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packages/excalidraw/data/ai/types.ts
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packages/excalidraw/data/ai/types.ts
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export namespace OpenAIInput {
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type ChatCompletionContentPart =
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| ChatCompletionContentPartText
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| ChatCompletionContentPartImage;
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interface ChatCompletionContentPartImage {
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image_url: ChatCompletionContentPartImage.ImageURL;
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/**
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* The type of the content part.
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*/
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type: "image_url";
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}
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namespace ChatCompletionContentPartImage {
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export interface ImageURL {
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/**
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* Either a URL of the image or the base64 encoded image data.
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*/
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url: string;
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/**
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* Specifies the detail level of the image.
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*/
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detail?: "auto" | "low" | "high";
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}
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}
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interface ChatCompletionContentPartText {
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/**
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* The text content.
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*/
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text: string;
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/**
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* The type of the content part.
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*/
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type: "text";
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}
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interface ChatCompletionUserMessageParam {
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/**
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* The contents of the user message.
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*/
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content: string | Array<ChatCompletionContentPart> | null;
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/**
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* The role of the messages author, in this case `user`.
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*/
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role: "user";
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}
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interface ChatCompletionSystemMessageParam {
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/**
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* The contents of the system message.
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*/
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content: string | null;
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/**
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* The role of the messages author, in this case `system`.
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*/
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role: "system";
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}
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export interface ChatCompletionCreateParamsBase {
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/**
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* A list of messages comprising the conversation so far.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
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*/
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messages: Array<
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ChatCompletionUserMessageParam | ChatCompletionSystemMessageParam
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>;
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/**
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* ID of the model to use. See the
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* [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
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* table for details on which models work with the Chat API.
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*/
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model:
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| (string & {})
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| "gpt-4-1106-preview"
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| "gpt-4-vision-preview"
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| "gpt-4"
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| "gpt-4-0314"
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| "gpt-4-0613"
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| "gpt-4-32k"
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| "gpt-4-32k-0314"
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| "gpt-4-32k-0613"
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| "gpt-3.5-turbo"
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| "gpt-3.5-turbo-16k"
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| "gpt-3.5-turbo-0301"
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| "gpt-3.5-turbo-0613"
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| "gpt-3.5-turbo-16k-0613";
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/**
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* Number between -2.0 and 2.0. Positive values penalize new tokens based on their
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* existing frequency in the text so far, decreasing the model's likelihood to
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* repeat the same line verbatim.
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*
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* [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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*/
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frequency_penalty?: number | null;
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/**
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* Modify the likelihood of specified tokens appearing in the completion.
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*
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* Accepts a JSON object that maps tokens (specified by their token ID in the
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* tokenizer) to an associated bias value from -100 to 100. Mathematically, the
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* bias is added to the logits generated by the model prior to sampling. The exact
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* effect will vary per model, but values between -1 and 1 should decrease or
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* increase likelihood of selection; values like -100 or 100 should result in a ban
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* or exclusive selection of the relevant token.
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*/
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logit_bias?: Record<string, number> | null;
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/**
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* The maximum number of [tokens](/tokenizer) to generate in the chat completion.
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*
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* The total length of input tokens and generated tokens is limited by the model's
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* context length.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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* for counting tokens.
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*/
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max_tokens?: number | null;
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/**
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* How many chat completion choices to generate for each input message.
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*/
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n?: number | null;
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/**
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* Number between -2.0 and 2.0. Positive values penalize new tokens based on
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* whether they appear in the text so far, increasing the model's likelihood to
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* talk about new topics.
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*
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* [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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*/
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presence_penalty?: number | null;
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/**
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* This feature is in Beta. If specified, our system will make a best effort to
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* sample deterministically, such that repeated requests with the same `seed` and
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* parameters should return the same result. Determinism is not guaranteed, and you
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* should refer to the `system_fingerprint` response parameter to monitor changes
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* in the backend.
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*/
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seed?: number | null;
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/**
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* Up to 4 sequences where the API will stop generating further tokens.
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*/
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stop?: string | null | Array<string>;
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/**
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* If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
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* sent as data-only
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* [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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* as they become available, with the stream terminated by a `data: [DONE]`
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* message.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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*/
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stream?: boolean | null;
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/**
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* What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
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* make the output more random, while lower values like 0.2 will make it more
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* focused and deterministic.
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*
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* We generally recommend altering this or `top_p` but not both.
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*/
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temperature?: number | null;
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/**
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* An alternative to sampling with temperature, called nucleus sampling, where the
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* model considers the results of the tokens with top_p probability mass. So 0.1
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* means only the tokens comprising the top 10% probability mass are considered.
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*
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* We generally recommend altering this or `temperature` but not both.
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*/
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top_p?: number | null;
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/**
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* A unique identifier representing your end-user, which can help OpenAI to monitor
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* and detect abuse.
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* [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
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*/
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user?: string;
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}
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}
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export namespace OpenAIOutput {
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export interface ChatCompletion {
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/**
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* A unique identifier for the chat completion.
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*/
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id: string;
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/**
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* A list of chat completion choices. Can be more than one if `n` is greater
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* than 1.
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*/
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choices: Array<Choice>;
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/**
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* The Unix timestamp (in seconds) of when the chat completion was created.
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*/
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created: number;
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/**
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* The model used for the chat completion.
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*/
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model: string;
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/**
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* The object type, which is always `chat.completion`.
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*/
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object: "chat.completion";
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/**
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* This fingerprint represents the backend configuration that the model runs with.
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*
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* Can be used in conjunction with the `seed` request parameter to understand when
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* backend changes have been made that might impact determinism.
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*/
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system_fingerprint?: string;
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/**
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* Usage statistics for the completion request.
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*/
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usage?: CompletionUsage;
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}
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export interface Choice {
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/**
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* The reason the model stopped generating tokens. This will be `stop` if the model
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* hit a natural stop point or a provided stop sequence, `length` if the maximum
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* number of tokens specified in the request was reached, `content_filter` if
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* content was omitted due to a flag from our content filters, `tool_calls` if the
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* model called a tool, or `function_call` (deprecated) if the model called a
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* function.
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*/
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finish_reason:
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| "stop"
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| "length"
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| "tool_calls"
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| "content_filter"
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| "function_call";
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/**
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* The index of the choice in the list of choices.
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*/
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index: number;
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/**
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* A chat completion message generated by the model.
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*/
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message: ChatCompletionMessage;
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}
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interface ChatCompletionMessage {
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/**
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* The contents of the message.
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*/
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content: string | null;
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/**
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* The role of the author of this message.
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*/
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role: "assistant";
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}
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/**
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* Usage statistics for the completion request.
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*/
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interface CompletionUsage {
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/**
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* Number of tokens in the generated completion.
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*/
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completion_tokens: number;
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/**
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* Number of tokens in the prompt.
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*/
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prompt_tokens: number;
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/**
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* Total number of tokens used in the request (prompt + completion).
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*/
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total_tokens: number;
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}
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export interface APIError {
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readonly status: 400 | 401 | 403 | 404 | 409 | 422 | 429 | 500 | undefined;
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readonly headers: Headers | undefined;
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readonly error: { message: string } | undefined;
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readonly code: string | null | undefined;
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readonly param: string | null | undefined;
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readonly type: string | undefined;
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}
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}
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