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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import Any, List, Type, Union, Iterable, Optional, cast
from functools import partial
from typing_extensions import Literal, overload
import httpx
from ... import _legacy_response
from ..._types import NOT_GIVEN, Body, Query, Headers, NoneType, NotGiven
from ..._utils import is_given, 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 .input_items import (
InputItems,
AsyncInputItems,
InputItemsWithRawResponse,
AsyncInputItemsWithRawResponse,
InputItemsWithStreamingResponse,
AsyncInputItemsWithStreamingResponse,
)
from ..._streaming import Stream, AsyncStream
from ...lib._tools import PydanticFunctionTool, ResponsesPydanticFunctionTool
from ..._base_client import make_request_options
from ...types.responses import response_create_params, response_retrieve_params
from ...lib._parsing._responses import (
TextFormatT,
parse_response,
type_to_text_format_param as _type_to_text_format_param,
)
from ...types.shared.chat_model import ChatModel
from ...types.responses.response import Response
from ...types.responses.tool_param import ToolParam, ParseableToolParam
from ...types.shared_params.metadata import Metadata
from ...types.shared_params.reasoning import Reasoning
from ...types.responses.parsed_response import ParsedResponse
from ...lib.streaming.responses._responses import ResponseStreamManager, AsyncResponseStreamManager
from ...types.responses.response_includable import ResponseIncludable
from ...types.shared_params.responses_model import ResponsesModel
from ...types.responses.response_input_param import ResponseInputParam
from ...types.responses.response_stream_event import ResponseStreamEvent
from ...types.responses.response_text_config_param import ResponseTextConfigParam
__all__ = ["Responses", "AsyncResponses"]
class Responses(SyncAPIResource):
@cached_property
def input_items(self) -> InputItems:
return InputItems(self._client)
@cached_property
def with_raw_response(self) -> ResponsesWithRawResponse:
"""
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 ResponsesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> ResponsesWithStreamingResponse:
"""
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 ResponsesWithStreamingResponse(self)
@overload
def create(
self,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
stream: Literal[True],
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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[ResponseStreamEvent]:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
stream: bool,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response | Stream[ResponseStreamEvent]:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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(["input", "model"], ["input", "model", "stream"])
def create(
self,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response | Stream[ResponseStreamEvent]:
return self._post(
"/responses",
body=maybe_transform(
{
"input": input,
"model": model,
"include": include,
"instructions": instructions,
"max_output_tokens": max_output_tokens,
"metadata": metadata,
"parallel_tool_calls": parallel_tool_calls,
"previous_response_id": previous_response_id,
"reasoning": reasoning,
"service_tier": service_tier,
"store": store,
"stream": stream,
"temperature": temperature,
"text": text,
"tool_choice": tool_choice,
"tools": tools,
"top_p": top_p,
"truncation": truncation,
"user": user,
},
response_create_params.ResponseCreateParamsStreaming
if stream
else response_create_params.ResponseCreateParamsNonStreaming,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Response,
stream=stream or False,
stream_cls=Stream[ResponseStreamEvent],
)
def stream(
self,
*,
input: Union[str, ResponseInputParam],
model: Union[str, ChatModel],
text_format: type[TextFormatT] | NotGiven = NOT_GIVEN,
tools: Iterable[ParseableToolParam] | NotGiven = NOT_GIVEN,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> ResponseStreamManager[TextFormatT]:
if is_given(text_format):
if not text:
text = {}
if "format" in text:
raise TypeError("Cannot mix and match text.format with text_format")
text["format"] = _type_to_text_format_param(text_format)
tools = _make_tools(tools)
api_request: partial[Stream[ResponseStreamEvent]] = partial(
self.create,
input=input,
model=model,
tools=tools,
include=include,
instructions=instructions,
max_output_tokens=max_output_tokens,
metadata=metadata,
parallel_tool_calls=parallel_tool_calls,
previous_response_id=previous_response_id,
store=store,
stream=True,
temperature=temperature,
text=text,
tool_choice=tool_choice,
reasoning=reasoning,
top_p=top_p,
truncation=truncation,
user=user,
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
)
return ResponseStreamManager(
api_request,
text_format=text_format,
input_tools=tools,
)
def parse(
self,
*,
input: Union[str, ResponseInputParam],
model: Union[str, ChatModel],
text_format: type[TextFormatT] | NotGiven = NOT_GIVEN,
tools: Iterable[ParseableToolParam] | NotGiven = NOT_GIVEN,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> ParsedResponse[TextFormatT]:
if is_given(text_format):
if not text:
text = {}
if "format" in text:
raise TypeError("Cannot mix and match text.format with text_format")
text["format"] = _type_to_text_format_param(text_format)
tools = _make_tools(tools)
def parser(raw_response: Response) -> ParsedResponse[TextFormatT]:
return parse_response(
input_tools=tools,
text_format=text_format,
response=raw_response,
)
return self._post(
"/responses",
body=maybe_transform(
{
"input": input,
"model": model,
"include": include,
"instructions": instructions,
"max_output_tokens": max_output_tokens,
"metadata": metadata,
"parallel_tool_calls": parallel_tool_calls,
"previous_response_id": previous_response_id,
"reasoning": reasoning,
"store": store,
"stream": stream,
"temperature": temperature,
"text": text,
"tool_choice": tool_choice,
"tools": tools,
"top_p": top_p,
"truncation": truncation,
"user": user,
},
response_create_params.ResponseCreateParams,
),
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
post_parser=parser,
),
# we turn the `Response` instance into a `ParsedResponse`
# in the `parser` function above
cast_to=cast(Type[ParsedResponse[TextFormatT]], Response),
)
def retrieve(
self,
response_id: str,
*,
include: List[ResponseIncludable] | 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,
) -> Response:
"""
Retrieves a model response with the given ID.
Args:
include: Additional fields to include in the response. See the `include` parameter for
Response creation above for more information.
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 response_id:
raise ValueError(f"Expected a non-empty value for `response_id` but received {response_id!r}")
return self._get(
f"/responses/{response_id}",
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=maybe_transform({"include": include}, response_retrieve_params.ResponseRetrieveParams),
),
cast_to=Response,
)
def delete(
self,
response_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,
) -> None:
"""
Deletes a model response with the given ID.
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 response_id:
raise ValueError(f"Expected a non-empty value for `response_id` but received {response_id!r}")
extra_headers = {"Accept": "*/*", **(extra_headers or {})}
return self._delete(
f"/responses/{response_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=NoneType,
)
class AsyncResponses(AsyncAPIResource):
@cached_property
def input_items(self) -> AsyncInputItems:
return AsyncInputItems(self._client)
@cached_property
def with_raw_response(self) -> AsyncResponsesWithRawResponse:
"""
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 AsyncResponsesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncResponsesWithStreamingResponse:
"""
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 AsyncResponsesWithStreamingResponse(self)
@overload
async def create(
self,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
stream: Literal[True],
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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[ResponseStreamEvent]:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
stream: bool,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response | AsyncStream[ResponseStreamEvent]:
"""Creates a model response.
Provide
[text](https://platform.openai.com/docs/guides/text) or
[image](https://platform.openai.com/docs/guides/images) inputs to generate
[text](https://platform.openai.com/docs/guides/text) or
[JSON](https://platform.openai.com/docs/guides/structured-outputs) outputs. Have
the model call your own
[custom code](https://platform.openai.com/docs/guides/function-calling) or use
built-in [tools](https://platform.openai.com/docs/guides/tools) like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search) to use
your own data as input for the model's response.
Args:
input: Text, image, or file inputs to the model, used to generate a response.
Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Image inputs](https://platform.openai.com/docs/guides/images)
- [File inputs](https://platform.openai.com/docs/guides/pdf-files)
- [Conversation state](https://platform.openai.com/docs/guides/conversation-state)
- [Function calling](https://platform.openai.com/docs/guides/function-calling)
model: Model ID used to generate the response, like `gpt-4o` or `o3`. OpenAI offers a
wide range of models with different capabilities, performance characteristics,
and price points. Refer to the
[model guide](https://platform.openai.com/docs/models) to browse and compare
available models.
stream: If set to true, the model response data will be streamed to the client as it is
generated using
[server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).
See the
[Streaming section below](https://platform.openai.com/docs/api-reference/responses-streaming)
for more information.
include: Specify additional output data to include in the model response. Currently
supported values are:
- `file_search_call.results`: Include the search results of the file search tool
call.
- `message.input_image.image_url`: Include image urls from the input message.
- `computer_call_output.output.image_url`: Include image urls from the computer
call output.
instructions: Inserts a system (or developer) message as the first item in the model's
context.
When using along with `previous_response_id`, the instructions from a previous
response will not be carried over to the next response. This makes it simple to
swap out system (or developer) messages in new responses.
max_output_tokens: An upper bound for the number of tokens that can be generated for a response,
including visible output tokens and
[reasoning tokens](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.
parallel_tool_calls: Whether to allow the model to run tool calls in parallel.
previous_response_id: The unique ID of the previous response to the model. Use this to create
multi-turn conversations. Learn more about
[conversation state](https://platform.openai.com/docs/guides/conversation-state).
reasoning: **o-series models only**
Configuration options for
[reasoning models](https://platform.openai.com/docs/guides/reasoning).
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 guarentee.
- If set to 'default', the request will be processed using the default service
tier with a lower uptime SLA and no latency guarentee.
- If set to 'flex', the request will be processed with the Flex Processing
service tier.
[Learn more](https://platform.openai.com/docs/guides/flex-processing).
- When not set, the default behavior is 'auto'.
When this parameter is set, the response body will include the `service_tier`
utilized.
store: Whether to store the generated model response for later retrieval via API.
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.
text: Configuration options for a text response from the model. Can be plain text or
structured JSON data. Learn more:
- [Text inputs and outputs](https://platform.openai.com/docs/guides/text)
- [Structured Outputs](https://platform.openai.com/docs/guides/structured-outputs)
tool_choice: How the model should select which tool (or tools) to use when generating a
response. See the `tools` parameter to see how to specify which tools the model
can call.
tools: An array of tools the model may call while generating a response. You can
specify which tool to use by setting the `tool_choice` parameter.
The two categories of tools you can provide the model are:
- **Built-in tools**: Tools that are provided by OpenAI that extend the model's
capabilities, like
[web search](https://platform.openai.com/docs/guides/tools-web-search) or
[file search](https://platform.openai.com/docs/guides/tools-file-search).
Learn more about
[built-in tools](https://platform.openai.com/docs/guides/tools).
- **Function calls (custom tools)**: Functions that are defined by you, enabling
the model to call your own code. Learn more about
[function calling](https://platform.openai.com/docs/guides/function-calling).
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.
truncation: The truncation strategy to use for the model response.
- `auto`: If the context of this response and previous ones exceeds the model's
context window size, the model will truncate the response to fit the context
window by dropping input items in the middle of the conversation.
- `disabled` (default): If a model response will exceed the context window size
for a model, the request will fail with a 400 error.
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(["input", "model"], ["input", "model", "stream"])
async def create(
self,
*,
input: Union[str, ResponseInputParam],
model: ResponsesModel,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
service_tier: Optional[Literal["auto", "default", "flex"]] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
tools: Iterable[ToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> Response | AsyncStream[ResponseStreamEvent]:
return await self._post(
"/responses",
body=await async_maybe_transform(
{
"input": input,
"model": model,
"include": include,
"instructions": instructions,
"max_output_tokens": max_output_tokens,
"metadata": metadata,
"parallel_tool_calls": parallel_tool_calls,
"previous_response_id": previous_response_id,
"reasoning": reasoning,
"service_tier": service_tier,
"store": store,
"stream": stream,
"temperature": temperature,
"text": text,
"tool_choice": tool_choice,
"tools": tools,
"top_p": top_p,
"truncation": truncation,
"user": user,
},
response_create_params.ResponseCreateParamsStreaming
if stream
else response_create_params.ResponseCreateParamsNonStreaming,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Response,
stream=stream or False,
stream_cls=AsyncStream[ResponseStreamEvent],
)
def stream(
self,
*,
input: Union[str, ResponseInputParam],
model: Union[str, ChatModel],
text_format: type[TextFormatT] | NotGiven = NOT_GIVEN,
tools: Iterable[ParseableToolParam] | NotGiven = NOT_GIVEN,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> AsyncResponseStreamManager[TextFormatT]:
if is_given(text_format):
if not text:
text = {}
if "format" in text:
raise TypeError("Cannot mix and match text.format with text_format")
text["format"] = _type_to_text_format_param(text_format)
tools = _make_tools(tools)
api_request = self.create(
input=input,
model=model,
tools=tools,
include=include,
instructions=instructions,
max_output_tokens=max_output_tokens,
metadata=metadata,
parallel_tool_calls=parallel_tool_calls,
previous_response_id=previous_response_id,
store=store,
stream=True,
temperature=temperature,
text=text,
tool_choice=tool_choice,
reasoning=reasoning,
top_p=top_p,
truncation=truncation,
user=user,
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
)
return AsyncResponseStreamManager(
api_request,
text_format=text_format,
input_tools=tools,
)
async def parse(
self,
*,
input: Union[str, ResponseInputParam],
model: Union[str, ChatModel],
text_format: type[TextFormatT] | NotGiven = NOT_GIVEN,
tools: Iterable[ParseableToolParam] | NotGiven = NOT_GIVEN,
include: Optional[List[ResponseIncludable]] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_output_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
parallel_tool_calls: Optional[bool] | NotGiven = NOT_GIVEN,
previous_response_id: Optional[str] | NotGiven = NOT_GIVEN,
reasoning: Optional[Reasoning] | NotGiven = NOT_GIVEN,
store: Optional[bool] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
text: ResponseTextConfigParam | NotGiven = NOT_GIVEN,
tool_choice: response_create_params.ToolChoice | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation: Optional[Literal["auto", "disabled"]] | 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,
) -> ParsedResponse[TextFormatT]:
if is_given(text_format):
if not text:
text = {}
if "format" in text:
raise TypeError("Cannot mix and match text.format with text_format")
text["format"] = _type_to_text_format_param(text_format)
tools = _make_tools(tools)
def parser(raw_response: Response) -> ParsedResponse[TextFormatT]:
return parse_response(
input_tools=tools,
text_format=text_format,
response=raw_response,
)
return await self._post(
"/responses",
body=maybe_transform(
{
"input": input,
"model": model,
"include": include,
"instructions": instructions,
"max_output_tokens": max_output_tokens,
"metadata": metadata,
"parallel_tool_calls": parallel_tool_calls,
"previous_response_id": previous_response_id,
"reasoning": reasoning,
"store": store,
"stream": stream,
"temperature": temperature,
"text": text,
"tool_choice": tool_choice,
"tools": tools,
"top_p": top_p,
"truncation": truncation,
"user": user,
},
response_create_params.ResponseCreateParams,
),
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
post_parser=parser,
),
# we turn the `Response` instance into a `ParsedResponse`
# in the `parser` function above
cast_to=cast(Type[ParsedResponse[TextFormatT]], Response),
)
async def retrieve(
self,
response_id: str,
*,
include: List[ResponseIncludable] | 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,
) -> Response:
"""
Retrieves a model response with the given ID.
Args:
include: Additional fields to include in the response. See the `include` parameter for
Response creation above for more information.
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 response_id:
raise ValueError(f"Expected a non-empty value for `response_id` but received {response_id!r}")
return await self._get(
f"/responses/{response_id}",
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=await async_maybe_transform(
{"include": include}, response_retrieve_params.ResponseRetrieveParams
),
),
cast_to=Response,
)
async def delete(
self,
response_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,
) -> None:
"""
Deletes a model response with the given ID.
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 response_id:
raise ValueError(f"Expected a non-empty value for `response_id` but received {response_id!r}")
extra_headers = {"Accept": "*/*", **(extra_headers or {})}
return await self._delete(
f"/responses/{response_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=NoneType,
)
class ResponsesWithRawResponse:
def __init__(self, responses: Responses) -> None:
self._responses = responses
self.create = _legacy_response.to_raw_response_wrapper(
responses.create,
)
self.retrieve = _legacy_response.to_raw_response_wrapper(
responses.retrieve,
)
self.delete = _legacy_response.to_raw_response_wrapper(
responses.delete,
)
@cached_property
def input_items(self) -> InputItemsWithRawResponse:
return InputItemsWithRawResponse(self._responses.input_items)
class AsyncResponsesWithRawResponse:
def __init__(self, responses: AsyncResponses) -> None:
self._responses = responses
self.create = _legacy_response.async_to_raw_response_wrapper(
responses.create,
)
self.retrieve = _legacy_response.async_to_raw_response_wrapper(
responses.retrieve,
)
self.delete = _legacy_response.async_to_raw_response_wrapper(
responses.delete,
)
@cached_property
def input_items(self) -> AsyncInputItemsWithRawResponse:
return AsyncInputItemsWithRawResponse(self._responses.input_items)
class ResponsesWithStreamingResponse:
def __init__(self, responses: Responses) -> None:
self._responses = responses
self.create = to_streamed_response_wrapper(
responses.create,
)
self.retrieve = to_streamed_response_wrapper(
responses.retrieve,
)
self.delete = to_streamed_response_wrapper(
responses.delete,
)
@cached_property
def input_items(self) -> InputItemsWithStreamingResponse:
return InputItemsWithStreamingResponse(self._responses.input_items)
class AsyncResponsesWithStreamingResponse:
def __init__(self, responses: AsyncResponses) -> None:
self._responses = responses
self.create = async_to_streamed_response_wrapper(
responses.create,
)
self.retrieve = async_to_streamed_response_wrapper(
responses.retrieve,
)
self.delete = async_to_streamed_response_wrapper(
responses.delete,
)
@cached_property
def input_items(self) -> AsyncInputItemsWithStreamingResponse:
return AsyncInputItemsWithStreamingResponse(self._responses.input_items)
def _make_tools(tools: Iterable[ParseableToolParam] | NotGiven) -> List[ToolParam] | NotGiven:
if not is_given(tools):
return NOT_GIVEN
converted_tools: List[ToolParam] = []
for tool in tools:
if tool["type"] != "function":
converted_tools.append(tool)
continue
if "function" not in tool:
# standard Responses API case
converted_tools.append(tool)
continue
function = cast(Any, tool)["function"] # pyright: ignore[reportUnnecessaryCast]
if not isinstance(function, PydanticFunctionTool):
raise Exception(
"Expected Chat Completions function tool shape to be created using `openai.pydantic_function_tool()`"
)
assert "parameters" in function
new_tool = ResponsesPydanticFunctionTool(
{
"type": "function",
"name": function["name"],
"description": function.get("description"),
"parameters": function["parameters"],
"strict": function.get("strict") or False,
},
function.model,
)
converted_tools.append(new_tool.cast())
return converted_tools
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