Skip to content

completion_create_params

Classes:

Name Description
CompletionCreateParamsBase
CompletionCreateParamsNonStreaming
CompletionCreateParamsStreaming

Attributes:

Name Type Description
CompletionCreateParams

CompletionCreateParams module-attribute 

CompletionCreateParams = Union[
    CompletionCreateParamsNonStreaming,
    CompletionCreateParamsStreaming,
]

CompletionCreateParamsBase

Attributes:

Name Type Description
best_of Optional[int]

Generates best_of completions server-side and returns the "best" (the one with
echo Optional[bool]

Echo back the prompt in addition to the completion
frequency_penalty Optional[float]

Number between -2.0 and 2.0.
logit_bias Optional[Dict[str, int]]

Modify the likelihood of specified tokens appearing in the completion.
logprobs Optional[int]

Include the log probabilities on the logprobs most likely output tokens, as
max_tokens Optional[int]

The maximum number of tokens that can be generated in the
model Required[Union[str, Literal['gpt-3.5-turbo-instruct', 'davinci-002', 'babbage-002']]]

ID of the model to use.
n Optional[int]

How many completions to generate for each prompt.
presence_penalty Optional[float]

Number between -2.0 and 2.0.
prompt Required[Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None]]

The prompt(s) to generate completions for, encoded as a string, array of
seed Optional[int]

If specified, our system will make a best effort to sample deterministically,
stop Union[Optional[str], List[str], None]

Up to 4 sequences where the API will stop generating further tokens.
suffix Optional[str]

The suffix that comes after a completion of inserted text.
temperature Optional[float]

What sampling temperature to use, between 0 and 2.
top_p Optional[float]

An alternative to sampling with temperature, called nucleus sampling, where the
user str

A unique identifier representing your end-user, which can help OpenAI to monitor

best_of instance-attribute 

best_of: Optional[int]

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.

echo instance-attribute 

echo: Optional[bool]

Echo back the prompt in addition to the completion

frequency_penalty instance-attribute 

frequency_penalty: Optional[float]

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.

logit_bias instance-attribute 

logit_bias: Optional[Dict[str, int]]

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.

logprobs instance-attribute 

logprobs: Optional[int]

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.

max_tokens instance-attribute 

max_tokens: Optional[int]

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.

model instance-attribute 

model: Required[
    Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ]
]

ID 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.

n instance-attribute 

n: Optional[int]

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.

presence_penalty instance-attribute 

presence_penalty: Optional[float]

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.

prompt instance-attribute 

prompt: Required[
    Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ]
]

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.

seed instance-attribute 

seed: Optional[int]

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.

stop instance-attribute 

stop: Union[Optional[str], List[str], None]

Up to 4 sequences where the API will stop generating further tokens.

The returned text will not contain the stop sequence.

suffix instance-attribute 

suffix: Optional[str]

The suffix that comes after a completion of inserted text.

This parameter is only supported for gpt-3.5-turbo-instruct.

temperature instance-attribute 

temperature: Optional[float]

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.

top_p instance-attribute 

top_p: Optional[float]

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.

user instance-attribute 

user: str

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

CompletionCreateParamsNonStreaming

Attributes:

Name Type Description
stream Optional[Literal[False]]

Whether to stream back partial progress.

stream instance-attribute 

stream: Optional[Literal[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. Example Python code.

CompletionCreateParamsStreaming

Attributes:

Name Type Description
stream Required[Literal[True]]

Whether to stream back partial progress.

stream instance-attribute 

stream: Required[Literal[True]]

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.