The arguments to call the function with, as generated by the model in JSON format.
Note that the model does not always generate valid JSON, and may hallucinate
parameters
not defined by your function schema. Validate the arguments in your code before
calling your function.
The arguments to call the function with, as generated by the model in JSON format.
Note that the model does not always generate valid JSON, and may hallucinate parameters
not defined by your function schema. Validate the arguments in your code before calling your
function.
Defaults to 0. 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.
Defaults to 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.
Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each
output token returned in the content of message.
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with
an associated log probability.
logprobs must be set to true if this parameter is used.
How many chat completion choices to generate for each input message. Note that you will be charged based on the
number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
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.
An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models
newer than gpt-3.5-turbo-1106.
Setting to {"type": "json_object"} enables JSON mode, which guarantees the message the model generates is valid
JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user
message.
Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit,
resulting in a long-running and seemingly “stuck” request.
Also note that the message content may be partially cut off if finish_reason="length", which indicates the
generation exceeded max_tokens or the conversation exceeded the max context length.
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.
If set, partial message deltas will be sent, like in ChatGPT. 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.
If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this
chunk shows the token usage statistics for the entire request, and the choices field will always be an
empty array. All other chunks will also include a usage field, but with a null value.
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.
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.
A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of
functions the model may generate JSON inputs for. A max of 128 functions are supported.
none means the model will not call any tool and instead generates a message.
auto means the model can pick between generating a message or calling one or more tools.
required means the model must call one or more tools.
none means the model will not call a function and instead generates a message.
auto means the model can pick between generating a message or calling a function.
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.
The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be `null` if there is no bytes representation for the token.
List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested `top_logprobs` returned.
The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be `null` if there is no bytes representation for the token.
The arguments to call the function with, as generated by the model
in JSON format. Note that the model does not always generate valid
JSON, and may hallucinate parameters not defined by your function
schema. Validate the arguments in your code before calling your
function.
The arguments to call the function with, as generated by the
model in JSON format. Note that the model does not always
generate valid JSON, and may hallucinate parameters not
defined by your function schema. Validate the arguments in
your code before calling your function.
The log probability of this token, if it is within the top 20 most likely tokens. Otherwise,
the value -9999.0 is used to signify that the token is very unlikely.
A list of integers representing the UTF-8 bytes representation of the token. Useful in
instances where characters are represented by multiple tokens and their byte representations
must be combined to generate the correct text representation. Can be null if there is no
bytes representation for the token.
List of the most likely tokens and their log probability, at this token position. In rare
cases, there may be fewer than the number of requested top_logprobs returned.
The log probability of this token, if it is within the top 20 most likely tokens.
Otherwise, the value -9999.0 is used to signify that the token is very unlikely.
A list of integers representing the UTF-8 bytes representation of the token. Useful
in instances where characters are represented by multiple tokens and their byte
representations must be combined to generate the correct text representation. Can be
null if there is no bytes representation for the token.
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.
An optional field that will only be present when you set stream_options: {'"include_usage": true} in your request. When present, it contains a null value except for the last chunk which contains the token usage statistics for the entire request.
