# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import Union, Iterable, Optional
from typing_extensions import Literal
import httpx
from ... import _legacy_response
from ..._types import NOT_GIVEN, Body, Query, Headers, NotGiven
from ..._utils import (
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 ...pagination import SyncCursorPage, AsyncCursorPage
from ...types.beta import (
assistant_list_params,
assistant_create_params,
assistant_update_params,
)
from ..._base_client import AsyncPaginator, make_request_options
from ...types.beta.assistant import Assistant
from ...types.shared.chat_model import ChatModel
from ...types.beta.assistant_deleted import AssistantDeleted
from ...types.shared_params.metadata import Metadata
from ...types.shared.reasoning_effort import ReasoningEffort
from ...types.beta.assistant_tool_param import AssistantToolParam
from ...types.beta.assistant_response_format_option_param import AssistantResponseFormatOptionParam
__all__ = ["Assistants", "AsyncAssistants"]
class Assistants(SyncAPIResource):
@cached_property
def with_raw_response(self) -> AssistantsWithRawResponse:
"""
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 AssistantsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AssistantsWithStreamingResponse:
"""
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 AssistantsWithStreamingResponse(self)
def create(
self,
*,
model: Union[str, ChatModel],
description: Optional[str] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
name: Optional[str] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_resources: Optional[assistant_create_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Iterable[AssistantToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | 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,
) -> Assistant:
"""
Create an assistant with a model and instructions.
Args:
model: ID of the model to use. You can use the
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
see all of your available models, or see our
[Model overview](https://platform.openai.com/docs/models) for descriptions of
them.
description: The description of the assistant. The maximum length is 512 characters.
instructions: The system instructions that the assistant uses. The maximum length is 256,000
characters.
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.
name: The name of the assistant. The maximum length is 256 characters.
reasoning_effort: **o-series 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: Specifies the format that the model must output. Compatible with
[GPT-4o](https://platform.openai.com/docs/models#gpt-4o),
[GPT-4 Turbo](https://platform.openai.com/docs/models#gpt-4-turbo-and-gpt-4),
and all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
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.
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.
tool_resources: A set of resources that are used by the assistant's tools. The resources are
specific to the type of tool. For example, the `code_interpreter` tool requires
a list of file IDs, while the `file_search` tool requires a list of vector store
IDs.
tools: A list of tool enabled on the assistant. There can be a maximum of 128 tools per
assistant. Tools can be of types `code_interpreter`, `file_search`, or
`function`.
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.
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
"""
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
"/assistants",
body=maybe_transform(
{
"model": model,
"description": description,
"instructions": instructions,
"metadata": metadata,
"name": name,
"reasoning_effort": reasoning_effort,
"response_format": response_format,
"temperature": temperature,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
},
assistant_create_params.AssistantCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
def retrieve(
self,
assistant_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,
) -> Assistant:
"""
Retrieves an assistant.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get(
f"/assistants/{assistant_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
def update(
self,
assistant_id: str,
*,
description: Optional[str] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"o3-mini",
"o3-mini-2025-01-31",
"o1",
"o1-2024-12-17",
"gpt-4o",
"gpt-4o-2024-11-20",
"gpt-4o-2024-08-06",
"gpt-4o-2024-05-13",
"gpt-4o-mini",
"gpt-4o-mini-2024-07-18",
"gpt-4.5-preview",
"gpt-4.5-preview-2025-02-27",
"gpt-4-turbo",
"gpt-4-turbo-2024-04-09",
"gpt-4-0125-preview",
"gpt-4-turbo-preview",
"gpt-4-1106-preview",
"gpt-4-vision-preview",
"gpt-4",
"gpt-4-0314",
"gpt-4-0613",
"gpt-4-32k",
"gpt-4-32k-0314",
"gpt-4-32k-0613",
"gpt-3.5-turbo",
"gpt-3.5-turbo-16k",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-1106",
"gpt-3.5-turbo-0125",
"gpt-3.5-turbo-16k-0613",
],
]
| NotGiven = NOT_GIVEN,
name: Optional[str] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_resources: Optional[assistant_update_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Iterable[AssistantToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | 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,
) -> Assistant:
"""Modifies an assistant.
Args:
description: The description of the assistant.
The maximum length is 512 characters.
instructions: The system instructions that the assistant uses. The maximum length is 256,000
characters.
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.
model: ID of the model to use. You can use the
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
see all of your available models, or see our
[Model overview](https://platform.openai.com/docs/models) for descriptions of
them.
name: The name of the assistant. The maximum length is 256 characters.
reasoning_effort: **o-series 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: Specifies the format that the model must output. Compatible with
[GPT-4o](https://platform.openai.com/docs/models#gpt-4o),
[GPT-4 Turbo](https://platform.openai.com/docs/models#gpt-4-turbo-and-gpt-4),
and all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
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.
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.
tool_resources: A set of resources that are used by the assistant's tools. The resources are
specific to the type of tool. For example, the `code_interpreter` tool requires
a list of file IDs, while the `file_search` tool requires a list of vector store
IDs.
tools: A list of tool enabled on the assistant. There can be a maximum of 128 tools per
assistant. Tools can be of types `code_interpreter`, `file_search`, or
`function`.
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.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
f"/assistants/{assistant_id}",
body=maybe_transform(
{
"description": description,
"instructions": instructions,
"metadata": metadata,
"model": model,
"name": name,
"reasoning_effort": reasoning_effort,
"response_format": response_format,
"temperature": temperature,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
},
assistant_update_params.AssistantUpdateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
def list(
self,
*,
after: str | NotGiven = NOT_GIVEN,
before: str | NotGiven = NOT_GIVEN,
limit: int | NotGiven = NOT_GIVEN,
order: Literal["asc", "desc"] | 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,
) -> SyncCursorPage[Assistant]:
"""Returns a list of assistants.
Args:
after: A cursor for use in pagination.
`after` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
ending with obj_foo, your subsequent call can include after=obj_foo in order to
fetch the next page of the list.
before: A cursor for use in pagination. `before` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
starting with obj_foo, your subsequent call can include before=obj_foo in order
to fetch the previous page of the list.
limit: A limit on the number of objects to be returned. Limit can range between 1 and
100, and the default is 20.
order: Sort order by the `created_at` timestamp of the objects. `asc` for ascending
order and `desc` for descending order.
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
"""
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get_api_list(
"/assistants",
page=SyncCursorPage[Assistant],
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=maybe_transform(
{
"after": after,
"before": before,
"limit": limit,
"order": order,
},
assistant_list_params.AssistantListParams,
),
),
model=Assistant,
)
def delete(
self,
assistant_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,
) -> AssistantDeleted:
"""
Delete an assistant.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._delete(
f"/assistants/{assistant_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=AssistantDeleted,
)
class AsyncAssistants(AsyncAPIResource):
@cached_property
def with_raw_response(self) -> AsyncAssistantsWithRawResponse:
"""
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 AsyncAssistantsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncAssistantsWithStreamingResponse:
"""
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 AsyncAssistantsWithStreamingResponse(self)
async def create(
self,
*,
model: Union[str, ChatModel],
description: Optional[str] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
name: Optional[str] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_resources: Optional[assistant_create_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Iterable[AssistantToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | 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,
) -> Assistant:
"""
Create an assistant with a model and instructions.
Args:
model: ID of the model to use. You can use the
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
see all of your available models, or see our
[Model overview](https://platform.openai.com/docs/models) for descriptions of
them.
description: The description of the assistant. The maximum length is 512 characters.
instructions: The system instructions that the assistant uses. The maximum length is 256,000
characters.
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.
name: The name of the assistant. The maximum length is 256 characters.
reasoning_effort: **o-series 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: Specifies the format that the model must output. Compatible with
[GPT-4o](https://platform.openai.com/docs/models#gpt-4o),
[GPT-4 Turbo](https://platform.openai.com/docs/models#gpt-4-turbo-and-gpt-4),
and all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
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.
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.
tool_resources: A set of resources that are used by the assistant's tools. The resources are
specific to the type of tool. For example, the `code_interpreter` tool requires
a list of file IDs, while the `file_search` tool requires a list of vector store
IDs.
tools: A list of tool enabled on the assistant. There can be a maximum of 128 tools per
assistant. Tools can be of types `code_interpreter`, `file_search`, or
`function`.
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.
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
"""
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
"/assistants",
body=await async_maybe_transform(
{
"model": model,
"description": description,
"instructions": instructions,
"metadata": metadata,
"name": name,
"reasoning_effort": reasoning_effort,
"response_format": response_format,
"temperature": temperature,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
},
assistant_create_params.AssistantCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
async def retrieve(
self,
assistant_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,
) -> Assistant:
"""
Retrieves an assistant.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._get(
f"/assistants/{assistant_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
async def update(
self,
assistant_id: str,
*,
description: Optional[str] | NotGiven = NOT_GIVEN,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"o3-mini",
"o3-mini-2025-01-31",
"o1",
"o1-2024-12-17",
"gpt-4o",
"gpt-4o-2024-11-20",
"gpt-4o-2024-08-06",
"gpt-4o-2024-05-13",
"gpt-4o-mini",
"gpt-4o-mini-2024-07-18",
"gpt-4.5-preview",
"gpt-4.5-preview-2025-02-27",
"gpt-4-turbo",
"gpt-4-turbo-2024-04-09",
"gpt-4-0125-preview",
"gpt-4-turbo-preview",
"gpt-4-1106-preview",
"gpt-4-vision-preview",
"gpt-4",
"gpt-4-0314",
"gpt-4-0613",
"gpt-4-32k",
"gpt-4-32k-0314",
"gpt-4-32k-0613",
"gpt-3.5-turbo",
"gpt-3.5-turbo-16k",
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-1106",
"gpt-3.5-turbo-0125",
"gpt-3.5-turbo-16k-0613",
],
]
| NotGiven = NOT_GIVEN,
name: Optional[str] | NotGiven = NOT_GIVEN,
reasoning_effort: Optional[ReasoningEffort] | NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
tool_resources: Optional[assistant_update_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Iterable[AssistantToolParam] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | 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,
) -> Assistant:
"""Modifies an assistant.
Args:
description: The description of the assistant.
The maximum length is 512 characters.
instructions: The system instructions that the assistant uses. The maximum length is 256,000
characters.
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.
model: ID of the model to use. You can use the
[List models](https://platform.openai.com/docs/api-reference/models/list) API to
see all of your available models, or see our
[Model overview](https://platform.openai.com/docs/models) for descriptions of
them.
name: The name of the assistant. The maximum length is 256 characters.
reasoning_effort: **o-series 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: Specifies the format that the model must output. Compatible with
[GPT-4o](https://platform.openai.com/docs/models#gpt-4o),
[GPT-4 Turbo](https://platform.openai.com/docs/models#gpt-4-turbo-and-gpt-4),
and all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
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.
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.
tool_resources: A set of resources that are used by the assistant's tools. The resources are
specific to the type of tool. For example, the `code_interpreter` tool requires
a list of file IDs, while the `file_search` tool requires a list of vector store
IDs.
tools: A list of tool enabled on the assistant. There can be a maximum of 128 tools per
assistant. Tools can be of types `code_interpreter`, `file_search`, or
`function`.
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.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
f"/assistants/{assistant_id}",
body=await async_maybe_transform(
{
"description": description,
"instructions": instructions,
"metadata": metadata,
"model": model,
"name": name,
"reasoning_effort": reasoning_effort,
"response_format": response_format,
"temperature": temperature,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
},
assistant_update_params.AssistantUpdateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Assistant,
)
def list(
self,
*,
after: str | NotGiven = NOT_GIVEN,
before: str | NotGiven = NOT_GIVEN,
limit: int | NotGiven = NOT_GIVEN,
order: Literal["asc", "desc"] | 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,
) -> AsyncPaginator[Assistant, AsyncCursorPage[Assistant]]:
"""Returns a list of assistants.
Args:
after: A cursor for use in pagination.
`after` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
ending with obj_foo, your subsequent call can include after=obj_foo in order to
fetch the next page of the list.
before: A cursor for use in pagination. `before` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
starting with obj_foo, your subsequent call can include before=obj_foo in order
to fetch the previous page of the list.
limit: A limit on the number of objects to be returned. Limit can range between 1 and
100, and the default is 20.
order: Sort order by the `created_at` timestamp of the objects. `asc` for ascending
order and `desc` for descending order.
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
"""
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get_api_list(
"/assistants",
page=AsyncCursorPage[Assistant],
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=maybe_transform(
{
"after": after,
"before": before,
"limit": limit,
"order": order,
},
assistant_list_params.AssistantListParams,
),
),
model=Assistant,
)
async def delete(
self,
assistant_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,
) -> AssistantDeleted:
"""
Delete an assistant.
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 assistant_id:
raise ValueError(f"Expected a non-empty value for `assistant_id` but received {assistant_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._delete(
f"/assistants/{assistant_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=AssistantDeleted,
)
class AssistantsWithRawResponse:
def __init__(self, assistants: Assistants) -> None:
self._assistants = assistants
self.create = _legacy_response.to_raw_response_wrapper(
assistants.create,
)
self.retrieve = _legacy_response.to_raw_response_wrapper(
assistants.retrieve,
)
self.update = _legacy_response.to_raw_response_wrapper(
assistants.update,
)
self.list = _legacy_response.to_raw_response_wrapper(
assistants.list,
)
self.delete = _legacy_response.to_raw_response_wrapper(
assistants.delete,
)
class AsyncAssistantsWithRawResponse:
def __init__(self, assistants: AsyncAssistants) -> None:
self._assistants = assistants
self.create = _legacy_response.async_to_raw_response_wrapper(
assistants.create,
)
self.retrieve = _legacy_response.async_to_raw_response_wrapper(
assistants.retrieve,
)
self.update = _legacy_response.async_to_raw_response_wrapper(
assistants.update,
)
self.list = _legacy_response.async_to_raw_response_wrapper(
assistants.list,
)
self.delete = _legacy_response.async_to_raw_response_wrapper(
assistants.delete,
)
class AssistantsWithStreamingResponse:
def __init__(self, assistants: Assistants) -> None:
self._assistants = assistants
self.create = to_streamed_response_wrapper(
assistants.create,
)
self.retrieve = to_streamed_response_wrapper(
assistants.retrieve,
)
self.update = to_streamed_response_wrapper(
assistants.update,
)
self.list = to_streamed_response_wrapper(
assistants.list,
)
self.delete = to_streamed_response_wrapper(
assistants.delete,
)
class AsyncAssistantsWithStreamingResponse:
def __init__(self, assistants: AsyncAssistants) -> None:
self._assistants = assistants
self.create = async_to_streamed_response_wrapper(
assistants.create,
)
self.retrieve = async_to_streamed_response_wrapper(
assistants.retrieve,
)
self.update = async_to_streamed_response_wrapper(
assistants.update,
)
self.list = async_to_streamed_response_wrapper(
assistants.list,
)
self.delete = async_to_streamed_response_wrapper(
assistants.delete,
)
