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completions.py
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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
import inspect
from typing import Dict, List, Union, Iterable, Optional
from typing_extensions import Literal, overload
import httpx
import pydantic
from .... import _legacy_response
from .messages import (
Messages,
AsyncMessages,
MessagesWithRawResponse,
AsyncMessagesWithRawResponse,
MessagesWithStreamingResponse,
AsyncMessagesWithStreamingResponse,
)
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 ....pagination import SyncCursorPage, AsyncCursorPage
from ....types.chat import (
ChatCompletionAudioParam,
ChatCompletionReasoningEffort,
completion_list_params,
completion_create_params,
completion_update_params,
)
from ...._base_client import AsyncPaginator, make_request_options
from ....types.chat_model import ChatModel
from ....types.chat.chat_completion import ChatCompletion
from ....types.shared_params.metadata import Metadata
from ....types.chat.chat_completion_chunk import ChatCompletionChunk
from ....types.chat.chat_completion_deleted import ChatCompletionDeleted
from ....types.chat.chat_completion_modality import ChatCompletionModality
from ....types.chat.chat_completion_tool_param import ChatCompletionToolParam
from ....types.chat.chat_completion_audio_param import ChatCompletionAudioParam
from ....types.chat.chat_completion_message_param import ChatCompletionMessageParam
from ....types.chat.chat_completion_reasoning_effort import ChatCompletionReasoningEffort
from ....types.chat.chat_completion_stream_options_param import ChatCompletionStreamOptionsParam
from ....types.chat.chat_completion_prediction_content_param import ChatCompletionPredictionContentParam
from ....types.chat.chat_completion_tool_choice_option_param import ChatCompletionToolChoiceOptionParam
__all__ = ["Completions", "AsyncCompletions"]
class Completions(SyncAPIResource):
@cached_property
def messages(self) -> Messages:
return Messages(self._client)
@cached_property
def with_raw_response(self) -> CompletionsWithRawResponse:
"""
This property can be used as a prefix for any HTTP method call to return
the raw response object instead of the parsed content.
For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers
"""
return CompletionsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> CompletionsWithStreamingResponse:
"""
An alternative to `.with_raw_response` that doesn't eagerly read the response body.
For more information, see https://www.github.com/openai/openai-python#with_streaming_response
"""
return CompletionsWithStreamingResponse(self)
@overload
def create(
self,
*,
messages: Iterable[ChatCompletionMessageParam],
model: Union[str, ChatModel],
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ChatCompletionReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
stream_options: Optional[ChatCompletionStreamOptionsParam] | 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.
Learn more in the
[text generation](https://platform.openai.com/docs/guides/text-generation),
[vision](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio) guides.
Parameter support can differ depending on the model used to generate the
response, particularly for newer reasoning models. Parameters that are only
supported for reasoning models are noted below. For the current state of
unsupported parameters in reasoning models,
[refer to the reasoning guide](https://platform.openai.com/docs/guides/reasoning).
Args:
messages: A list of messages comprising the conversation so far. Depending on the
[model](https://platform.openai.com/docs/models) you use, different message
types (modalities) are supported, like
[text](https://platform.openai.com/docs/guides/text-generation),
[images](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio).
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.
audio: Parameters for audio output. Required when audio output is requested with
`modalities: ["audio"]`.
[Learn more](https://platform.openai.com/docs/guides/audio).
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.
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_completion_tokens: An upper bound for the number of tokens that can be generated for a completion,
including visible output tokens and
[reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the chat
completion. This value can be used to control
[costs](https://openai.com/api/pricing/) for text generated via API.
This value is now deprecated in favor of `max_completion_tokens`, and is not
compatible with
[o1 series models](https://platform.openai.com/docs/guides/reasoning).
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with
a maximum length of 512 characters.
modalities: Output types that you would like the model to generate for this request. Most
models are capable of generating text, which is the default:
`["text"]`
The `gpt-4o-audio-preview` model can also be used to
[generate audio](https://platform.openai.com/docs/guides/audio). To request that
this model generate both text and audio responses, you can use:
`["text", "audio"]`
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.
parallel_tool_calls: Whether to enable
[parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
during tool use.
prediction: Static predicted output content, such as the content of a text file that is
being regenerated.
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.
reasoning_effort: **o1 and o3-mini models only**
Constrains effort on reasoning for
[reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
supported values are `low`, `medium`, and `high`. Reducing reasoning effort can
result in faster responses and fewer tokens used on reasoning in a response.
response_format: An object specifying the format that the model must output.
Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
Outputs which ensures the model will match your supplied JSON schema. Learn more
in the
[Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
Setting to `{ "type": "json_object" }` enables JSON mode, which ensures 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.
service_tier: Specifies the latency tier to use for processing the request. This parameter is
relevant for customers subscribed to the scale tier service:
- If set to 'auto', and the Project is Scale tier enabled, the system will
utilize scale tier credits until they are exhausted.
- If set to 'auto', and the Project is not Scale tier enabled, the request will
be processed using the default service tier with a lower uptime SLA and no
latency guarantee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarantee.
- When not set, the default behavior is 'auto'.
stop: Up to 4 sequences where the API will stop generating further tokens.
store: Whether or not to store the output of this chat completion request for use in
our [model distillation](https://platform.openai.com/docs/guides/distillation)
or [evals](https://platform.openai.com/docs/guides/evals) products.
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).
stream_options: Options for streaming response. Only set this when you set `stream: true`.
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],
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ChatCompletionReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream_options: Optional[ChatCompletionStreamOptionsParam] | 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.
Learn more in the
[text generation](https://platform.openai.com/docs/guides/text-generation),
[vision](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio) guides.
Parameter support can differ depending on the model used to generate the
response, particularly for newer reasoning models. Parameters that are only
supported for reasoning models are noted below. For the current state of
unsupported parameters in reasoning models,
[refer to the reasoning guide](https://platform.openai.com/docs/guides/reasoning).
Args:
messages: A list of messages comprising the conversation so far. Depending on the
[model](https://platform.openai.com/docs/models) you use, different message
types (modalities) are supported, like
[text](https://platform.openai.com/docs/guides/text-generation),
[images](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio).
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).
audio: Parameters for audio output. Required when audio output is requested with
`modalities: ["audio"]`.
[Learn more](https://platform.openai.com/docs/guides/audio).
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.
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_completion_tokens: An upper bound for the number of tokens that can be generated for a completion,
including visible output tokens and
[reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the chat
completion. This value can be used to control
[costs](https://openai.com/api/pricing/) for text generated via API.
This value is now deprecated in favor of `max_completion_tokens`, and is not
compatible with
[o1 series models](https://platform.openai.com/docs/guides/reasoning).
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with
a maximum length of 512 characters.
modalities: Output types that you would like the model to generate for this request. Most
models are capable of generating text, which is the default:
`["text"]`
The `gpt-4o-audio-preview` model can also be used to
[generate audio](https://platform.openai.com/docs/guides/audio). To request that
this model generate both text and audio responses, you can use:
`["text", "audio"]`
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.
parallel_tool_calls: Whether to enable
[parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
during tool use.
prediction: Static predicted output content, such as the content of a text file that is
being regenerated.
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.
reasoning_effort: **o1 and o3-mini models only**
Constrains effort on reasoning for
[reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
supported values are `low`, `medium`, and `high`. Reducing reasoning effort can
result in faster responses and fewer tokens used on reasoning in a response.
response_format: An object specifying the format that the model must output.
Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
Outputs which ensures the model will match your supplied JSON schema. Learn more
in the
[Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
Setting to `{ "type": "json_object" }` enables JSON mode, which ensures 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.
service_tier: Specifies the latency tier to use for processing the request. This parameter is
relevant for customers subscribed to the scale tier service:
- If set to 'auto', and the Project is Scale tier enabled, the system will
utilize scale tier credits until they are exhausted.
- If set to 'auto', and the Project is not Scale tier enabled, the request will
be processed using the default service tier with a lower uptime SLA and no
latency guarantee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarantee.
- When not set, the default behavior is 'auto'.
stop: Up to 4 sequences where the API will stop generating further tokens.
store: Whether or not to store the output of this chat completion request for use in
our [model distillation](https://platform.openai.com/docs/guides/distillation)
or [evals](https://platform.openai.com/docs/guides/evals) products.
stream_options: Options for streaming response. Only set this when you set `stream: true`.
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,
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ChatCompletionReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream_options: Optional[ChatCompletionStreamOptionsParam] | 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.
Learn more in the
[text generation](https://platform.openai.com/docs/guides/text-generation),
[vision](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio) guides.
Parameter support can differ depending on the model used to generate the
response, particularly for newer reasoning models. Parameters that are only
supported for reasoning models are noted below. For the current state of
unsupported parameters in reasoning models,
[refer to the reasoning guide](https://platform.openai.com/docs/guides/reasoning).
Args:
messages: A list of messages comprising the conversation so far. Depending on the
[model](https://platform.openai.com/docs/models) you use, different message
types (modalities) are supported, like
[text](https://platform.openai.com/docs/guides/text-generation),
[images](https://platform.openai.com/docs/guides/vision), and
[audio](https://platform.openai.com/docs/guides/audio).
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).
audio: Parameters for audio output. Required when audio output is requested with
`modalities: ["audio"]`.
[Learn more](https://platform.openai.com/docs/guides/audio).
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.
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_completion_tokens: An upper bound for the number of tokens that can be generated for a completion,
including visible output tokens and
[reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the chat
completion. This value can be used to control
[costs](https://openai.com/api/pricing/) for text generated via API.
This value is now deprecated in favor of `max_completion_tokens`, and is not
compatible with
[o1 series models](https://platform.openai.com/docs/guides/reasoning).
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with
a maximum length of 512 characters.
modalities: Output types that you would like the model to generate for this request. Most
models are capable of generating text, which is the default:
`["text"]`
The `gpt-4o-audio-preview` model can also be used to
[generate audio](https://platform.openai.com/docs/guides/audio). To request that
this model generate both text and audio responses, you can use:
`["text", "audio"]`
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.
parallel_tool_calls: Whether to enable
[parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling)
during tool use.
prediction: Static predicted output content, such as the content of a text file that is
being regenerated.
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.
reasoning_effort: **o1 and o3-mini models only**
Constrains effort on reasoning for
[reasoning models](https://platform.openai.com/docs/guides/reasoning). Currently
supported values are `low`, `medium`, and `high`. Reducing reasoning effort can
result in faster responses and fewer tokens used on reasoning in a response.
response_format: An object specifying the format that the model must output.
Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured
Outputs which ensures the model will match your supplied JSON schema. Learn more
in the
[Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
Setting to `{ "type": "json_object" }` enables JSON mode, which ensures 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.
service_tier: Specifies the latency tier to use for processing the request. This parameter is
relevant for customers subscribed to the scale tier service:
- If set to 'auto', and the Project is Scale tier enabled, the system will
utilize scale tier credits until they are exhausted.
- If set to 'auto', and the Project is not Scale tier enabled, the request will
be processed using the default service tier with a lower uptime SLA and no
latency guarantee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarantee.
- When not set, the default behavior is 'auto'.
stop: Up to 4 sequences where the API will stop generating further tokens.
store: Whether or not to store the output of this chat completion request for use in
our [model distillation](https://platform.openai.com/docs/guides/distillation)
or [evals](https://platform.openai.com/docs/guides/evals) products.
stream_options: Options for streaming response. Only set this when you set `stream: true`.
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],
audio: Optional[ChatCompletionAudioParam] | NotGiven = NOT_GIVEN,
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_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
modalities: Optional[List[ChatCompletionModality]] | NotGiven = NOT_GIVEN,
n: Optional[int] | NotGiven = NOT_GIVEN,
parallel_tool_calls: bool | NotGiven = NOT_GIVEN,
prediction: Optional[ChatCompletionPredictionContentParam] | NotGiven = NOT_GIVEN,
presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ChatCompletionReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: completion_create_params.ResponseFormat | NotGiven = NOT_GIVEN,
seed: Optional[int] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default"]] | NotGiven = NOT_GIVEN,
stop: Union[Optional[str], List[str]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
stream_options: Optional[ChatCompletionStreamOptionsParam] | 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]:
validate_response_format(response_format)
return self._post(
"/chat/completions",
body=maybe_transform(
{
"messages": messages,
"model": model,
"audio": audio,
"frequency_penalty": frequency_penalty,
"function_call": function_call,
"functions": functions,
"logit_bias": logit_bias,
"logprobs": logprobs,
"max_completion_tokens": max_completion_tokens,
"max_tokens": max_tokens,
"metadata": metadata,
"modalities": modalities,
"n": n,
"parallel_tool_calls": parallel_tool_calls,
"prediction": prediction,
"presence_penalty": presence_penalty,
"reasoning_effort": reasoning_effort,
"response_format": response_format,
"seed": seed,
"service_tier": service_tier,
"stop": stop,
"store": store,
"stream": stream,
"stream_options": stream_options,
"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],
)
def retrieve(
self,
completion_id: str,
*,
# 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:
"""Get a stored chat completion.
Only chat completions that have been created with
the `store` parameter set to `true` will be returned.
Args:
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
"""
if not completion_id:
raise ValueError(f"Expected a non-empty value for `completion_id` but received {completion_id!r}")
return self._get(
f"/chat/completions/{completion_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=ChatCompletion,
)
def update(
self,
completion_id: str,
*,
metadata: Optional[Metadata],
# 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:
"""Modify a stored chat completion.
Only chat completions that have been created
with the `store` parameter set to `true` can be modified. Currently, the only
supported modification is to update the `metadata` field.
Args:
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with
a maximum length of 512 characters.
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
"""
if not completion_id:
raise ValueError(f"Expected a non-empty value for `completion_id` but received {completion_id!r}")
return self._post(
f"/chat/completions/{completion_id}",
body=maybe_transform({"metadata": metadata}, completion_update_params.CompletionUpdateParams),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),