gpt3

type APIError struct {
	StatusCode int    `json:"status_code"`
	Message    string `json:"message"`
	Type       string `json:"type"`
}

type APIErrorResponse struct {
	Error APIError `json:"error"`
}

type Client interface {
	// Completion creates a completion with the default engine. This is the main endpoint of the API
	// which auto-completes based on the given prompt.
	Completion(ctx context.Context, request CompletionRequest) (*CompletionResponse, error)

	// CompletionStream creates a completion with the default engine and streams the results through
	// multiple calls to onData.
	CompletionStream(ctx context.Context, request CompletionRequest, onData func(*CompletionResponse)) error

	// Given a prompt and an instruction, the model will return an edited version of the prompt.
	Edits(ctx context.Context, request EditsRequest) (*EditsResponse, error)

	// Search performs a semantic search over a list of documents with the default engine.
	Search(ctx context.Context, request SearchRequest) (*SearchResponse, error)

	// Returns an embedding using the provided request.
	Embeddings(ctx context.Context, request EmbeddingsRequest) (*EmbeddingsResponse, error)
}

type ClientOption func(*client) error

type CompletionRequest struct {
	// A list of string prompts to use.
	// TODO there are other prompt types here for using token integers that we could add support for.
	Prompt []string `json:"prompt"`
	// How many tokens to complete up to. Max of 512
	MaxTokens *int `json:"max_tokens,omitempty"`
	// Sampling temperature to use
	Temperature *float32 `json:"temperature,omitempty"`
	// Alternative to temperature for nucleus sampling
	TopP *float32 `json:"top_p,omitempty"`
	// How many choice to create for each prompt
	N *int `json:"n"`
	// Include the probabilities of most likely tokens
	LogProbs *int `json:"logprobs,omitempty"`
	// Echo back the prompt in addition to the completion
	Echo bool `json:"echo,omitempty"`
	// Up to 4 sequences where the API will stop generating tokens. Response will not contain the stop sequence.
	Stop []string `json:"stop,omitempty"`
	// PresencePenalty number between 0 and 1 that penalizes tokens that have already appeared in the text so far.
	PresencePenalty float32 `json:"presence_penalty"`
	// FrequencyPenalty number between 0 and 1 that penalizes tokens on existing frequency in the text so far.
	FrequencyPenalty float32 `json:"frequency_penalty"`

	// Whether to stream back results or not. Don't set this value in the request yourself
	// as it will be overridden depending on if you use CompletionStream or Completion methods.
	Stream bool `json:"stream,omitempty"`
}

type CompletionResponse struct {
	ID      string                     `json:"id"`
	Object  string                     `json:"object"`
	Created int                        `json:"created"`
	Model   string                     `json:"model"`
	Choices []CompletionResponseChoice `json:"choices"`
	Usage   CompletionResponseUsage    `json:"usage"`
}

type CompletionResponseChoice struct {
	Text         string        `json:"text"`
	Index        int           `json:"index"`
	LogProbs     LogprobResult `json:"logprobs"`
	FinishReason string        `json:"finish_reason"`
}

type CompletionResponseUsage struct {
	PromptTokens     int `json:"prompt_tokens"`
	CompletionTokens int `json:"completion_tokens"`
	TotalTokens      int `json:"total_tokens"`
}

type EditsRequest struct {
	// ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.
	Model string `json:"model"`
	// The input text to use as a starting point for the edit.
	Input string `json:"input"`
	// The instruction that tells the model how to edit the prompt.
	Instruction string `json:"instruction"`
	// Sampling temperature to use
	Temperature *float32 `json:"temperature,omitempty"`
	// Alternative to temperature for nucleus sampling
	TopP *float32 `json:"top_p,omitempty"`
	// How many edits to generate for the input and instruction. Defaults to 1
	N *int `json:"n"`
}

type EditsResponse struct {
	Object  string                `json:"object"`
	Created int                   `json:"created"`
	Choices []EditsResponseChoice `json:"choices"`
	Usage   EditsResponseUsage    `json:"usage"`
}

type EditsResponseChoice struct {
	Text  string `json:"text"`
	Index int    `json:"index"`
}

type EditsResponseUsage struct {
	PromptTokens     int `json:"prompt_tokens"`
	CompletionTokens int `json:"completion_tokens"`
	TotalTokens      int `json:"total_tokens"`
}

type EmbeddingsRequest struct {
	// Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings
	// for multiple inputs in a single request, pass an array of strings or array of token arrays.
	// Each input must not exceed 2048 tokens in length.
	Input []string `json:"input"`
	// ID of the model to use
	Model string `json:"model"`
	// The request user is an optional parameter meant to be used to trace abusive requests
	// back to the originating user. OpenAI states:
	// "The [user] IDs should be a string that uniquely identifies each user. We recommend hashing
	// their username or email address, in order to avoid sending us any identifying information.
	// If you offer a preview of your product to non-logged in users, you can send a session ID
	// instead."
	User string `json:"user,omitempty"`
}

type EmbeddingsResponse struct {
	Object string             `json:"object"`
	Data   []EmbeddingsResult `json:"data"`
	Usage  EmbeddingsUsage    `json:"usage"`
}

type EmbeddingsResult struct {
	// The type of object returned (e.g., "list", "object")
	Object string `json:"object"`
	// The embedding data for the input
	Embedding []float64 `json:"embedding"`
	Index     int       `json:"index"`
}

type EmbeddingsUsage struct {
	// The number of tokens used by the prompt
	PromptTokens int `json:"prompt_tokens"`
	// The total tokens used
	TotalTokens int `json:"total_tokens"`
}

type EngineObject struct {
	ID     string `json:"id"`
	Object string `json:"object"`
	Owner  string `json:"owner"`
	Ready  bool   `json:"ready"`
}

type EnginesResponse struct {
	Data   []EngineObject `json:"data"`
	Object string         `json:"object"`
}

type LogprobResult struct {
	Tokens        []string             `json:"tokens"`
	TokenLogprobs []float32            `json:"token_logprobs"`
	TopLogprobs   []map[string]float32 `json:"top_logprobs"`
	TextOffset    []int                `json:"text_offset"`
}

type SearchData struct {
	Document int     `json:"document"`
	Object   string  `json:"object"`
	Score    float64 `json:"score"`
}

type SearchRequest struct {
	Documents []string `json:"documents"`
	Query     string   `json:"query"`
}

type SearchResponse struct {
	Data   []SearchData `json:"data"`
	Object string       `json:"object"`
}