# 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.chat_model import ChatModel from ...types.beta.assistant import Assistant from ...types.beta.assistant_deleted import AssistantDeleted 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 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[object] | NotGiven = NOT_GIVEN, name: Optional[str] | 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/overview) 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. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. name: The name of the assistant. The maximum length is 256 characters. 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, "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[object] | NotGiven = NOT_GIVEN, model: str | NotGiven = NOT_GIVEN, name: Optional[str] | 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. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. 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/overview) for descriptions of them. name: The name of the assistant. The maximum length is 256 characters. 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, "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, ending 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 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[object] | NotGiven = NOT_GIVEN, name: Optional[str] | 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/overview) 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. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. name: The name of the assistant. The maximum length is 256 characters. 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, "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[object] | NotGiven = NOT_GIVEN, model: str | NotGiven = NOT_GIVEN, name: Optional[str] | 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. Keys can be a maximum of 64 characters long and values can be a maxium of 512 characters long. 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/overview) for descriptions of them. name: The name of the assistant. The maximum length is 256 characters. 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, "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, ending 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, )