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fix(streaming): add additional overload for ambiguous stream param
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‎src/openai/resources/chat/completions.py

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@@ -271,6 +271,131 @@ def create(
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"""
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...
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@overload
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def create(
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self,
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*,
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messages: List[ChatCompletionMessageParam],
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model: Union[
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str,
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Literal[
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"gpt-4",
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"gpt-4-0314",
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"gpt-4-0613",
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"gpt-4-32k",
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"gpt-4-32k-0314",
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"gpt-4-32k-0613",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-16k",
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"gpt-3.5-turbo-0301",
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"gpt-3.5-turbo-0613",
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"gpt-3.5-turbo-16k-0613",
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],
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],
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stream: bool,
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frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
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functions: List[completion_create_params.Function] | NotGiven = NOT_GIVEN,
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logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
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max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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n: Optional[int] | NotGiven = NOT_GIVEN,
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presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
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temperature: Optional[float] | NotGiven = NOT_GIVEN,
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top_p: Optional[float] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | None | NotGiven = NOT_GIVEN,
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) -> ChatCompletion | Stream[ChatCompletionChunk]:
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"""
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Creates a model response for the given chat conversation.
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Args:
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messages: A list of messages comprising the conversation so far.
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[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
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model: ID of the model to use. See the
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[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
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table for details on which models work with the Chat API.
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stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
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sent as data-only
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[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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as they become available, with the stream terminated by a `data: [DONE]`
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message.
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[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
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existing frequency in the text so far, decreasing the model's likelihood to
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repeat the same line verbatim.
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[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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function_call: Controls how the model calls functions. "none" means the model will not call a
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function and instead generates a message. "auto" means the model can pick
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between generating a message or calling a function. Specifying a particular
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function via `{"name": "my_function"}` forces the model to call that function.
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"none" is the default when no functions are present. "auto" is the default if
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functions are present.
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functions: A list of functions the model may generate JSON inputs for.
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logit_bias: Modify the likelihood of specified tokens appearing in the completion.
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Accepts a json object that maps tokens (specified by their token ID in the
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tokenizer) to an associated bias value from -100 to 100. Mathematically, the
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bias is added to the logits generated by the model prior to sampling. The exact
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effect will vary per model, but values between -1 and 1 should decrease or
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increase likelihood of selection; values like -100 or 100 should result in a ban
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or exclusive selection of the relevant token.
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max_tokens: The maximum number of [tokens](/tokenizer) to generate in the chat completion.
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The total length of input tokens and generated tokens is limited by the model's
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context length.
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[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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for counting tokens.
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n: How many chat completion choices to generate for each input message.
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presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
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whether they appear in the text so far, increasing the model's likelihood to
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talk about new topics.
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[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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stop: Up to 4 sequences where the API will stop generating further tokens.
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temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
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make the output more random, while lower values like 0.2 will make it more
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focused and deterministic.
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We generally recommend altering this or `top_p` but not both.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
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means only the tokens comprising the top 10% probability mass are considered.
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We generally recommend altering this or `temperature` but not both.
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user: A unique identifier representing your end-user, which can help OpenAI to monitor
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and detect abuse.
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[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
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extra_headers: Send extra headers
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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...
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@required_args(["messages", "model"], ["messages", "model", "stream"])
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def create(
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self,
@@ -592,6 +717,131 @@ async def create(
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"""
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...
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@overload
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async def create(
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self,
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*,
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messages: List[ChatCompletionMessageParam],
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model: Union[
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str,
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Literal[
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"gpt-4",
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"gpt-4-0314",
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"gpt-4-0613",
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"gpt-4-32k",
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"gpt-4-32k-0314",
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"gpt-4-32k-0613",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-16k",
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"gpt-3.5-turbo-0301",
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"gpt-3.5-turbo-0613",
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"gpt-3.5-turbo-16k-0613",
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],
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],
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stream: bool,
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frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
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functions: List[completion_create_params.Function] | NotGiven = NOT_GIVEN,
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logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
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max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
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n: Optional[int] | NotGiven = NOT_GIVEN,
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presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
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stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
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temperature: Optional[float] | NotGiven = NOT_GIVEN,
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top_p: Optional[float] | NotGiven = NOT_GIVEN,
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user: str | NotGiven = NOT_GIVEN,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Headers | None = None,
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extra_query: Query | None = None,
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extra_body: Body | None = None,
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timeout: float | None | NotGiven = NOT_GIVEN,
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) -> ChatCompletion | AsyncStream[ChatCompletionChunk]:
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"""
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Creates a model response for the given chat conversation.
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Args:
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messages: A list of messages comprising the conversation so far.
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[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
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model: ID of the model to use. See the
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[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
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table for details on which models work with the Chat API.
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stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
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sent as data-only
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[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
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as they become available, with the stream terminated by a `data: [DONE]`
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message.
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[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
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frequency_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on their
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existing frequency in the text so far, decreasing the model's likelihood to
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repeat the same line verbatim.
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[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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function_call: Controls how the model calls functions. "none" means the model will not call a
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function and instead generates a message. "auto" means the model can pick
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between generating a message or calling a function. Specifying a particular
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function via `{"name": "my_function"}` forces the model to call that function.
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"none" is the default when no functions are present. "auto" is the default if
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functions are present.
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functions: A list of functions the model may generate JSON inputs for.
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logit_bias: Modify the likelihood of specified tokens appearing in the completion.
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Accepts a json object that maps tokens (specified by their token ID in the
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tokenizer) to an associated bias value from -100 to 100. Mathematically, the
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bias is added to the logits generated by the model prior to sampling. The exact
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effect will vary per model, but values between -1 and 1 should decrease or
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increase likelihood of selection; values like -100 or 100 should result in a ban
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or exclusive selection of the relevant token.
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max_tokens: The maximum number of [tokens](/tokenizer) to generate in the chat completion.
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The total length of input tokens and generated tokens is limited by the model's
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context length.
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[Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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for counting tokens.
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n: How many chat completion choices to generate for each input message.
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presence_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens based on
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whether they appear in the text so far, increasing the model's likelihood to
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talk about new topics.
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[See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/gpt/parameter-details)
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stop: Up to 4 sequences where the API will stop generating further tokens.
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temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
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make the output more random, while lower values like 0.2 will make it more
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focused and deterministic.
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We generally recommend altering this or `top_p` but not both.
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top_p: An alternative to sampling with temperature, called nucleus sampling, where the
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model considers the results of the tokens with top_p probability mass. So 0.1
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means only the tokens comprising the top 10% probability mass are considered.
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We generally recommend altering this or `temperature` but not both.
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user: A unique identifier representing your end-user, which can help OpenAI to monitor
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and detect abuse.
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[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
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extra_headers: Send extra headers
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extra_query: Add additional query parameters to the request
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extra_body: Add additional JSON properties to the request
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timeout: Override the client-level default timeout for this request, in seconds
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"""
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...
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@required_args(["messages", "model"], ["messages", "model", "stream"])
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async def create(
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self,

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