CreateCompletionRequest
objectAny OfID 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.
One OfThe 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|>
Default:
Example:This is a test.
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
>= 0<= 20
Echo back the prompt in addition to the completion
Default:false
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.
Default:0
>= -2<= 2
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 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.
Default: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.
Default:null
>= 0<= 5
The maximum number of tokens 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 for counting tokens.
Default:16
>= 0
Example:16
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.
Default:1
>= 1<= 128
Example:1
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.
Default:0
>= -2<= 2
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.
One OfUp to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
Default:null
Default:<|endoftext|>
Example:
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. Example Python code.
Default:false
Options for streaming response. Only set this when you set stream: true.
Default:null
Show Child Parameters
The suffix that comes after a completion of inserted text.
This parameter is only supported for gpt-3.5-turbo-instruct.
Default:null
Example:test.
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.
Default:1
>= 0<= 2
Example: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.
Default:1
>= 0<= 1
Example:1
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example:user-1234
CreateCompletionResponse
objectRepresents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).
A unique identifier for the completion.
The list of completion choices the model generated for the input prompt.
Show Child Parameters
The Unix timestamp (in seconds) of when the completion was created.
The model used for 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.
The object type, which is always “text_completion”
Allowed values:text_completion
Usage statistics for the completion request.
Show Child Parameters
CreateEmbeddingRequest
objectOne OfInput text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens. Some models may also impose a limit on total number of tokens summed across inputs.
Example:The quick brown fox jumped over the lazy dog
The string that will be turned into an embedding.
Default:
Example:This is a test.
Any OfID 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.
Example:text-embedding-3-small
The format to return the embeddings in. Can be either float or base64.
Allowed values:floatbase64
Default:float
Example:float
The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
>= 1
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example:user-1234
CreateEmbeddingResponse
objectRepresents an embedding vector returned by embedding endpoint.
Show Child Parameters
The name of the model used to generate the embedding.
The object type, which is always “list”.
Allowed values:list
The usage information for the request.
Show Child Parameters
CreateFileRequest
objectThe File object (not file name) to be uploaded.
The intended purpose of the uploaded file.
Use “assistants” for Assistants and Message files, “vision” for Assistants image file inputs, “batch” for Batch API, and “fine-tune” for Fine-tuning.
Allowed values:assistantsbatchfine-tunevision