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ai-content-maker/.venv/Lib/site-packages/openai/resources/chat/completions.py

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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import Dict, List, Union, Iterable, Optional, overload
from typing_extensions import Literal
import httpx
from ... import _legacy_response
from ..._types import NOT_GIVEN, Body, Query, Headers, NotGiven
from ..._utils import (
required_args,
maybe_transform,
async_maybe_transform,
)
from ..._compat import cached_property
from ..._resource import SyncAPIResource, AsyncAPIResource
from ..._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
from ..._streaming import Stream, AsyncStream
from ...types.chat import completion_create_params
from ..._base_client import (
make_request_options,
)
from ...types.chat_model import ChatModel
from ...types.chat.chat_completion import ChatCompletion
from ...types.chat.chat_completion_chunk import ChatCompletionChunk
from ...types.chat.chat_completion_tool_param import ChatCompletionToolParam
from ...types.chat.chat_completion_message_param import ChatCompletionMessageParam
from ...types.chat.chat_completion_tool_choice_option_param import ChatCompletionToolChoiceOptionParam
__all__ = ["Completions", "AsyncCompletions"]
class Completions(SyncAPIResource):
@cached_property
def with_raw_response(self) -> CompletionsWithRawResponse:
return CompletionsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> CompletionsWithStreamingResponse:
return CompletionsWithStreamingResponse(self)
@overload
def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@overload
def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
stream: Literal[True],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> Stream[ChatCompletionChunk]:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@overload
def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
stream: bool,
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion | Stream[ChatCompletionChunk]:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@required_args(["messages", "model"], ["messages", "model", "stream"])
def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion | Stream[ChatCompletionChunk]:
return self._post(
"/chat/completions",
body=maybe_transform(
{
"messages": messages,
"model": model,
"frequency_penalty": frequency_penalty,
"function_call": function_call,
"functions": functions,
"logit_bias": logit_bias,
"logprobs": logprobs,
"max_tokens": max_tokens,
"n": n,
"presence_penalty": presence_penalty,
"response_format": response_format,
"seed": seed,
"stop": stop,
"stream": stream,
"temperature": temperature,
"tool_choice": tool_choice,
"tools": tools,
"top_logprobs": top_logprobs,
"top_p": top_p,
"user": user,
},
completion_create_params.CompletionCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=ChatCompletion,
stream=stream or False,
stream_cls=Stream[ChatCompletionChunk],
)
class AsyncCompletions(AsyncAPIResource):
@cached_property
def with_raw_response(self) -> AsyncCompletionsWithRawResponse:
return AsyncCompletionsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncCompletionsWithStreamingResponse:
return AsyncCompletionsWithStreamingResponse(self)
@overload
async def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@overload
async def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
stream: Literal[True],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> AsyncStream[ChatCompletionChunk]:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@overload
async def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
stream: bool,
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion | AsyncStream[ChatCompletionChunk]:
"""
Creates a model response for the given chat conversation.
Args:
messages: A list of messages comprising the conversation so far.
[Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
model: ID of the model to use. See the
[model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility)
table for details on which models work with the Chat API.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be
sent as data-only
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
as they become available, with the stream terminated by a `data: [DONE]`
message.
[Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
frequency_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
function_call: 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.
functions: Deprecated in favor of `tools`.
A list of functions the model may generate JSON inputs for.
logit_bias: 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.
logprobs: 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`.
max_tokens: The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
for counting tokens.
n: 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.
presence_penalty: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
response_format: An object specifying the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.
seed: 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.
stop: Up to 4 sequences where the API will stop generating further tokens.
temperature: 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.
tool_choice: 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.
tools: 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.
top_logprobs: 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.
top_p: 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: A unique identifier representing your end-user, which can help OpenAI to monitor
and detect abuse.
[Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
...
@required_args(["messages", "model"], ["messages", "model", "stream"])
async def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
function_call: completion_create_params.FunctionCall | NotGiven = NOT_GIVEN,
functions: Iterable[completion_create_params.Function] | NotGiven = NOT_GIVEN,
logit_bias: Optional[Dict[str, int]] | NotGiven = NOT_GIVEN,
logprobs: Optional[bool] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN,
tools: Iterable[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
top_logprobs: Optional[int] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
user: str | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> ChatCompletion | AsyncStream[ChatCompletionChunk]:
return await self._post(
"/chat/completions",
body=await async_maybe_transform(
{
"messages": messages,
"model": model,
"frequency_penalty": frequency_penalty,
"function_call": function_call,
"functions": functions,
"logit_bias": logit_bias,
"logprobs": logprobs,
"max_tokens": max_tokens,
"n": n,
"presence_penalty": presence_penalty,
"response_format": response_format,
"seed": seed,
"stop": stop,
"stream": stream,
"temperature": temperature,
"tool_choice": tool_choice,
"tools": tools,
"top_logprobs": top_logprobs,
"top_p": top_p,
"user": user,
},
completion_create_params.CompletionCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=ChatCompletion,
stream=stream or False,
stream_cls=AsyncStream[ChatCompletionChunk],
)
class CompletionsWithRawResponse:
def __init__(self, completions: Completions) -> None:
self._completions = completions
self.create = _legacy_response.to_raw_response_wrapper(
completions.create,
)
class AsyncCompletionsWithRawResponse:
def __init__(self, completions: AsyncCompletions) -> None:
self._completions = completions
self.create = _legacy_response.async_to_raw_response_wrapper(
completions.create,
)
class CompletionsWithStreamingResponse:
def __init__(self, completions: Completions) -> None:
self._completions = completions
self.create = to_streamed_response_wrapper(
completions.create,
)
class AsyncCompletionsWithStreamingResponse:
def __init__(self, completions: AsyncCompletions) -> None:
self._completions = completions
self.create = async_to_streamed_response_wrapper(
completions.create,
)