export interface EngineResponse {
    id: string;
    object: 'engine';
    owner: string;
    ready: boolean;
}

export interface EnginesListResponse {
    data: Array<EngineResponse>;
    object: 'list';
}

export interface CreateCompletionRequest {
    /**
     * string or array Optional Defaults to <|endoftext|>
     *
     * The prompt(s) to generate completions for, encoded as a string, a list of strings, or a list of token lists.
     * 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 | string[];

        /**
         * integer Optional Defaults to 16
         *
         * The maximum number of tokens to generate. Requests can use up to 2048 tokens shared between prompt and completion. (One token is roughly 4 characters for normal English text)
         */
        max_tokens?: number;

    /**
     * number Optional Defaults to 1
     *
     * 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?: number;

    /**
     * number Optional Defaults to 1
     *
     * 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.
     */
    top_p?: number;

    /**
     * integer Optional Defaults to 1
     *
     * How many completions to generate for each prompt.
     * 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.
     */
    n?: number;

    /**
     * boolean Optional Defaults to false
     *
     * 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?: boolean;

    /**
     * integer Optional Defaults to null
     *
     * Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 10, the API will return a list of the 10 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.
     */
    logprobs?: number;

    /**
     * boolean Optional Defaults to false
     *
     * Echo back the prompt in addition to the completion
     */
    echo?: boolean;

    /**
     * string or array Optional Defaults to null
     *
     * Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
     */
    stop?: string | string[];

    /**
     * number Optional Defaults to 0
     *
     * Number between 0 and 1 that penalizes new tokens based on whether they appear in the text so far. Increases the model's likelihood to talk about new topics.
     */
    presence_penalty?: number;

    /**
     * number Optional Defaults to 0
     *
     * Number between 0 and 1 that penalizes new tokens based on their existing frequency in the text so far. Decreases the model's likelihood to repeat the same line verbatim.
     */
    frequency_penalty?: number;

    /**
     * integer Optional Defaults to 1
     *
     * Generates best_of completions server-side and returns the "best" (the one with the lowest 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.
     */
    best_of?: number;

    /**
     * map Optional Defaults to null
     *
     * 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.
     */
    logit_bias?: {[tokenId: string]: number};

}

export interface CreateCompletionViaGetRequest {
    /**
     * string or array Optional Defaults to <|endoftext|>
     *
     * The prompt(s) to generate completions for, encoded as a string, a list of strings, or a list of token lists.
     * 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 | string[];

    /**
     * integer Optional Defaults to 16
     *
     * The maximum number of tokens to generate. Requests can use up to 2048 tokens shared between prompt and completion. (One token is roughly 4 characters for normal English text)
     */
    max_tokens?: number;

    /**
     * number Optional Defaults to 1
     *
     * 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?: number;

    /**
     * number Optional Defaults to 1
     *
     * 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.
     */
    top_p?: number;

    /**
     * integer Optional Defaults to 1
     *
     * How many completions to generate for each prompt.
     * 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.
     */
    n?: number;

    /**
     * integer Optional Defaults to null
     *
     * Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 10, the API will return a list of the 10 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.
     */
    logprobs?: number;

    /**
     * boolean Optional Defaults to false
     *
     * Echo back the prompt in addition to the completion
     */
    echo?: boolean;

    /**
     * string or array Optional Defaults to null
     *
     * Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
     */
    stop?: string | string[];

    /**
     * number Optional Defaults to 0
     *
     * Number between 0 and 1 that penalizes new tokens based on whether they appear in the text so far. Increases the model's likelihood to talk about new topics.
     */
    presence_penalty?: number;

    /**
     * number Optional Defaults to 0
     *
     * Number between 0 and 1 that penalizes new tokens based on their existing frequency in the text so far. Decreases the model's likelihood to repeat the same line verbatim.
     */
    frequency_penalty?: number;

    /**
     * integer Optional Defaults to 1
     *
     * Generates best_of completions server-side and returns the "best" (the one with the lowest 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.
     */
    best_of?: number;

    /**
     * map Optional Defaults to null
     *
     * 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.
     */
    logit_bias?: {[tokenId: string]: number};

}

export interface Choice {

    text: string;
    index: number;
    logprobs: any;
    finish_reason: string;

}

export interface CreateCompletionResponse {
    id: string;
    object: 'text_completion';
    created: number;
    model: string;
    choices: Array<Choice>;
}

export interface CreateSearchRequestBase {

    /**
     * string Required
     * Query to search against the documents.
     */
    query: string;

    /**
     * Integer Optional Defaults to 200
     *
     * The maximum number of documents to be re-ranked and returned by search.
     * This flag only takes effect when file is set.
     */
    max_rerank?: number;

    /**
     * boolean Optional Defaults to false
     *
     * A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field.
     * This flag only takes effect when file is set.
     */
    return_metadata?: boolean;

}

export interface CreateSearchRequestViaDocuments extends CreateSearchRequestBase {
    /**
     * string Required
     *
     * Up to 200 documents to search over, provided as a list of strings.
     * The maximum document length (in tokens) is 2034 minus the number of tokens in the query.
     */
    documents: Array<string>;
}

export interface CreateSearchRequestViaFile extends CreateSearchRequestBase {
    /**
     * string Required
     *
     * The ID of an uploaded file that contains documents to search over.
     */
    file: string;
}

export interface CreateSearchResponse {
    data: Array<{
        document: number;
        object: 'search_result';
        score: number;
    }>;
    object: 'list';
}

export interface CreateClassificationRequestBase {

    /**
     * string Required
     *
     * ID of the engine to use for completion.
     */
    model: string;

    /**
     * string Required
     *
     * Query to be classified.
     */
    query: string;

    /**
     * array Optional Defaults to null
     *
     * The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.
     */
    labels?: string[];

    /**
     * string Optional Defaults to ada
     *
     * ID of the engine to use for Search.
     */
    search_model?: string;

    /**
     * Optional Defaults to 0
     *
     * What sampling temperature to use. Higher values mean the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
     */
    temperature?: number;

    /**
     * Optional Defaults to null
     *
     * Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 10, the API will return a list of the 10 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.
     * When logprobs is set, completion will be automatically added into expand to get the logprobs.
     */
    logprobs?: number;

    /**
     * map Optional Defaults to null
     *
     * 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.
     */
    logit_bias?: {[tokenId: string]: number};

    /**
     * boolean Optional Defaults to false
     *
     * If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
     */
    return_prompt?: boolean;

    /**
     * boolean Optional Defaults to false
     *
     * A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field.
     * This flag only takes effect when file is set.
     */
    return_metadata?: boolean;

    /**
     * Optional Defaults to []
     *
     * If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.
     */
    expand?: Array<string>;
}

export interface CreateClassificationRequestViaExamples extends CreateClassificationRequestBase {
    /**
     * array Required
     *
     * A list of examples with labels, in the follwing format:
     * [["The movie is so interesting.", "Positive"], ["It is quite boring.", "Negative"], ...]
     * All the label strings will be normalized to be capitalized.
     */
    examples: Array<string>;
}

export interface CreateClassificationRequestViaFile extends CreateClassificationRequestBase {
    /**
     * string Required
     *
     * The ID of an uploaded file that contains documents to search over.
     */
    file: string;

    /**
     * integer Optional defaults to 200
     *
     * The maximum number of examples to be ranked by Search when using file. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
     */
    max_examples?: number;
}

export interface CreateClassificationResponse {

    completion: string;
    label: string;
    model: string;
    object: 'classification';
    search_model: string;
    selected_examples?: Array<{
        document: number;
        text: string;
    }>;
    selected_documents?: Array<{
        document: number;
        text: string;
    }>;
}

export interface CreateAnswerRequestBase {

    /**
     * string Required
     *
     * ID of the engine to use for completion.
     */
    model: string;

    /**
     * string Required
     *
     * Question to get answered.
     */
    question: string;

    /**
     * array Required
     *
     * List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples.
     */
    examples: Array<string>;

    /**
     * string Required
     *
     * A text snippet containing the contextual information used to generate the answers for the examples you provide.
     */
    examples_context: string;

    /**
     * string Optional Defaults to ada
     *
     * ID of the engine to use for Search.
     */
    search_model?: string;

    /**
     * number Optional Defaults to 1
     *
     * 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?: number;

    /**
     * integer Optional Defaults to null
     *
     * Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 10, the API will return a list of the 10 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.
     */
    logprobs?: number;

    /**
     * integer Optional Defaults to 16
     *
     * The maximum number of tokens to generate. Requests can use up to 2048 tokens shared between prompt and completion. (One token is roughly 4 characters for normal English text)
     */
    max_tokens?: number;

    /**
     * string or array Optional Defaults to null
     *
     * Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
     */
    stop?: string | string[];

    /**
     * integer Optional Defaults to 1
     *
     * How many completions to generate for each prompt.
     * 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.
     */
    n?: number;

    /**
     * map Optional Defaults to null
     *
     * 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.
     */
    logit_bias?: {[tokenId: string]: number};

    /**
     * boolean Optional Defaults to false
     *
     * A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field.
     * This flag only takes effect when file is set.
     */
    return_metadata?: boolean;

    /**
     * boolean Optional Defaults to false
     *
     * If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
     */
    return_prompt?: boolean;

    /**
     * Optional Defaults to []
     *
     * If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.
     */
    expand?: Array<string>;
}


export interface CreateAnswerRequestViaDocuments extends CreateAnswerRequestBase {
    /**
     * array Required
     *
     * List of documents from which the answer for the input question should be derived. If this is an empty list, the question will be answered based on the question-answer examples.
     */
    documents: Array<string>;
}

export interface CreateAnswerRequestViaFile extends CreateAnswerRequestBase {
    /**
     * string Required
     *
     * The ID of an uploaded file that contains documents to search over.
     */
    file: string;

    /**
     * Integer Optional Defaults to 200
     *
     * The maximum number of documents to be re-ranked and returned by search.
     * This flag only takes effect when file is set.
     */
    max_rerank?: number;
}

export interface CreateAnswerResponse {

    answers: Array<string>;
    completion: string;
    model: string;
    object: 'answer';
    search_model: string;
    selected_documents: Array<{
        document: number;
        text: string;
    }>;

}

export type Purpose = 'search' | 'answers' | 'classifications';

export interface OpenAIFile {

    id: string;
    object: 'file';
    bytes: number;
    created_at: number;
    filename: string;
    purpose: Purpose;

}

export interface ListFilesResponse {
    data: Array<OpenAIFile>;
    object: 'list';
}

export type UploadFileResponse = OpenAIFile;

export type RetrieveFileResponse = OpenAIFile;