Assistants API (Preview) runs reference
Note
- File search can ingest up to 10,000 files per assistant - 500 times more than before. It is fast, supports parallel queries through multi-threaded searches, and features enhanced reranking and query rewriting.
- Vector store is a new object in the API. Once a file is added to a vector store, it's automatically parsed, chunked, and embedded, made ready to be searched. Vector stores can be used across assistants and threads, simplifying file management and billing.
- We've added support for the
tool_choiceparameter which can be used to force the use of a specific tool (like file search, code interpreter, or a function) in a particular run.
This article provides reference documentation for Python and REST for the new Assistants API (Preview). More in-depth step-by-step guidance is provided in the getting started guide.
Create run
POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs?api-version=2024-08-01-preview
Create a run.
Path parameter
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread to create a message for. |
Request body
| Name | Type | Required | Description |
|---|---|---|---|
assistant_id |
string | Required | The ID of the assistant to use to execute this run. |
model |
string or null | Optional | The model deployment name to be used to execute this run. If a value is provided here, it will override the model deployment name associated with the assistant. If not, the model deployment name associated with the assistant will be used. |
instructions |
string or null | Optional | Overrides the instructions of the assistant. This is useful for modifying the behavior on a per-run basis. |
additional_instructions |
string | Optional | Appends additional instructions at the end of the instructions for the run. This is useful for modifying the behavior on a per-run basis without overriding other instructions. |
additional_messages |
array | Optional | Adds additional messages to the thread before creating the run. |
tools |
array or null | Optional | Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis. |
metadata |
map | Optional | 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 maximum of 512 characters long. |
temperature |
number | Optional | 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. Default is 1. |
top_p |
number | Optional | 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. Default is 1. |
stream |
boolean | optional | 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. |
max_prompt_tokens |
integer | optional | The maximum number of completion tokens that might 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. |
max_completion_tokens |
integer | optional | The maximum number of completion tokens that might 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. |
truncation_strategy |
truncationObject | optional | Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run. |
tool_choice |
string or object | optional | Controls which (if any) tool is called by the model. A none value means the model won't 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 a tool. Specifying a particular tool like {"type": "file_search"} or {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. |
response_format |
string or object | optional | Specifies the format that the model must output. Compatible with 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 might 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 might be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length. |
Returns
A run object.
Example create run request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.runs.create(
thread_id="thread_abc123",
assistant_id="asst_abc123"
)
print(run)
Create thread and run
POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/runs?api-version=2024-08-01-preview
Create a thread and run it in a single request.
Request Body
| Name | Type | Required | Description |
|---|---|---|---|
assistant_id |
string | Required | The ID of the assistant to use to execute this run. |
thread |
object | Optional | |
model |
string or null | Optional | The ID of the Model deployment name to be used to execute this run. If a value is provided here, it will override the model deployment name associated with the assistant. If not, the model deployment name associated with the assistant will be used. |
instructions |
string or null | Optional | Override the default system message of the assistant. This is useful for modifying the behavior on a per-run basis. |
tools |
array or null | Optional | Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis. |
metadata |
map | Optional | 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 maximum of 512 characters long. |
temperature |
number | Optional | 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. Default is 1. |
top_p |
number | Optional | 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. Default is 1. |
stream |
boolean | optional | 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. |
max_prompt_tokens |
integer | optional | The maximum number of completion tokens that might 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. |
max_completion_tokens |
integer | optional | The maximum number of completion tokens that might 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. |
truncation_strategy |
truncationObject | optional | Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run. |
tool_choice |
string or object | optional | Controls which (if any) tool is called by the model. A none value means the model won't 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 a tool. Specifying a particular tool like {"type": "file_search"} or {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. |
response_format |
string or object | optional | Specifies the format that the model must output. Compatible with 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 might 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 might be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length. |
Returns
A run object.
Example create thread and run request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.create_and_run(
assistant_id="asst_abc123",
thread={
"messages": [
{"role": "user", "content": "Explain deep learning to a 5 year old."}
]
}
)
List runs
GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs?api-version=2024-08-01-preview
Returns a list of runs belonging to a thread.
Path parameter
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread that the run belongs to. |
Query Parameters
| Name | Type | Required | Description |
|---|---|---|---|
limit |
integer | Optional - Defaults to 20 | A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20. |
order |
string | Optional - Defaults to desc | Sort order by the created_at timestamp of the objects. asc for ascending order and desc for descending order. |
after |
string | Optional | 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 |
string | Optional | 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. |
Returns
A list of run objects.
Example list runs request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
runs = client.beta.threads.runs.list(
"thread_abc123"
)
print(runs)
List run steps
GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs/{run_id}/steps?api-version=2024-08-01-preview
Returns a list of steps belonging to a run.
Path parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread that the run belongs to. |
run_id |
string | Required | The ID of the run associated with the run steps to be queried. |
Query parameters
| Name | Type | Required | Description |
|---|---|---|---|
limit |
integer | Optional - Defaults to 20 | A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20. |
order |
string | Optional - Defaults to desc | Sort order by the created_at timestamp of the objects. asc for ascending order and desc for descending order. |
after |
string | Optional | 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 |
string | Optional | 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. |
Returns
A list of run step objects.
Example list run steps request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run_steps = client.beta.threads.runs.steps.list(
thread_id="thread_abc123",
run_id="run_abc123"
)
print(run_steps)
Retrieve run
from openai import OpenAI
client = OpenAI()
run = client.beta.threads.runs.retrieve(
thread_id="thread_abc123",
run_id="run_abc123"
)
print(run)
Retrieves a run.
Path parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread that was run. |
run_id |
string | Required | The ID of the run to retrieve. |
Returns
The run object matching the specified run ID.
Example list run steps request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.runs.retrieve(
thread_id="thread_abc123",
run_id="run_abc123"
)
print(run)
Retrieve run step
GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs/{run_id}/steps/{step_id}?api-version=2024-08-01-preview
Retrieves a run step.
Path Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread to which the run and run step belongs. |
run_id |
string | Required | The ID of the run to which the run step belongs. |
step_id |
string | Required | The ID of the run step to retrieve. |
Returns
The run step object matching the specified ID.
Example retrieve run steps request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run_step = client.beta.threads.runs.steps.retrieve(
thread_id="thread_abc123",
run_id="run_abc123",
step_id="step_abc123"
)
print(run_step)
Modify run
POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs/{run_id}?api-version=2024-08-01-preview
Modifies a run.
Path Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread that was run. |
run_id |
string | Required | The ID of the run to modify. |
Request body
| Name | Type | Required | Description |
|---|---|---|---|
metadata |
map | Optional | 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 maximum of 512 characters long. |
Returns
The modified run object matching the specified ID.
Example modify run request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.runs.update(
thread_id="thread_abc123",
run_id="run_abc123",
metadata={"user_id": "user_abc123"},
)
print(run)
Submit tool outputs to run
POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs/{run_id}/submit_tool_outputs?api-version=2024-08-01-preview
When a run has the status: "requires_action" and required_action.type is submit_tool_outputs, this endpoint can be used to submit the outputs from the tool calls once they're all completed. All outputs must be submitted in a single request.
Path Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread to which this run belongs. |
run_id |
string | Required | The ID of the run that requires the tool output submission. |
Request body
| Name | Type | Required | Description |
|---|---|---|---|
tool_outputs |
array | Required | A list of tools for which the outputs are being submitted. |
stream |
boolean | Optional | 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. |
Returns
The modified run object matching the specified ID.
Example submit tool outputs to run request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.runs.submit_tool_outputs(
thread_id="thread_abc123",
run_id="run_abc123",
tool_outputs=[
{
"tool_call_id": "call_abc123",
"output": "28C"
}
]
)
print(run)
Cancel a run
POST https://YOUR_RESOURCE_NAME.openai.azure.com/openai/threads/{thread_id}/runs/{run_id}/cancel?api-version=2024-08-01-preview
Cancels a run that is in_progress.
Path Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
thread_id |
string | Required | The ID of the thread to which this run belongs. |
run_id |
string | Required | The ID of the run to cancel. |
Returns
The modified run object matching the specified ID.
Example submit tool outputs to run request
from openai import AzureOpenAI
client = AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version="2024-08-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
run = client.beta.threads.runs.cancel(
thread_id="thread_abc123",
run_id="run_abc123"
)
print(run)
Run object
Represents an execution run on a thread.
| Name | Type | Description |
|---|---|---|
id |
string | The identifier, which can be referenced in API endpoints. |
object |
string | The object type, which is always thread.run. |
created_at |
integer | The Unix timestamp (in seconds) for when the run was created. |
thread_id |
string | The ID of the thread that was executed on as a part of this run. |
assistant_id |
string | The ID of the assistant used for execution of this run. |
status |
string | The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, or expired. |
required_action |
object or null | Details on the action required to continue the run. Will be null if no action is required. |
last_error |
object or null | The last error associated with this run. Will be null if there are no errors. |
expires_at |
integer | The Unix timestamp (in seconds) for when the run will expire. |
started_at |
integer or null | The Unix timestamp (in seconds) for when the run was started. |
cancelled_at |
integer or null | The Unix timestamp (in seconds) for when the run was canceled. |
failed_at |
integer or null | The Unix timestamp (in seconds) for when the run failed. |
completed_at |
integer or null | The Unix timestamp (in seconds) for when the run was completed. |
model |
string | The model deployment name that the assistant used for this run. |
instructions |
string | The instructions that the assistant used for this run. |
tools |
array | The list of tools that the assistant used for this run. |
file_ids |
array | The list of File IDs the assistant used for this run. |
metadata |
map | 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 maximum of 512 characters long. |
tool_choice |
string or object | Controls which (if any) tool is called by the model. none means the model won't 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 a tool. Specifying a particular tool like {"type": "file_search"} or {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. |
max_prompt_tokens |
integer or null | The maximum number of prompt tokens specified to have been used over the course of the run. |
max_completion_tokens |
integer or null | The maximum number of completion tokens specified to have been used over the course of the run. |
usage |
object or null | Usage statistics related to the run. This value will be null if the run is not in a terminal state (for example in_progress, queued). |
truncation_strategy |
object | Controls for how a thread will be truncated prior to the run. |
response_format |
string | The format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106. |
tool_choice |
string | Controls which (if any) tool is called by the model. none means the model won't 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 a tool. |
Run step object
Represent a step in execution of a run.
| Name | Type | Description |
|---|---|---|
id |
string | The identifier of the run step, which can be referenced in API endpoints. |
object |
string | The object type, which is always thread.run.step. |
created_at |
integer | The Unix timestamp (in seconds) for when the run step was created. |
assistant_id |
string | The ID of the assistant associated with the run step. |
thread_id |
string | The ID of the thread that was run. |
run_id |
string | The ID of the run that this run step is a part of. |
type |
string | The type of run step, which can be either message_creation or tool_calls. |
status |
string | The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired. |
step_details |
object | The details of the run step. |
last_error |
object or null | The last error associated with this run step. Will be null if there are no errors. |
expired_at |
integer or null | The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired. |
cancelled_at |
integer or null | The Unix timestamp (in seconds) for when the run step was canceled. |
failed_at |
integer or null | The Unix timestamp (in seconds) for when the run step failed. |
completed_at |
integer or null | The Unix timestamp (in seconds) for when the run step completed. |
metadata |
map | 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 maximum of 512 characters long. |
Stream a run result (preview)
Stream the result of executing a Run or resuming a Run after submitting tool outputs. You can stream events after:
To stream a result, pass "stream": true while creating a run. The response will be a Server-Sent events stream.
Streaming example
from typing_extensions import override
from openai import AssistantEventHandler
# First, we create a EventHandler class to define
# how we want to handle the events in the response stream.
class EventHandler(AssistantEventHandler):
@override
def on_text_created(self, text) -> None:
print(f"\nassistant > ", end="", flush=True)
@override
def on_text_delta(self, delta, snapshot):
print(delta.value, end="", flush=True)
def on_tool_call_created(self, tool_call):
print(f"\nassistant > {tool_call.type}\n", flush=True)
def on_tool_call_delta(self, delta, snapshot):
if delta.type == 'code_interpreter':
if delta.code_interpreter.input:
print(delta.code_interpreter.input, end="", flush=True)
if delta.code_interpreter.outputs:
print(f"\n\noutput >", flush=True)
for output in delta.code_interpreter.outputs:
if output.type == "logs":
print(f"\n{output.logs}", flush=True)
# Then, we use the `create_and_stream` SDK helper
# with the `EventHandler` class to create the Run
# and stream the response.
with client.beta.threads.runs.stream(
thread_id=thread.id,
assistant_id=assistant.id,
instructions="Please address the user as Jane Doe. The user has a premium account.",
event_handler=EventHandler(),
) as stream:
stream.until_done()
Truncation object
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
| Name | Type | Description | Required |
|---|---|---|---|
type |
string | The truncation strategy to use for the thread. The default is auto. If set to last_messages, the thread will be truncated to the n most recent messages in the thread. When set to auto, messages in the middle of the thread will be dropped to fit the context length of the model, max_prompt_tokens. |
Yes |
last_messages |
integer | The number of most recent messages from the thread when constructing the context for the run. | No |
Message delta object
Represents a message delta. For example any changed fields on a message during streaming.
| Name | Type | Description |
|---|---|---|
id |
string | The identifier of the message, which can be referenced in API endpoints. |
object |
string | The object type, which is always thread.message.delta. |
delta |
object | The delta containing the fields that have changed on the Message. |
Run step delta object
Represents a run step delta. For example any changed fields on a run step during streaming.
| Name | Type | Description |
|---|---|---|
id |
string | The identifier of the run step, which can be referenced in API endpoints. |
object |
string | The object type, which is always thread.run.step.delta. |
delta |
object | The delta containing the fields that have changed on the run step. |
Assistant stream events
Represents an event emitted when streaming a Run. Each event in a server-sent events stream has an event and data property:
event: thread.created
data: {"id": "thread_123", "object": "thread", ...}
Events are emitted whenever a new object is created, transitions to a new state, or is being streamed in parts (deltas). For example, thread.run.created is emitted when a new run is created, thread.run.completed when a run completes, and so on. When an Assistant chooses to create a message during a run, we emit a thread.message.created event, a thread.message.in_progress event, many thread.message.delta events, and finally a thread.message.completed event.
| Name | Type | Description |
|---|---|---|
thread.created |
data is a thread. |
Occurs when a new thread is created. |
thread.run.created |
data is a run. |
Occurs when a new run is created. |
thread.run.queued |
data is a run. |
Occurs when a run moves to a queued status. |
thread.run.in_progress |
data is a run. |
Occurs when a run moves to an in_progress status. |
thread.run.requires_action |
data is a run. |
Occurs when a run moves to a requires_action status. |
thread.run.completed |
data is a run. |
Occurs when a run is completed. |
thread.run.failed |
data is a run. |
Occurs when a run fails. |
thread.run.cancelling |
data is a run. |
Occurs when a run moves to a cancelling status. |
thread.run.cancelled |
data is a run. |
Occurs when a run is canceled. |
thread.run.expired |
data is a run. |
Occurs when a run expires. |
thread.run.step.created |
data is a run step. |
Occurs when a run step is created. |
thread.run.step.in_progress |
data is a run step. |
Occurs when a run step moves to an in_progress state. |
thread.run.step.delta |
data is a run step delta. |
Occurs when parts of a run step are being streamed. |
thread.run.step.completed |
data is a run step. |
Occurs when a run step is completed. |
thread.run.step.failed |
data is a run step. |
Occurs when a run step fails. |
thread.run.step.cancelled |
data is a run step. |
Occurs when a run step is canceled. |
thread.run.step.expired |
data is a run step. |
Occurs when a run step expires. |
thread.message.created |
data is a message. |
Occurs when a message is created. |
thread.message.in_progress |
data is a message. |
Occurs when a message moves to an in_progress state. |
thread.message.delta |
data is a message delta. |
Occurs when parts of a Message are being streamed. |
thread.message.completed |
data is a message. |
Occurs when a message is completed. |
thread.message.incomplete |
data is a message. |
Occurs when a message ends before it is completed. |
error |
data is an error. |
Occurs when an error occurs. This can happen due to an internal server error or a timeout. |
done |
data is [DONE] |
Occurs when a stream ends. |