002-v2"
-- >     , object = "list"
-- >     , usage = { prompt_tokens = 8, total_tokens = 8 }
-- >     }

type alias Input =

{ model : ModelID

, input : String

, user : Maybe String

}

type alias Output =

{ object : String

, data : List Data

, model : ModelID

, usage : Usage

}

type alias Data =

{ object : String

, index : Int

, embedding : List Float

}

type alias Usage =

{ prompt_tokens : Int

, total_tokens : Int

}

choonkeat/elm-openai/3.0.0

OpenAI.Embeddings

Modules