ggpt3

package module
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 Go to latest Published: Dec 20, 2022 License: Apache-2.0 Imports: 9 Imported by: 0

README

GPT-3 API Go Wrapper

This library is under heavy developement. This readme will be updated with examples and more information on usage in the near future.

Features

  • Models API
  • Edits API
  • Images API
    • Images Edits
    • Images Variations
  • Embedding API
  • Files API
    • List
    • Upload
    • Delete
    • Retrieve
    • RetrieveContent
  • Fine-Tunes API
  • Moderation API
  • Engines API

Documentation

Index

Constants

View Source 
const (
	// Davinci is the most capable model family and can perform any task the other models can
	// perform and often with less instruction. For applications requiring a lot of
	// understanding of the content, like summarization for a specific audience and
	// creative content generation, Davinci is going to produce the best results.
	// These increased capabilities require more compute resources, so Davinci costs
	// more per API call and is not as fast as the other models.
	//
	// Good at: Complex intent, cause and effect, summarization for audience
	TextDavinci003 = "text-davinci-003"

	// Curie is extremely powerful, yet very fast. While Davinci is stronger when it
	// comes to analyzing complicated text, Curie is quite capable for many nuanced
	// tasks like sentiment classification and summarization. Curie is also quite
	// good at answering questions and performing Q&A and as a general service chatbot.
	//
	// Good at: Language translation, complex classification, text sentiment, summarization
	TextCurie001 = "text-curie-001"

	// Babbage can perform straightforward tasks like simple classification. It’s also quite
	// capable when it comes to Semantic Search ranking how well documents match up with search queries.
	//
	// Good at: Moderate classification, semantic search classification
	TextBabbage001 = "text-babbage-001"

	// Ada is usually the fastest model and can perform tasks like parsing text, address correction
	// and certain kinds of classification tasks that don’t require too much nuance.
	// Ada’s performance can often be improved by providing more context.
	//
	// Good at: Parsing text, simple classification, address correction, keywords
	TextAda001 = "text-ada-001"

	// Most capable Codex model. Particularly good at translating natural language to code.
	// In addition to completing code, also supports inserting completions within code.
	CodexCodeDavinci002 = "code-davinci-002"

	// Almost as capable as Davinci Codex, but slightly faster.
	// This speed advantage may make it preferable for real-time applications.
	CodexCodeCushman001 = "code-cushman-001"

	TextDavinciEdit001       = "text-davinci-edit-001"
	TextSimilarityBabbage001 = "text-similarity-babbage-001"
)

Variables

This section is empty.

Functions

This section is empty.

Types

type Client 

type Client struct {
	// contains filtered or unexported fields
}

func NewClient 

func NewClient(apiKey string) *Client

func (*Client) Model 

func (c *Client) Model(ctx context.Context, model string) (*ModelResponse, error)

Model retrieves a model instance, providing basic information about the model such as the owner and permissioning.

func (*Client) Models 

func (c *Client) Models(ctx context.Context) (*ModelResponse, error)

Models lists the currently available models, and provides basic information about each one such as the owner and availability.

func (*Client) RequestCompletion 

func (c *Client) RequestCompletion(ctx context.Context, cr *CompletionRequest) (*CompletionResponse, error)

RequestCompletion creates a completion for the provided prompt and parameters

func (*Client) RequestEdits 

func (c *Client) RequestEdits(ctx context.Context, er *EditsRequest) (*EditsResponse, error)

func (*Client) RequestEmbedding 

func (c *Client) RequestEmbedding(ctx context.Context, er *EmbeddingRequest) (*EmbeddingsResponse, error)

func (*Client) RequestImageEdits 

func (c *Client) RequestImageEdits(ctx context.Context, ir *ImageRequest) (*ImageResponse, error)

func (*Client) RequestImageVariations 

func (c *Client) RequestImageVariations(ctx context.Context, ir *ImageRequest) (*ImageResponse, error)

func (*Client) RequestImages 

func (c *Client) RequestImages(ctx context.Context, ir *ImageRequest) (*ImageResponse, error)

type CompletionRequest 

type CompletionRequest 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 prompt(s) to generate completions for, encoded as a string, array of strings,
	// array of tokens, or array of token arrays.
	//
	// Note that <|endoftext|> is the document separator that the model sees during training,
	// so if a prompt is not specified the model will generate as if from the beginning of a new document.
	Prompt string `json:"prompt,omitempty"`

	// The suffix that comes after a completion of inserted text.
	Suffix string `json:"suffix,omitempty"`

	// The maximum number of tokens to generate in the completion.
	//
	// The token count of your prompt plus max_tokens cannot exceed the model's context length.
	// Most models have a context length of 2048 tokens (except for the newest models, which support 4096).
	MaxTokens int `json:"max_tokens,omitempty"`

	// What sampling temperature to use. Higher values means the model will take more risks.
	// Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
	//
	// It is generally recommended to alter this or `top_p` but not both.
	Temperature float64 `json:"temperature,omitempty"`

	// An alternative to sampling with temperature, called nucleus sampling,
	// where the model considers the results of the tokens with top_p probability mass.
	// So 0.1 means only the tokens comprising the top 10% probability mass are considered.
	//
	// It is generally recommended to alter this or `tempature` but not both.
	TopP float64 `json:"top_p,omitempty"`

	// How many completions to generate for each prompt.
	N int `json:"n,omitempty"`

	// Whether to stream back partial progress. If set, tokens will
	// be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.
	Stream bool `json:"stream,omitempty"`

	// Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example,
	// if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return
	//the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.
	//
	// The maximum value for logprobs is 5. If you need more than this, please contact us through our Help center and describe your use case.
	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 further tokens. The returned text will not contain the stop sequence.
	Stop []string `json:"stop,omitempty"`

	// Number between -2.0 and 2.0. Positive values penalize new tokens based on whether
	// they appear in the text so far, increasing the model's likelihood to talk about new topics.
	PresencePenalty float64 `json:"presence_penalty,omitempty"`

	// Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency
	// in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
	FrequencyPenalty float64 `json:"frequency_penalty,omitempty"`

	// Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token).
	// Results cannot be streamed.
	//
	// When used with n, best_of controls the number of candidate completions and n specifies how many to
	// return – best_of must be greater than n.
	//
	// Note: Because this parameter generates many completions, it can quickly consume your token quota.
	// Use carefully and ensure that you have reasonable settings for max_tokens and stop.
	BestOf int `json:"best_of,omitempty"`

	// Modify the likelihood of specified tokens appearing in the completion.
	//
	// Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer)
	// to an associated bias value from -100 to 100. You can use this tokenizer tool
	// (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically,
	// the bias is added to the logits generated by the model prior to sampling. The exact effect
	// will vary per model, but values between -1 and 1 should decrease or increase likelihood of
	// selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
	//
	// As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.
	LogitBias map[string]int `json:"logit_bias,omitempty"`

	// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
	User string `json:"user,omitempty"`
}

CompletionRequest represents a request for text completion.

type CompletionResponse 

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

CompletionResponse represents a reponse from the Completions v1 endpoint.

type EditsRequest 

type EditsRequest struct {
	// Model is the 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,omitempty"`

	// The instruction that tells the model how to edit the prompt.
	Instruction string `json:"instruction"`

	// How many edits to generate for the input and instruction.
	N int `json:"n,omitempty"`

	// What sampling temperature to use. Higher values means the model will take more risks.
	// Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
	//
	// We generally recommend altering this or `top_p` but not both.
	Temperature float64 `json:"temperature,omitempty"`

	// An alternative to sampling with temperature, called nucleus sampling,
	// where the model considers the results of the tokens with top_p probability mass.
	// So 0.1 means only the tokens comprising the top 10% probability mass are considered.
	//
	// We generally recommend altering this or `temperature` but not both.
	TopP float64 `json:"top_p,omitempty"`
}

EditsRequest represents a request for edits.

type EditsResponse 

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

EditsResponse represents a response from the edits endpoint.

type EmbeddingRequest 

type EmbeddingRequest struct {
	// ID of the model to use.
	Model string `json:"model"`

	// 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.
	//
	// Unless you are embedding code, we suggest replacing newlines (\n) in
	// your input with a single space, as we have observed inferior results when newlines are present.
	Input []string `json:"input"`

	// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
	User string `json:"user,omitempty"`
}

EmbeddingRequest represent a request body for the embeddings endpoint.

type EmbeddingsResponse 

type EmbeddingsResponse struct {
	Object string
	Data   []data
	Usage  usage
}

EmbeddingsResponse represents a response from the embeddings endpoint.

type Error 

type Error struct {
	Message string  `json:"message"`
	Type    string  `json:"type"`
	Param   *string `json:"param"`
	Code    *string `json:"code"`
}

type ErrorResponse 

type ErrorResponse struct {
	*Error `json:"error"`
}

type ImageRequest 

type ImageRequest struct {
	// The image to edit. Must be a valid PNG file, less than 4MB, and square.
	// If mask is not provided, image must have transparency, which will be used as the mask.
	Image string `json:"image,omitempty"`

	// An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where
	// image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.
	Mask string `json:"mask,omitempty"`

	// A text description of the desired image(s). The maximum length is 1000 characters.
	Prompt string `json:"prompt,omitempty"`

	// The number of images to generate. Must be between 1 and 10.
	N int `json:"n,omitempty"`

	// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024
	Size string `json:"size,omitempty"`

	// The format in which the generated images are returned. Must be one of url or b64_json.
	ResponseFormat string `json:"response_format,omitempty"`

	// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
	User string `json:"user,omitempty"`
}

ImageRequest represent a request body for the image endpoint.

type ImageResponse 

type ImageResponse struct {
	Created int
	Data    []data
}

ImageResponse represents a response from the images endpoint.

type ModelResponse 

type ModelResponse struct {
	Data []data `json:"data"`
}

ModelResponse represents a response from the Models v1 endpoint.

Source Files

View all Source files

Directories

Path Synopsis
examples
completion

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