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