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# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import Union, Iterable, Optional, overload
from functools import partial
from typing_extensions import Literal
import httpx
from .... import _legacy_response
from .runs import (
Runs,
AsyncRuns,
RunsWithRawResponse,
AsyncRunsWithRawResponse,
RunsWithStreamingResponse,
AsyncRunsWithStreamingResponse,
)
from .messages import (
Messages,
AsyncMessages,
MessagesWithRawResponse,
AsyncMessagesWithRawResponse,
MessagesWithStreamingResponse,
AsyncMessagesWithStreamingResponse,
)
from ...._types import NOT_GIVEN, Body, Query, Headers, NotGiven
from ...._utils import (
required_args,
maybe_transform,
async_maybe_transform,
)
from .runs.runs import Runs, AsyncRuns
from ...._compat import cached_property
from ...._resource import SyncAPIResource, AsyncAPIResource
from ...._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
from ...._streaming import Stream, AsyncStream
from ....types.beta import (
thread_create_params,
thread_update_params,
thread_create_and_run_params,
)
from ...._base_client import (
make_request_options,
)
from ....lib.streaming import (
AssistantEventHandler,
AssistantEventHandlerT,
AssistantStreamManager,
AsyncAssistantEventHandler,
AsyncAssistantEventHandlerT,
AsyncAssistantStreamManager,
)
from ....types.beta.thread import Thread
from ....types.beta.threads.run import Run
from ....types.beta.thread_deleted import ThreadDeleted
from ....types.beta.assistant_stream_event import AssistantStreamEvent
from ....types.beta.assistant_tool_choice_option_param import AssistantToolChoiceOptionParam
from ....types.beta.assistant_response_format_option_param import AssistantResponseFormatOptionParam
__all__ = ["Threads", "AsyncThreads"]
class Threads(SyncAPIResource):
@cached_property
def runs(self) -> Runs:
return Runs(self._client)
@cached_property
def messages(self) -> Messages:
return Messages(self._client)
@cached_property
def with_raw_response(self) -> ThreadsWithRawResponse:
return ThreadsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> ThreadsWithStreamingResponse:
return ThreadsWithStreamingResponse(self)
def create(
self,
*,
messages: Iterable[thread_create_params.Message] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_params.ToolResources] | 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,
) -> Thread:
"""
Create a thread.
Args:
messages: A list of [messages](https://platform.openai.com/docs/api-reference/messages) to
start the thread with.
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.
tool_resources: A set of resources that are made available to the assistant's tools in this
thread. 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.
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(
"/threads",
body=maybe_transform(
{
"messages": messages,
"metadata": metadata,
"tool_resources": tool_resources,
},
thread_create_params.ThreadCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
def retrieve(
self,
thread_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,
) -> Thread:
"""
Retrieves a thread.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get(
f"/threads/{thread_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
def update(
self,
thread_id: str,
*,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_update_params.ToolResources] | 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,
) -> Thread:
"""
Modifies a thread.
Args:
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format. Keys
can be a maximum of 64 characters long and values can be a maxium of 512
characters long.
tool_resources: A set of resources that are made available to the assistant's tools in this
thread. 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.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
f"/threads/{thread_id}",
body=maybe_transform(
{
"metadata": metadata,
"tool_resources": tool_resources,
},
thread_update_params.ThreadUpdateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
def delete(
self,
thread_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,
) -> ThreadDeleted:
"""
Delete a thread.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._delete(
f"/threads/{thread_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=ThreadDeleted,
)
@overload
def create_and_run(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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_and_run(
self,
*,
assistant_id: str,
stream: Literal[True],
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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[AssistantStreamEvent]:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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_and_run(
self,
*,
assistant_id: str,
stream: bool,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run | Stream[AssistantStreamEvent]:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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(["assistant_id"], ["assistant_id", "stream"])
def create_and_run(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run | Stream[AssistantStreamEvent]:
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
"/threads/runs",
body=maybe_transform(
{
"assistant_id": assistant_id,
"instructions": instructions,
"max_completion_tokens": max_completion_tokens,
"max_prompt_tokens": max_prompt_tokens,
"metadata": metadata,
"model": model,
"response_format": response_format,
"stream": stream,
"temperature": temperature,
"thread": thread,
"tool_choice": tool_choice,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
"truncation_strategy": truncation_strategy,
},
thread_create_and_run_params.ThreadCreateAndRunParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Run,
stream=stream or False,
stream_cls=Stream[AssistantStreamEvent],
)
def create_and_run_poll(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
poll_interval_ms: int | 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,
) -> Run:
"""
A helper to create a thread, start a run and then poll for a terminal state.
More information on Run lifecycles can be found here:
https://platform.openai.com/docs/assistants/how-it-works/runs-and-run-steps
"""
run = self.create_and_run(
assistant_id=assistant_id,
instructions=instructions,
max_completion_tokens=max_completion_tokens,
max_prompt_tokens=max_prompt_tokens,
metadata=metadata,
model=model,
response_format=response_format,
temperature=temperature,
stream=False,
thread=thread,
tool_resources=tool_resources,
tool_choice=tool_choice,
truncation_strategy=truncation_strategy,
top_p=top_p,
tools=tools,
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
)
return self.runs.poll(run.id, run.thread_id, extra_headers, extra_query, extra_body, timeout, poll_interval_ms)
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> AssistantStreamManager[AssistantEventHandler]:
"""Create a thread and stream the run back"""
...
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
event_handler: AssistantEventHandlerT,
# 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,
) -> AssistantStreamManager[AssistantEventHandlerT]:
"""Create a thread and stream the run back"""
...
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
event_handler: AssistantEventHandlerT | None = None,
# 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,
) -> AssistantStreamManager[AssistantEventHandler] | AssistantStreamManager[AssistantEventHandlerT]:
"""Create a thread and stream the run back"""
extra_headers = {
"OpenAI-Beta": "assistants=v2",
"X-Stainless-Stream-Helper": "threads.create_and_run_stream",
"X-Stainless-Custom-Event-Handler": "true" if event_handler else "false",
**(extra_headers or {}),
}
make_request = partial(
self._post,
"/threads/runs",
body=maybe_transform(
{
"assistant_id": assistant_id,
"instructions": instructions,
"max_completion_tokens": max_completion_tokens,
"max_prompt_tokens": max_prompt_tokens,
"metadata": metadata,
"model": model,
"response_format": response_format,
"temperature": temperature,
"tool_choice": tool_choice,
"stream": True,
"thread": thread,
"tools": tools,
"tool": tool_resources,
"truncation_strategy": truncation_strategy,
"top_p": top_p,
},
thread_create_and_run_params.ThreadCreateAndRunParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Run,
stream=True,
stream_cls=Stream[AssistantStreamEvent],
)
return AssistantStreamManager(make_request, event_handler=event_handler or AssistantEventHandler())
class AsyncThreads(AsyncAPIResource):
@cached_property
def runs(self) -> AsyncRuns:
return AsyncRuns(self._client)
@cached_property
def messages(self) -> AsyncMessages:
return AsyncMessages(self._client)
@cached_property
def with_raw_response(self) -> AsyncThreadsWithRawResponse:
return AsyncThreadsWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncThreadsWithStreamingResponse:
return AsyncThreadsWithStreamingResponse(self)
async def create(
self,
*,
messages: Iterable[thread_create_params.Message] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_params.ToolResources] | 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,
) -> Thread:
"""
Create a thread.
Args:
messages: A list of [messages](https://platform.openai.com/docs/api-reference/messages) to
start the thread with.
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.
tool_resources: A set of resources that are made available to the assistant's tools in this
thread. 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.
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(
"/threads",
body=await async_maybe_transform(
{
"messages": messages,
"metadata": metadata,
"tool_resources": tool_resources,
},
thread_create_params.ThreadCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
async def retrieve(
self,
thread_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,
) -> Thread:
"""
Retrieves a thread.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._get(
f"/threads/{thread_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
async def update(
self,
thread_id: str,
*,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_update_params.ToolResources] | 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,
) -> Thread:
"""
Modifies a thread.
Args:
metadata: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format. Keys
can be a maximum of 64 characters long and values can be a maxium of 512
characters long.
tool_resources: A set of resources that are made available to the assistant's tools in this
thread. 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.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
f"/threads/{thread_id}",
body=await async_maybe_transform(
{
"metadata": metadata,
"tool_resources": tool_resources,
},
thread_update_params.ThreadUpdateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Thread,
)
async def delete(
self,
thread_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,
) -> ThreadDeleted:
"""
Delete a thread.
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 thread_id:
raise ValueError(f"Expected a non-empty value for `thread_id` but received {thread_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._delete(
f"/threads/{thread_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=ThreadDeleted,
)
@overload
async def create_and_run(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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_and_run(
self,
*,
assistant_id: str,
stream: Literal[True],
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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[AssistantStreamEvent]:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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_and_run(
self,
*,
assistant_id: str,
stream: bool,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run | AsyncStream[AssistantStreamEvent]:
"""
Create a thread and run it in one request.
Args:
assistant_id: The ID of the
[assistant](https://platform.openai.com/docs/api-reference/assistants) to use to
execute this run.
stream: If `true`, returns a stream of events that happen during the Run as server-sent
events, terminating when the Run enters a terminal state with a `data: [DONE]`
message.
instructions: Override the default system message of the assistant. This is useful for
modifying the behavior on a per-run basis.
max_completion_tokens: The maximum number of completion tokens that may be used over the course of the
run. The run will make a best effort to use only the number of completion tokens
specified, across multiple turns of the run. If the run exceeds the number of
completion tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
max_prompt_tokens: The maximum number of prompt tokens that may be used over the course of the run.
The run will make a best effort to use only the number of prompt tokens
specified, across multiple turns of the run. If the run exceeds the number of
prompt tokens specified, the run will end with status `incomplete`. See
`incomplete_details` for more info.
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: The ID of the [Model](https://platform.openai.com/docs/api-reference/models) to
be used to execute this run. If a value is provided here, it will override the
model associated with the assistant. If not, the model associated with the
assistant will be used.
response_format: Specifies the format that the model must output. Compatible with
[GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) and
all GPT-3.5 Turbo models since `gpt-3.5-turbo-1106`.
Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the
message the model generates is valid JSON.
**Important:** when using JSON mode, you **must** also instruct the model to
produce JSON yourself via a system or user message. Without this, the model may
generate an unending stream of whitespace until the generation reaches the token
limit, resulting in a long-running and seemingly "stuck" request. Also note that
the message content may be partially cut off if `finish_reason="length"`, which
indicates the generation exceeded `max_tokens` or the conversation exceeded the
max context length.
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.
thread: If no thread is provided, an empty thread will be created.
tool_choice: Controls which (if any) tool is called by the model. `none` means the model will
not call any tools and instead generates a message. `auto` is the default value
and means the model can pick between generating a message or calling one or more
tools. `required` means the model must call one or more tools before responding
to the user. Specifying a particular tool like `{"type": "file_search"}` or
`{"type": "function", "function": {"name": "my_function"}}` forces the model to
call that tool.
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: Override the tools the assistant can use for this run. This is useful for
modifying the behavior on a per-run basis.
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_strategy: Controls for how a thread will be truncated prior to the run. Use this to
control the intial context window of the run.
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(["assistant_id"], ["assistant_id", "stream"])
async def create_and_run(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> Run | AsyncStream[AssistantStreamEvent]:
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
"/threads/runs",
body=await async_maybe_transform(
{
"assistant_id": assistant_id,
"instructions": instructions,
"max_completion_tokens": max_completion_tokens,
"max_prompt_tokens": max_prompt_tokens,
"metadata": metadata,
"model": model,
"response_format": response_format,
"stream": stream,
"temperature": temperature,
"thread": thread,
"tool_choice": tool_choice,
"tool_resources": tool_resources,
"tools": tools,
"top_p": top_p,
"truncation_strategy": truncation_strategy,
},
thread_create_and_run_params.ThreadCreateAndRunParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Run,
stream=stream or False,
stream_cls=AsyncStream[AssistantStreamEvent],
)
async def create_and_run_poll(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
poll_interval_ms: int | 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,
) -> Run:
"""
A helper to create a thread, start a run and then poll for a terminal state.
More information on Run lifecycles can be found here:
https://platform.openai.com/docs/assistants/how-it-works/runs-and-run-steps
"""
run = await self.create_and_run(
assistant_id=assistant_id,
instructions=instructions,
max_completion_tokens=max_completion_tokens,
max_prompt_tokens=max_prompt_tokens,
metadata=metadata,
model=model,
response_format=response_format,
temperature=temperature,
stream=False,
thread=thread,
tool_resources=tool_resources,
tool_choice=tool_choice,
truncation_strategy=truncation_strategy,
top_p=top_p,
tools=tools,
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
)
return await self.runs.poll(
run.id, run.thread_id, extra_headers, extra_query, extra_body, timeout, poll_interval_ms
)
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | 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,
) -> AsyncAssistantStreamManager[AsyncAssistantEventHandler]:
"""Create a thread and stream the run back"""
...
@overload
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
event_handler: AsyncAssistantEventHandlerT,
# 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,
) -> AsyncAssistantStreamManager[AsyncAssistantEventHandlerT]:
"""Create a thread and stream the run back"""
...
def create_and_run_stream(
self,
*,
assistant_id: str,
instructions: Optional[str] | NotGiven = NOT_GIVEN,
max_completion_tokens: Optional[int] | NotGiven = NOT_GIVEN,
max_prompt_tokens: Optional[int] | NotGiven = NOT_GIVEN,
metadata: Optional[object] | NotGiven = NOT_GIVEN,
model: Union[
str,
Literal[
"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",
],
None,
]
| NotGiven = NOT_GIVEN,
response_format: Optional[AssistantResponseFormatOptionParam] | NotGiven = NOT_GIVEN,
temperature: Optional[float] | NotGiven = NOT_GIVEN,
thread: thread_create_and_run_params.Thread | NotGiven = NOT_GIVEN,
tool_choice: Optional[AssistantToolChoiceOptionParam] | NotGiven = NOT_GIVEN,
tool_resources: Optional[thread_create_and_run_params.ToolResources] | NotGiven = NOT_GIVEN,
tools: Optional[Iterable[thread_create_and_run_params.Tool]] | NotGiven = NOT_GIVEN,
top_p: Optional[float] | NotGiven = NOT_GIVEN,
truncation_strategy: Optional[thread_create_and_run_params.TruncationStrategy] | NotGiven = NOT_GIVEN,
event_handler: AsyncAssistantEventHandlerT | None = None,
# 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,
) -> (
AsyncAssistantStreamManager[AsyncAssistantEventHandler]
| AsyncAssistantStreamManager[AsyncAssistantEventHandlerT]
):
"""Create a thread and stream the run back"""
extra_headers = {
"OpenAI-Beta": "assistants=v2",
"X-Stainless-Stream-Helper": "threads.create_and_run_stream",
"X-Stainless-Custom-Event-Handler": "true" if event_handler else "false",
**(extra_headers or {}),
}
request = self._post(
"/threads/runs",
body=maybe_transform(
{
"assistant_id": assistant_id,
"instructions": instructions,
"max_completion_tokens": max_completion_tokens,
"max_prompt_tokens": max_prompt_tokens,
"metadata": metadata,
"model": model,
"response_format": response_format,
"temperature": temperature,
"tool_choice": tool_choice,
"stream": True,
"thread": thread,
"tools": tools,
"tool": tool_resources,
"truncation_strategy": truncation_strategy,
"top_p": top_p,
},
thread_create_and_run_params.ThreadCreateAndRunParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=Run,
stream=True,
stream_cls=AsyncStream[AssistantStreamEvent],
)
return AsyncAssistantStreamManager(request, event_handler=event_handler or AsyncAssistantEventHandler())
class ThreadsWithRawResponse:
def __init__(self, threads: Threads) -> None:
self._threads = threads
self.create = _legacy_response.to_raw_response_wrapper(
threads.create,
)
self.retrieve = _legacy_response.to_raw_response_wrapper(
threads.retrieve,
)
self.update = _legacy_response.to_raw_response_wrapper(
threads.update,
)
self.delete = _legacy_response.to_raw_response_wrapper(
threads.delete,
)
self.create_and_run = _legacy_response.to_raw_response_wrapper(
threads.create_and_run,
)
@cached_property
def runs(self) -> RunsWithRawResponse:
return RunsWithRawResponse(self._threads.runs)
@cached_property
def messages(self) -> MessagesWithRawResponse:
return MessagesWithRawResponse(self._threads.messages)
class AsyncThreadsWithRawResponse:
def __init__(self, threads: AsyncThreads) -> None:
self._threads = threads
self.create = _legacy_response.async_to_raw_response_wrapper(
threads.create,
)
self.retrieve = _legacy_response.async_to_raw_response_wrapper(
threads.retrieve,
)
self.update = _legacy_response.async_to_raw_response_wrapper(
threads.update,
)
self.delete = _legacy_response.async_to_raw_response_wrapper(
threads.delete,
)
self.create_and_run = _legacy_response.async_to_raw_response_wrapper(
threads.create_and_run,
)
@cached_property
def runs(self) -> AsyncRunsWithRawResponse:
return AsyncRunsWithRawResponse(self._threads.runs)
@cached_property
def messages(self) -> AsyncMessagesWithRawResponse:
return AsyncMessagesWithRawResponse(self._threads.messages)
class ThreadsWithStreamingResponse:
def __init__(self, threads: Threads) -> None:
self._threads = threads
self.create = to_streamed_response_wrapper(
threads.create,
)
self.retrieve = to_streamed_response_wrapper(
threads.retrieve,
)
self.update = to_streamed_response_wrapper(
threads.update,
)
self.delete = to_streamed_response_wrapper(
threads.delete,
)
self.create_and_run = to_streamed_response_wrapper(
threads.create_and_run,
)
@cached_property
def runs(self) -> RunsWithStreamingResponse:
return RunsWithStreamingResponse(self._threads.runs)
@cached_property
def messages(self) -> MessagesWithStreamingResponse:
return MessagesWithStreamingResponse(self._threads.messages)
class AsyncThreadsWithStreamingResponse:
def __init__(self, threads: AsyncThreads) -> None:
self._threads = threads
self.create = async_to_streamed_response_wrapper(
threads.create,
)
self.retrieve = async_to_streamed_response_wrapper(
threads.retrieve,
)
self.update = async_to_streamed_response_wrapper(
threads.update,
)
self.delete = async_to_streamed_response_wrapper(
threads.delete,
)
self.create_and_run = async_to_streamed_response_wrapper(
threads.create_and_run,
)
@cached_property
def runs(self) -> AsyncRunsWithStreamingResponse:
return AsyncRunsWithStreamingResponse(self._threads.runs)
@cached_property
def messages(self) -> AsyncMessagesWithStreamingResponse:
return AsyncMessagesWithStreamingResponse(self._threads.messages)
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