Optionalfrequency_Optionalfunction_Deprecated in favor of tool_choice.
Controls which (if any) function is called by the model. 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. Specifying a
particular function via {"name": "my_function"} forces the model to call that
function.
none is the default when no functions are present. auto is the default if
functions are present.
OptionalfunctionsDeprecated in favor of tools.
A list of functions the model may generate JSON inputs for.
Optionallogit_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.
OptionallogprobsWhether 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.
Optionalmax_The maximum number of tokens that can be generated in the chat completion.
The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.
A list of messages comprising the conversation so far. Example Python code.
ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
OptionalnHow 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.
Optionalparallel_Whether to enable parallel function calling during tool use.
Optionalpresence_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.
Optionalresponse_An object specifying the format that the model must output. Compatible with
GPT-4o,
GPT-4o mini,
GPT-4 Turbo and
all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured
Outputs which guarantees the model will match your supplied JSON schema. Learn
more in the
Structured Outputs guide.
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.
OptionalseedThis 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.
Optionalservice_Specifies the latency tier to use for processing the request. This parameter is relevant for customers subscribed to the scale tier service:
When this parameter is set, the response body will include the service_tier
utilized.
OptionalstopUp to 4 sequences where the API will stop generating further tokens.
OptionalstreamIf 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.
Optionalstream_Options for streaming response. Only set this when you set stream: true.
OptionaltemperatureWhat 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.
Optionaltool_Controls which (if any) tool is called by the model. 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. Specifying a particular tool via
{"type": "function", "function": {"name": "my_function"}} forces the model to
call that tool.
none is the default when no tools are present. auto is the default if tools
are present.
OptionaltoolsA 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.
Optionaltop_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.
Optionaltop_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.
OptionaluserA unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
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.