fontanierh/openai.ex

OpenAI API Wrapper written in Elixir.

Openai.ex

Provides wrappers for OpenAI REST APIs See https://beta.openai.com/docs/api-reference/introduction for further info on REST endpoints

Installation

Add :openai as a dependency in your mix.exs file.

def deps do
  [
    {:openai, "~> 0.1.1"}
  ]
end

Configuration

You can configure openai in your mix config.exs (default $project_root/config/config.exs). If you're using Phoenix add the configuration in your config/dev.exs|test.exs|prod.exs files. An example config is:

use Mix.Config

config :openai,
  api_key: "your-api-key", # find it at https://beta.openai.com/account/api-keys
  organization_key: "your-organization-key" # find it at https://beta.openai.com/account/api-keys

Usage overview

Get your API key from https://beta.openai.com/docs/developer-quickstart/your-api-keys

engines()

Get the list of available engines

Example request

OpenAI.engines()

Example response

{:ok, %{
  "data" => [
    %{"id" => "davinci", "object" => "engine", "max_replicas": ...},
    ...,
    ...
  ]
}

See: https://beta.openai.com/docs/api-reference/engines/list

engines(engine_id)

Retrieve specific engine info

Example request

OpenAI.engines("davinci")

Example respone

{:ok, %{
    "id" => "davinci",
    "object" => "engine",
    "max_replicas": ...
  }
}

See: https://beta.openai.com/docs/api-reference/engines/retrieve

completions(engine_id, params)

It returns one or more predicted completions given a prompt. The function accepts as arguments the "engine_id" and the set of parameters used by the Completions OpenAI api.

Example request

  OpenAI.completions(
    "davinci", # engine_id
    prompt: "once upon a time",
    max_tokens: 5,
    temperature: 1,
    ...
)

Example response

{:ok, %{
  choices: [
    %{
      "finish_reason" => "length",
      "index" => 0,
      "logprobs" => nil,
      "text" => "\" thing we are given"
    }
  ],
  created: 1617147958,
  id: "...",
  model: "...",
  object: "text_completion"
  }
}

See: https://beta.openai.com/docs/api-reference/completions/create for the complete list of parameters you can pass to the completions function

search(engine_id, params)

It returns a rank of each document passed to the function, based on its semantic similarity to the passed query. The function accepts as arguments the engine_id and theset of parameters used by the Search OpenAI api

Example request

OpenAI.search(
  "babbage", #engine_id
  documents: ["White House", "hospital", "school"],
  query: "the president"
)

Example response

{:ok,
  %{
    data: [
      %{"document" => 0, "object" => "search_result", "score" => 218.676},
      %{"document" => 1, "object" => "search_result", "score" => 17.797},
      %{"document" => 2, "object" => "search_result", "score" => 29.65}
    ],
    model: "...",
    object: "list"
  }
}

See: https://beta.openai.com/docs/api-reference/searches for the complete list of parameters you can pass to the search function

classifications(params)

It returns the most likely label for the query passed to the function. The function accepts as arguments a set of parameters that will be passed to the Classifications OpenAI api

Given a query and a set of labeled examples, the model will predict the most likely label for the query. Useful as a drop-in replacement for any ML classification or text-to-label task.

Example request

OpenAI.classifications(
  examples: [
    ["A happy moment", "Positive"],
    ["I am sad.", "Negative"],
    ["I am feeling awesome", "Positive"]
  ],
  labels: ["Positive", "Negative", "Neutral"],
  query: "It is a raining day :(",
  search_model: "ada",
  model: "curie"
)

Example response

{:ok,
  %{
    completion: "cmpl-2jIXZYg7Buyg1DDRYtozkre50TSMb",
    label: "Negative",
    model: "curie:2020-05-03",
    object: "classification",
    search_model: "ada",
    selected_examples: [
      %{"document" => 1, "label" => "Negative", "text" => "I am sad."},
      %{"document" => 0, "label" => "Positive", "text" => "A happy moment"},
      %{"document" => 2, "label" => "Positive", "text" => "I am feeling awesome"}
    ]
  }
}

See: https://beta.openai.com/docs/api-reference/classifications for the complete list of parameters you can pass to the classifications function

answers(params)

The endpoint first searches over provided documents or files to find relevant context. The relevant context is combined with the provided examples and question to create the prompt for completion.

Example request

OpenAI.answers(
  model: "curie",
  documents: ["Puppy A is happy.", "Puppy B is sad."],
  question: "which puppy is happy?",
  search_model: "ada",
  examples_context: "In 2017, U.S. life expectancy was 78.6 years.",
  examples: [["What is human life expectancy in the United States?", "78 years."]],
  max_tokens: 5
)

Example response

{:ok,
  %{
    answers: ["puppy A."],
    completion: "cmpl-2kdRgXcoUfaAXxlPjmZXBT8AlKWfB",
    model: "curie:2020-05-03",
    object: "answer",
    search_model: "ada",
    selected_documents: [
      %{"document" => 0, "text" => "Puppy A is happy. "},
      %{"document" => 1, "text" => "Puppy B is sad. "}
    ]
  }
}

See: https://beta.openai.com/docs/api-reference/answers

TODO:

• integrate file apis

License

The package is available as open source under the terms of the MIT License.