Beta
BetaChatKit
ModelsExpand Collapse
chatkit_workflow: object { id, state_variables, tracing, version } Workflow metadata and state returned for the session.
Workflow metadata and state returned for the session.
BetaChatKitSessions
BetaChatKitThreads
List ChatKit thread items
ModelsExpand Collapse
chat_session: object { id, chatkit_configuration, client_secret, 7 more } Represents a ChatKit session and its resolved configuration.
Represents a ChatKit session and its resolved configuration.
chatkit_configuration: object { automatic_thread_titling, file_upload, history } Resolved ChatKit feature configuration for the session.
Resolved ChatKit feature configuration for the session.
workflow: object { id, state_variables, tracing, version } Workflow metadata for the session.
Workflow metadata for the session.
chat_session_chatkit_configuration: object { automatic_thread_titling, file_upload, history } ChatKit configuration for the session.
ChatKit configuration for the session.
chat_session_chatkit_configuration_param: object { automatic_thread_titling, file_upload, history } Optional per-session configuration settings for ChatKit behavior.
Optional per-session configuration settings for ChatKit behavior.
automatic_thread_titling: optional object { enabled } Configuration for automatic thread titling. When omitted, automatic thread titling is enabled by default.
Configuration for automatic thread titling. When omitted, automatic thread titling is enabled by default.
file_upload: optional object { enabled, max_file_size, max_files } Configuration for upload enablement and limits. When omitted, uploads are disabled by default (max_files 10, max_file_size 512 MB).
Configuration for upload enablement and limits. When omitted, uploads are disabled by default (max_files 10, max_file_size 512 MB).
chat_session_workflow_param: object { id, state_variables, tracing, version } Workflow reference and overrides applied to the chat session.
Workflow reference and overrides applied to the chat session.
state_variables: optional map[string or boolean or number]State variables forwarded to the workflow. Keys may be up to 64 characters, values must be primitive types, and the map defaults to an empty object.
State variables forwarded to the workflow. Keys may be up to 64 characters, values must be primitive types, and the map defaults to an empty object.
chatkit_response_output_text: object { annotations, text, type } Assistant response text accompanied by optional annotations.
Assistant response text accompanied by optional annotations.
chatkit_thread: object { id, created_at, object, 3 more } Represents a ChatKit thread and its current status.
Represents a ChatKit thread and its current status.
status: object { type } or object { reason, type } or object { reason, type } Current status for the thread. Defaults to active for newly created threads.
Current status for the thread. Defaults to active for newly created threads.
chatkit_thread_assistant_message_item: object { id, content, created_at, 3 more } Assistant-authored message within a thread.
Assistant-authored message within a thread.
chatkit_thread_item_list: object { data, first_id, has_more, 2 more } A paginated list of thread items rendered for the ChatKit API.
A paginated list of thread items rendered for the ChatKit API.
data: array of ChatKitThreadUserMessageItem { id, attachments, content, 5 more } or ChatKitThreadAssistantMessageItem { id, content, created_at, 3 more } or ChatKitWidgetItem { id, created_at, object, 3 more } or 3 moreA list of items
A list of items
chatkit_thread_user_message_item: object { id, attachments, content, 5 more } User-authored messages within a thread.
User-authored messages within a thread.
content: array of object { text, type } or object { text, type } Ordered content elements supplied by the user.
Ordered content elements supplied by the user.
chatkit_thread_assistant_message_item: object { id, content, created_at, 3 more } Assistant-authored message within a thread.
Assistant-authored message within a thread.
chatkit_widget_item: object { id, created_at, object, 3 more } Thread item that renders a widget payload.
Thread item that renders a widget payload.
chatkit.client_tool_call: object { id, arguments, call_id, 7 more } Record of a client side tool invocation initiated by the assistant.
Record of a client side tool invocation initiated by the assistant.
chatkit.task: object { id, created_at, heading, 5 more } Task emitted by the workflow to show progress and status updates.
Task emitted by the workflow to show progress and status updates.
chatkit_thread_user_message_item: object { id, attachments, content, 5 more } User-authored messages within a thread.
User-authored messages within a thread.
content: array of object { text, type } or object { text, type } Ordered content elements supplied by the user.
Ordered content elements supplied by the user.
BetaAssistants
Build Assistants that can call models and use tools.
ModelsExpand Collapse
assistant: object { id, created_at, description, 10 more } Represents an assistant that can call the model and use tools.
Represents an assistant that can call the model and use tools.
The system instructions that the assistant uses. The maximum length is 256,000 characters.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.
A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.
A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
response_format: optional "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
tool_resources: optional object { code_interpreter, file_search } 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.
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.
code_interpreter: optional object { file_ids }
A list of file IDs made available to the `code_interpreter“ tool. There can be a maximum of 20 files associated with the tool.
file_search: optional object { vector_store_ids }
The ID of the vector store attached to this assistant. There can be a maximum of 1 vector store attached to the assistant.
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.
assistant_stream_event: object { data, event, enabled } or object { data, event } or object { data, event } or 21 moreRepresents 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", ...}
We emit events whenever a new object is created, transitions to a new state, or is being
streamed in parts (deltas). For example, we emit thread.run.created 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.
We may add additional events over time, so we recommend handling unknown events gracefully
in your code. See the Assistants API quickstart to learn how to
integrate the Assistants API with streaming.
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", ...}We emit events whenever a new object is created, transitions to a new state, or is being
streamed in parts (deltas). For example, we emit thread.run.created 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.
We may add additional events over time, so we recommend handling unknown events gracefully in your code. See the Assistants API quickstart to learn how to integrate the Assistants API with streaming.
thread.created: object { data, event, enabled } Occurs when a new thread is created.
Occurs when a new thread is created.
data: object { id, created_at, metadata, 2 more } Represents a thread that contains messages.
Represents a thread that contains messages.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
tool_resources: object { code_interpreter, file_search } 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.
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.
code_interpreter: optional object { file_ids }
A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.
file_search: optional object { vector_store_ids }
The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.
thread.run.created: object { data, event } Occurs when a new run is created.
Occurs when a new run is created.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.queued: object { data, event } Occurs when a run moves to a queued status.
Occurs when a run moves to a queued status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.in_progress: object { data, event } Occurs when a run moves to an in_progress status.
Occurs when a run moves to an in_progress status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.requires_action: object { data, event } Occurs when a run moves to a requires_action status.
Occurs when a run moves to a requires_action status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.completed: object { data, event } Occurs when a run is completed.
Occurs when a run is completed.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.incomplete: object { data, event } Occurs when a run ends with status incomplete.
Occurs when a run ends with status incomplete.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.failed: object { data, event } Occurs when a run fails.
Occurs when a run fails.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.cancelling: object { data, event } Occurs when a run moves to a cancelling status.
Occurs when a run moves to a cancelling status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.cancelled: object { data, event } Occurs when a run is cancelled.
Occurs when a run is cancelled.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.expired: object { data, event } Occurs when a run expires.
Occurs when a run expires.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.step.created: object { data, event } Occurs when a run step is created.
Occurs when a run step is created.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.in_progress: object { data, event } Occurs when a run step moves to an in_progress state.
Occurs when a run step moves to an in_progress state.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.delta: object { data, event } Occurs when parts of a run step are being streamed.
Occurs when parts of a run step are being streamed.
data: object { id, delta, object } Represents a run step delta i.e. any changed fields on a run step during streaming.
Represents a run step delta i.e. any changed fields on a run step during streaming.
delta: object { step_details } The delta containing the fields that have changed on the run step.
The delta containing the fields that have changed on the run step.
step_details: optional RunStepDeltaMessageDelta { type, message_creation } or ToolCallDeltaObject { type, tool_calls } The details of the run step.
The details of the run step.
run_step_delta_message_delta: object { type, message_creation } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_call_delta_object: object { type, tool_calls } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
thread.run.step.completed: object { data, event } Occurs when a run step is completed.
Occurs when a run step is completed.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.failed: object { data, event } Occurs when a run step fails.
Occurs when a run step fails.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.cancelled: object { data, event } Occurs when a run step is cancelled.
Occurs when a run step is cancelled.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.expired: object { data, event } Occurs when a run step expires.
Occurs when a run step expires.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.message.created: object { data, event } Occurs when a message is created.
Occurs when a message is created.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.in_progress: object { data, event } Occurs when a message moves to an in_progress state.
Occurs when a message moves to an in_progress state.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.delta: object { data, event } Occurs when parts of a Message are being streamed.
Occurs when parts of a Message are being streamed.
data: object { id, delta, object } Represents a message delta i.e. any changed fields on a message during streaming.
Represents a message delta i.e. any changed fields on a message during streaming.
delta: object { content, role } The delta containing the fields that have changed on the Message.
The delta containing the fields that have changed on the Message.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
thread.message.completed: object { data, event } Occurs when a message is completed.
Occurs when a message is completed.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.incomplete: object { data, event } Occurs when a message ends before it is completed.
Occurs when a message ends before it is completed.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
error_event: object { data, event } Occurs when an error occurs. This can happen due to an internal server error or a timeout.
Occurs when an error occurs. This can happen due to an internal server error or a timeout.
assistant_tool: CodeInterpreterTool { type } or FileSearchTool { type, file_search } or FunctionTool { function, type }
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
message_stream_event: object { data, event } or object { data, event } or object { data, event } or 2 moreOccurs when a message is created.
Occurs when a message is created.
thread.message.created: object { data, event } Occurs when a message is created.
Occurs when a message is created.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.in_progress: object { data, event } Occurs when a message moves to an in_progress state.
Occurs when a message moves to an in_progress state.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.delta: object { data, event } Occurs when parts of a Message are being streamed.
Occurs when parts of a Message are being streamed.
data: object { id, delta, object } Represents a message delta i.e. any changed fields on a message during streaming.
Represents a message delta i.e. any changed fields on a message during streaming.
delta: object { content, role } The delta containing the fields that have changed on the Message.
The delta containing the fields that have changed on the Message.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
thread.message.completed: object { data, event } Occurs when a message is completed.
Occurs when a message is completed.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
thread.message.incomplete: object { data, event } Occurs when a message ends before it is completed.
Occurs when a message ends before it is completed.
data: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
run_step_stream_event: object { data, event } or object { data, event } or object { data, event } or 4 moreOccurs when a run step is created.
Occurs when a run step is created.
thread.run.step.created: object { data, event } Occurs when a run step is created.
Occurs when a run step is created.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.in_progress: object { data, event } Occurs when a run step moves to an in_progress state.
Occurs when a run step moves to an in_progress state.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.delta: object { data, event } Occurs when parts of a run step are being streamed.
Occurs when parts of a run step are being streamed.
data: object { id, delta, object } Represents a run step delta i.e. any changed fields on a run step during streaming.
Represents a run step delta i.e. any changed fields on a run step during streaming.
delta: object { step_details } The delta containing the fields that have changed on the run step.
The delta containing the fields that have changed on the run step.
step_details: optional RunStepDeltaMessageDelta { type, message_creation } or ToolCallDeltaObject { type, tool_calls } The details of the run step.
The details of the run step.
run_step_delta_message_delta: object { type, message_creation } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_call_delta_object: object { type, tool_calls } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
thread.run.step.completed: object { data, event } Occurs when a run step is completed.
Occurs when a run step is completed.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.failed: object { data, event } Occurs when a run step fails.
Occurs when a run step fails.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.cancelled: object { data, event } Occurs when a run step is cancelled.
Occurs when a run step is cancelled.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
thread.run.step.expired: object { data, event } Occurs when a run step expires.
Occurs when a run step expires.
data: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
run_stream_event: object { data, event } or object { data, event } or object { data, event } or 7 moreOccurs when a new run is created.
Occurs when a new run is created.
thread.run.created: object { data, event } Occurs when a new run is created.
Occurs when a new run is created.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.queued: object { data, event } Occurs when a run moves to a queued status.
Occurs when a run moves to a queued status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.in_progress: object { data, event } Occurs when a run moves to an in_progress status.
Occurs when a run moves to an in_progress status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.requires_action: object { data, event } Occurs when a run moves to a requires_action status.
Occurs when a run moves to a requires_action status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.completed: object { data, event } Occurs when a run is completed.
Occurs when a run is completed.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.incomplete: object { data, event } Occurs when a run ends with status incomplete.
Occurs when a run ends with status incomplete.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.failed: object { data, event } Occurs when a run fails.
Occurs when a run fails.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.cancelling: object { data, event } Occurs when a run moves to a cancelling status.
Occurs when a run moves to a cancelling status.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.cancelled: object { data, event } Occurs when a run is cancelled.
Occurs when a run is cancelled.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread.run.expired: object { data, event } Occurs when a run expires.
Occurs when a run expires.
data: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
thread_stream_event: object { data, event, enabled } Occurs when a new thread is created.
Occurs when a new thread is created.
data: object { id, created_at, metadata, 2 more } Represents a thread that contains messages.
Represents a thread that contains messages.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
tool_resources: object { code_interpreter, file_search } 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.
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.
code_interpreter: optional object { file_ids }
A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.
file_search: optional object { vector_store_ids }
The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.
BetaThreads
Build Assistants that can call models and use tools.
ModelsExpand Collapse
assistant_response_format_option: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
assistant_tool_choice_option: "none" or "auto" or "required" or AssistantToolChoice { type, function } 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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
thread: object { id, created_at, metadata, 2 more } Represents a thread that contains messages.
Represents a thread that contains messages.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
tool_resources: object { code_interpreter, file_search } 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.
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.
code_interpreter: optional object { file_ids }
A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.
file_search: optional object { vector_store_ids }
The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.
BetaThreadsRuns
Build Assistants that can call models and use tools.
Submit tool outputs to run
Cancel a run
ModelsExpand Collapse
required_action_function_tool_call: object { id, function, type } Tool call objects
Tool call objects
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
run: object { id, assistant_id, cancelled_at, 24 more } Represents an execution run on a thread.
Represents an execution run on a thread.
The ID of the assistant used for execution of this run.
incomplete_details: object { reason } Details on why the run is incomplete. Will be null if the run is not incomplete.
Details on why the run is incomplete. Will be null if the run is not incomplete.
The instructions that the assistant used for this run.
last_error: object { code, message } The last error associated with this run. Will be null if there are no errors.
The last error associated with this run. Will be null if there are no errors.
The maximum number of completion tokens specified to have been used over the course of the run.
The maximum number of prompt tokens specified to have been used over the course of the run.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The model that the assistant used for this run.
Whether to enable parallel function calling during tool use.
required_action: object { submit_tool_outputs, type } Details on the action required to continue the run. Will be null if no action is required.
Details on the action required to continue the run. Will be null if no action is required.
submit_tool_outputs: object { tool_calls } Details on the tool outputs needed for this run to continue.
Details on the tool outputs needed for this run to continue.
A list of the relevant tool calls.
A list of the relevant tool calls.
The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.
response_format: "auto" or ResponseFormatText { type } or ResponseFormatJSONObject { type } or ResponseFormatJSONSchema { json_schema, type } Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.
Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
response_format_json_object: object { type } JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
JSON object response format. An older method of generating JSON responses.
Using json_schema is recommended for models that support it. Note that the
model will not generate JSON without a system or user message instructing it
to do so.
response_format_json_schema: object { json_schema, type } JSON Schema response format. Used to generate structured JSON responses.
Learn more about Structured Outputs.
JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.
json_schema: object { name, description, schema, strict } Structured Outputs configuration options, including a JSON Schema.
Structured Outputs configuration options, including a JSON Schema.
The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the response format is for, used by the model to determine how to respond in the format.
The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.
Whether to enable strict schema adherence when generating the output.
If set to true, the model will always follow the exact schema defined
in the schema field. Only a subset of JSON Schema is supported when
strict is true. To learn more, read the Structured Outputs
guide.
status: "queued" or "in_progress" or "requires_action" or 6 moreThe status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.
The ID of the thread that was executed on as a part of this run.
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.
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.
Auto: "none" or "auto" or "required"none means the model will not call any tools and instead generates a message. auto 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.
none means the model will not call any tools and instead generates a message. auto 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.
The list of tools that the assistant used for this run.
The list of tools that the assistant used for this run.
file_search_tool: object { type, file_search }
file_search: optional object { max_num_results, ranking_options } Overrides for the file search tool.
Overrides for the file search tool.
The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.
Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.
ranking_options: optional object { score_threshold, ranker } The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.
See the file search tool documentation for more information.
function_tool: object { function, type }
function: object { name, description, parameters, strict }
The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
A description of what the function does, used by the model to choose when and how to call the function.
The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.
Omitting parameters defines a function with an empty parameter list.
Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.
truncation_strategy: object { type, last_messages } Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.
type: "auto" or "last_messages"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.
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.
BetaThreadsRunsSteps
Build Assistants that can call models and use tools.
List run steps
Retrieve run step
ModelsExpand Collapse
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
run_step: object { id, assistant_id, cancelled_at, 13 more } Represents a step in execution of a run.
Represents a step in execution of a run.
The ID of the assistant associated with the run step.
The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.
last_error: object { code, message } The last error associated with this run step. Will be null if there are no errors.
The last error associated with this run step. Will be null if there are no errors.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run that this run step is a part of.
status: "in_progress" or "cancelled" or "failed" or 2 moreThe status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.
step_details: MessageCreationStepDetails { message_creation, type } or ToolCallsStepDetails { tool_calls, type } The details of the run step.
The details of the run step.
message_creation_step_details: object { message_creation, type } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
The ID of the thread that was run.
type: "message_creation" or "tool_calls"The type of run step, which can be either message_creation or tool_calls.
The type of run step, which can be either message_creation or tool_calls.
run_step_delta: object { step_details } The delta containing the fields that have changed on the run step.
The delta containing the fields that have changed on the run step.
step_details: optional RunStepDeltaMessageDelta { type, message_creation } or ToolCallDeltaObject { type, tool_calls } The details of the run step.
The details of the run step.
run_step_delta_message_delta: object { type, message_creation } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_call_delta_object: object { type, tool_calls } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
run_step_delta_event: object { id, delta, object } Represents a run step delta i.e. any changed fields on a run step during streaming.
Represents a run step delta i.e. any changed fields on a run step during streaming.
delta: object { step_details } The delta containing the fields that have changed on the run step.
The delta containing the fields that have changed on the run step.
step_details: optional RunStepDeltaMessageDelta { type, message_creation } or ToolCallDeltaObject { type, tool_calls } The details of the run step.
The details of the run step.
run_step_delta_message_delta: object { type, message_creation } Details of the message creation by the run step.
Details of the message creation by the run step.
tool_call_delta_object: object { type, tool_calls } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
tool_call: CodeInterpreterToolCall { id, code_interpreter, type } or FileSearchToolCall { id, file_search, type } or FunctionToolCall { id, function, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
tool_call_delta: CodeInterpreterToolCallDelta { index, type, id, code_interpreter } or FileSearchToolCallDelta { file_search, index, type, id } or FunctionToolCallDelta { index, type, id, function } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
tool_call_delta_object: object { type, tool_calls } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call_delta: object { index, type, id, code_interpreter } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
The type of tool call. This is always going to be code_interpreter for this type of tool call.
code_interpreter: optional object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: optional array of CodeInterpreterLogs { index, type, logs } or CodeInterpreterOutputImage { index, type, image } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
code_interpreter_logs: object { index, type, logs } Text output from the Code Interpreter tool call as part of a run step.
Text output from the Code Interpreter tool call as part of a run step.
code_interpreter_output_image: object { index, type, image }
image: optional object { file_id }
The file ID of the image.
function_tool_call_delta: object { index, type, id, function }
The type of tool call. This is always going to be function for this type of tool call.
function: optional object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
tool_calls_step_details: object { tool_calls, type } Details of the tool call.
Details of the tool call.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.
code_interpreter_tool_call: object { id, code_interpreter, type } Details of the Code Interpreter tool call the run step was involved in.
Details of the Code Interpreter tool call the run step was involved in.
code_interpreter: object { input, outputs } The Code Interpreter tool call definition.
The Code Interpreter tool call definition.
outputs: array of object { logs, type } or object { image, type } The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
The outputs from the Code Interpreter tool call. Code Interpreter can output one or more items, including text (logs) or images (image). Each of these are represented by a different object type.
image: object { image, type }
image: object { file_id }
The file ID of the image.
file_search_tool_call: object { id, file_search, type }
file_search: object { ranking_options, results } For now, this is always going to be an empty object.
For now, this is always going to be an empty object.
function_tool_call: object { id, function, type }
function: object { arguments, name, output } The definition of the function that was called.
The definition of the function that was called.
The output of the function. This will be null if the outputs have not been submitted yet.
BetaThreadsMessages
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annotation: FileCitationAnnotation { end_index, file_citation, start_index, 2 more } or FilePathAnnotation { end_index, file_path, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
annotation_delta: FileCitationDeltaAnnotation { index, type, end_index, 3 more } or FilePathDeltaAnnotation { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_file_delta: object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
message: object { id, assistant_id, attachments, 11 more } Represents a message within a thread.
Represents a message within a thread.
If applicable, the ID of the assistant that authored this message.
attachments: array of object { file_id, tools } A list of files attached to the message, and the tools they were added to.
A list of files attached to the message, and the tools they were added to.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
incomplete_details: object { reason } On an incomplete message, details about why the message is incomplete.
On an incomplete message, details about why the message is incomplete.
Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.
Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.
status: "in_progress" or "incomplete" or "completed"The status of the message, which can be either in_progress, incomplete, or completed.
The status of the message, which can be either in_progress, incomplete, or completed.
The thread ID that this message belongs to.
message_content: ImageFileContentBlock { image_file, type } or ImageURLContentBlock { image_url, type } or TextContentBlock { text, type } or RefusalContentBlock { refusal, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
image_url_content_block: object { image_url, type } References an image URL in the content of a message.
References an image URL in the content of a message.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
message_content_delta: ImageFileDeltaBlock { index, type, image_file } or TextDeltaBlock { index, type, text } or RefusalDeltaBlock { index, type, refusal } or ImageURLDeltaBlock { index, type, image_url } References an image File in the content of a message.
References an image File in the content of a message.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
message_content_part_param: ImageFileContentBlock { image_file, type } or ImageURLContentBlock { image_url, type } or TextContentBlockParam { text, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file_content_block: object { image_file, type } References an image File in the content of a message.
References an image File in the content of a message.
image_file: object { file_id, detail }
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
message_delta: object { content, role } The delta containing the fields that have changed on the Message.
The delta containing the fields that have changed on the Message.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
message_delta_event: object { id, delta, object } Represents a message delta i.e. any changed fields on a message during streaming.
Represents a message delta i.e. any changed fields on a message during streaming.
delta: object { content, role } The delta containing the fields that have changed on the Message.
The delta containing the fields that have changed on the Message.
The content of the message in array of text and/or images.
The content of the message in array of text and/or images.
image_file_delta_block: object { index, type, image_file } References an image File in the content of a message.
References an image File in the content of a message.
image_file: optional object { detail, file_id }
detail: optional "auto" or "low" or "high"Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.
The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
text_content_block: object { text, type } The text content that is part of a message.
The text content that is part of a message.
text: object { annotations, value }
file_citation_annotation: object { end_index, file_citation, start_index, 2 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
text_delta: object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
text_delta_block: object { index, type, text } The text content that is part of a message.
The text content that is part of a message.
text: optional object { annotations, value }
file_citation_delta_annotation: object { index, type, end_index, 3 more } A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.
A citation within the message that points to a specific quote from a specific File associated with the assistant or the message. Generated when the assistant uses the “file_search” tool to search files.