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completions

The completions module provides access to the legacy chat endpoint, /v1/completions. Use the chat.completions module instead for new applications.

You should not use this module for new projects. The legacy /v1/completions endpoint this module interacts with no longer receives updates and is expected to be deprecated. Use this module only in applications that require compatibility with the legacy endpoint.

You're strongly encouraged to migrate existing applications to the chat.completions module⁠—which interacts with the current (non-legacy) /v1/chat/completions endpoint—prior to the deprecation of the /v1/completions endpoint.

Classes:

Name Description
AsyncCompletions
AsyncCompletionsWithRawResponse
AsyncCompletionsWithStreamingResponse
Completions
CompletionsWithRawResponse
CompletionsWithStreamingResponse

AsyncCompletions 

AsyncCompletions(client: AsyncOpenAI)

Methods:

Name Description
create

Creates a completion for the provided prompt and parameters.
with_raw_response
with_streaming_response

create async 

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    stream: Literal[True],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> AsyncStream[Completion]

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    stream: bool,
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion | AsyncStream[Completion]

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    stream: (
        Optional[Literal[False]] | Literal[True] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion | AsyncStream[Completion]

Creates a completion for the provided prompt and parameters.

Parameters:

Name Type Description Default
model 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.

 required
prompt 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.

 required
best_of Optional[int] | NotGiven

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.

 NOT_GIVEN
echo Optional[bool] | NotGiven

Echo back the prompt in addition to the completion

 NOT_GIVEN
frequency_penalty Optional[float] | NotGiven

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.

 NOT_GIVEN
logit_bias Optional[Dict[str, int]] | NotGiven

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.

 NOT_GIVEN
logprobs Optional[int] | NotGiven

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.

 NOT_GIVEN
max_tokens Optional[int] | NotGiven

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.

 NOT_GIVEN
n Optional[int] | NotGiven

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.

 NOT_GIVEN
presence_penalty Optional[float] | NotGiven

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.

 NOT_GIVEN
seed Optional[int] | NotGiven

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.

 NOT_GIVEN
stop Union[Optional[str], List[str], None] | NotGiven

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

 NOT_GIVEN
stream Optional[Literal[False]] | Literal[True] | NotGiven

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.

 NOT_GIVEN
suffix Optional[str] | NotGiven

The suffix that comes after a completion of inserted text.

 NOT_GIVEN
temperature Optional[float] | NotGiven

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.

 NOT_GIVEN
top_p Optional[float] | NotGiven

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.

 NOT_GIVEN
user str | NotGiven

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

 NOT_GIVEN
extra_headers Headers | None

Send extra headers

 None
extra_query Query | None

Add additional query parameters to the request

 None
extra_body Body | None

Add additional JSON properties to the request

 None
timeout float | Timeout | None | NotGiven

Override the client-level default timeout for this request, in seconds

 NOT_GIVEN

with_raw_response 

with_raw_response() -> AsyncCompletionsWithRawResponse

with_streaming_response 

with_streaming_response() -> (
    AsyncCompletionsWithStreamingResponse
)

AsyncCompletionsWithRawResponse 

AsyncCompletionsWithRawResponse(
    completions: AsyncCompletions,
)

Attributes:

Name Type Description
create

create instance-attribute 

create = async_to_raw_response_wrapper(create)

AsyncCompletionsWithStreamingResponse 

AsyncCompletionsWithStreamingResponse(
    completions: AsyncCompletions,
)

Attributes:

Name Type Description
create

create instance-attribute 

create = async_to_streamed_response_wrapper(create)

Completions 

Completions(client: OpenAI)

Methods:

Name Description
create

Creates a completion for the provided prompt and parameters.
with_raw_response
with_streaming_response

create 

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    stream: Literal[True],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Stream[Completion]

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    stream: bool,
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion | Stream[Completion]

create(
    *,
    model: Union[
        str,
        Literal[
            "gpt-3.5-turbo-instruct",
            "davinci-002",
            "babbage-002",
        ],
    ],
    prompt: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
        None,
    ],
    best_of: Optional[int] | NotGiven = NOT_GIVEN,
    echo: Optional[bool] | NotGiven = NOT_GIVEN,
    frequency_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    logit_bias: (
        Optional[Dict[str, int]] | NotGiven
    ) = NOT_GIVEN,
    logprobs: Optional[int] | NotGiven = NOT_GIVEN,
    max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
    n: Optional[int] | NotGiven = NOT_GIVEN,
    presence_penalty: (
        Optional[float] | NotGiven
    ) = NOT_GIVEN,
    seed: Optional[int] | NotGiven = NOT_GIVEN,
    stop: (
        Union[Optional[str], List[str], None] | NotGiven
    ) = NOT_GIVEN,
    stream: (
        Optional[Literal[False]] | Literal[True] | NotGiven
    ) = NOT_GIVEN,
    suffix: Optional[str] | NotGiven = NOT_GIVEN,
    temperature: Optional[float] | NotGiven = NOT_GIVEN,
    top_p: Optional[float] | NotGiven = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> Completion | Stream[Completion]

Creates a completion for the provided prompt and parameters.

Parameters:

Name Type Description Default
model 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.

 required
prompt 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.

 required
best_of Optional[int] | NotGiven

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.

 NOT_GIVEN
echo Optional[bool] | NotGiven

Echo back the prompt in addition to the completion

 NOT_GIVEN
frequency_penalty Optional[float] | NotGiven

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.

 NOT_GIVEN
logit_bias Optional[Dict[str, int]] | NotGiven

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.

 NOT_GIVEN
logprobs Optional[int] | NotGiven

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.

 NOT_GIVEN
max_tokens Optional[int] | NotGiven

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.

 NOT_GIVEN
n Optional[int] | NotGiven

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.

 NOT_GIVEN
presence_penalty Optional[float] | NotGiven

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.

 NOT_GIVEN
seed Optional[int] | NotGiven

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.

 NOT_GIVEN
stop Union[Optional[str], List[str], None] | NotGiven

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

 NOT_GIVEN
stream Optional[Literal[False]] | Literal[True] | NotGiven

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.

 NOT_GIVEN
suffix Optional[str] | NotGiven

The suffix that comes after a completion of inserted text.

 NOT_GIVEN
temperature Optional[float] | NotGiven

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.

 NOT_GIVEN
top_p Optional[float] | NotGiven

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.

 NOT_GIVEN
user str | NotGiven

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

 NOT_GIVEN
extra_headers Headers | None

Send extra headers

 None
extra_query Query | None

Add additional query parameters to the request

 None
extra_body Body | None

Add additional JSON properties to the request

 None
timeout float | Timeout | None | NotGiven

Override the client-level default timeout for this request, in seconds

 NOT_GIVEN

with_raw_response 

with_raw_response() -> CompletionsWithRawResponse

with_streaming_response 

with_streaming_response() -> (
    CompletionsWithStreamingResponse
)

CompletionsWithRawResponse 

CompletionsWithRawResponse(completions: Completions)

Attributes:

Name Type Description
create

create instance-attribute 

create = to_raw_response_wrapper(create)

CompletionsWithStreamingResponse 

CompletionsWithStreamingResponse(completions: Completions)

Attributes:

Name Type Description
create

create instance-attribute 

create = to_streamed_response_wrapper(create)