@sap-ai-sdk/foundation-models
Version:
SAP Cloud SDK for AI is the official Software Development Kit (SDK) for **SAP AI Core**, **SAP Generative AI Hub**, and **Orchestration Service**.
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TypeScript
/**
* Representation of the 'AzureOpenAiCreateCompletionRequest' schema.
*/
export type AzureOpenAiCreateCompletionRequest = {
/**
* 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.
*
* Default: "<|endoftext|>".
*/
prompt: string | string[] | null;
/**
* 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`.
*
* Default: 1.
* Maximum: 20.
*/
best_of?: number | null;
/**
* Echo back the prompt in addition to the completion
*
*/
echo?: boolean | null;
/**
* 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.
*
* Maximum: 2.
* Minimum: -2.
*/
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 GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](https://platform.openai.com/tokenizer?view=bpe) 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?: Record<string, number> | null;
/**
* Include the log probabilities on the `logprobs` most likely output 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.
*
* Maximum: 5.
*/
logprobs?: number | null;
/**
* The maximum number of [tokens](https://platform.openai.com/tokenizer) that can be generated in the completion.
*
* The token count of your prompt plus `max_tokens` cannot exceed the model's context length. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
*
* @example 16
* Default: 16.
*/
max_tokens?: number | null;
/**
* 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`.
*
* @example 1
* Default: 1.
* Maximum: 128.
* Minimum: 1.
*/
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.
*
* Maximum: 2.
* Minimum: -2.
*/
presence_penalty?: number | null;
/**
* 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.
*
* Maximum: 9223372036854776000.
* Minimum: -9223372036854776000.
*/
seed?: number | 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[] | null;
/**
* Whether to stream back partial progress. If set, 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;
/**
* The suffix that comes after a completion of inserted text.
*
* This parameter is only supported for `gpt-3.5-turbo-instruct`.
*
* @example "test."
*/
suffix?: string | 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.
*
* @example 1
* Default: 1.
* Maximum: 2.
*/
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.
*
* @example 1
* Default: 1.
* Maximum: 1.
*/
top_p?: number | null;
/**
* A unique identifier representing your end-user, which can help to monitor and detect abuse.
*
* @example "user-1234"
*/
user?: string;
} & Record<string, any>;
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