# File generated from our OpenAPI spec by Castiron. See CONTRIBUTING.md for details. from typing import Dict, List, Union, Optional, cast from typing_extensions import Literal, Annotated, TypeAlias from .tool import Tool from ..._utils import PropertyInfo from ..._models import BaseModel from .service_tier import ServiceTier from .response_error import ResponseError from .response_usage import ResponseUsage from .response_prompt import ResponsePrompt from .response_status import ResponseStatus from .tool_choice_mcp import ToolChoiceMcp from ..shared.metadata import Metadata from ..shared.reasoning import Reasoning from .tool_choice_shell import ToolChoiceShell from .tool_choice_types import ToolChoiceTypes from .tool_choice_custom import ToolChoiceCustom from .response_input_item import ResponseInputItem from .tool_choice_allowed import ToolChoiceAllowed from .tool_choice_options import ToolChoiceOptions from .response_output_item import ResponseOutputItem from .response_text_config import ResponseTextConfig from .tool_choice_function import ToolChoiceFunction from ..shared.responses_model import ResponsesModel from .tool_choice_apply_patch import ToolChoiceApplyPatch __all__ = [ "Response", "AccessPrograms", "IncompleteDetails", "ToolChoice", "ToolChoiceSpecificProgrammaticToolCallingParam", "Conversation", "Image", "InputTransformation", "Moderation", "ModerationInput", "ModerationInputModerationResult", "ModerationInputError", "ModerationOutput", "ModerationOutputModerationResult", "ModerationOutputError", "PromptCacheDiagnostics", "PromptCacheDiagnosticsCacheMiss", "PromptCacheDiagnosticsCacheHit", "PromptCacheDiagnosticsComparisonResponseNotFound", "PromptCacheDiagnosticsUnavailable", "PromptCacheOptions", "ToolUsage", "ToolUsageImageGen", "ToolUsageImageGenInputTokensDetails", "ToolUsageImageGenCachedTokensDetails", "ToolUsageImageGenOutputTokensDetails", "ToolUsageWebSearch", ] class AccessPrograms(BaseModel): cyber: Literal["standard", "daybreak_blue", "daybreak_red"] """The effective Cyber access program used for this response.""" class IncompleteDetails(BaseModel): """Details about why the response is incomplete.""" reason: Optional[Literal["max_output_tokens", "max_messages", "content_filter", "steered"]] = None """The reason why the response is incomplete. `steered` means the response stopped at a safe output boundary after a WebSocket `response.steer` event. The server can then create a successor response automatically with the queued input. """ class ToolChoiceSpecificProgrammaticToolCallingParam(BaseModel): type: Literal["programmatic_tool_calling"] """The tool to call. Always `programmatic_tool_calling`.""" ToolChoice: TypeAlias = Union[ ToolChoiceOptions, ToolChoiceAllowed, ToolChoiceTypes, ToolChoiceFunction, ToolChoiceMcp, ToolChoiceCustom, ToolChoiceSpecificProgrammaticToolCallingParam, ToolChoiceApplyPatch, ToolChoiceShell, ] class Conversation(BaseModel): """The conversation that this response belonged to. Input items and output items from this response were automatically added to this conversation. """ id: str """The unique ID of the conversation that this response was associated with.""" class Image(BaseModel): context_compression: Literal["enabled", "disabled"] """Effective image context optimization policy.""" class InputTransformation(BaseModel): id: str """The original reasoning item ID.""" action: Literal["removed"] reason: Literal["organization_mismatch", "end_user_mismatch", "model_mismatch", "prefix_mismatch", "turn_boundary"] type: Literal["reasoning"] class ModerationInputModerationResult(BaseModel): """A moderation result produced for the response input or output.""" categories: Dict[str, bool] """ A dictionary of moderation categories to booleans, True if the input is flagged under this category. """ category_applied_input_types: Dict[str, List[Literal["text", "image"]]] """Which modalities of input are reflected by the score for each category.""" category_scores: Dict[str, float] """A dictionary of moderation categories to scores.""" flagged: bool """A boolean indicating whether the content was flagged by any category.""" model: str """The moderation model that produced this result.""" type: Literal["moderation_result"] """ The object type, which was always `moderation_result` for successful moderation results. """ class ModerationInputError(BaseModel): """An error produced while attempting moderation for the response input or output.""" code: str """The error code.""" message: str """The error message.""" type: Literal["error"] """The object type, which was always `error` for moderation failures.""" ModerationInput: TypeAlias = Annotated[ Union[ModerationInputModerationResult, ModerationInputError], PropertyInfo(discriminator="type") ] class ModerationOutputModerationResult(BaseModel): """A moderation result produced for the response input or output.""" categories: Dict[str, bool] """ A dictionary of moderation categories to booleans, True if the input is flagged under this category. """ category_applied_input_types: Dict[str, List[Literal["text", "image"]]] """Which modalities of input are reflected by the score for each category.""" category_scores: Dict[str, float] """A dictionary of moderation categories to scores.""" flagged: bool """A boolean indicating whether the content was flagged by any category.""" model: str """The moderation model that produced this result.""" type: Literal["moderation_result"] """ The object type, which was always `moderation_result` for successful moderation results. """ class ModerationOutputError(BaseModel): """An error produced while attempting moderation for the response input or output.""" code: str """The error code.""" message: str """The error message.""" type: Literal["error"] """The object type, which was always `error` for moderation failures.""" ModerationOutput: TypeAlias = Annotated[ Union[ModerationOutputModerationResult, ModerationOutputError], PropertyInfo(discriminator="type") ] class Moderation(BaseModel): """ Moderation results for the response input and output, if moderated completions were requested. """ input: ModerationInput """Moderation for the response input.""" output: ModerationOutput """Moderation for the response output.""" class PromptCacheDiagnosticsCacheMiss(BaseModel): cache_missed_tokens: int """ The estimated number of input tokens affected after the first detected divergence. """ reason: Literal[ "model_changed", "prompt_cache_key_changed", "tools_changed", "text_format_changed", "reasoning_effort_changed", "verbosity_changed", "context_compacted", "input_changed", "service_tier_changed", ] """The reason prompt cache reuse did not occur.""" type: Literal["cache_miss"] comparison_reusable_tokens: Optional[int] = None """The raw token count of the reusable prefix in the compared response.""" class PromptCacheDiagnosticsCacheHit(BaseModel): type: Literal["cache_hit"] class PromptCacheDiagnosticsComparisonResponseNotFound(BaseModel): type: Literal["comparison_response_not_found"] class PromptCacheDiagnosticsUnavailable(BaseModel): type: Literal["unavailable"] PromptCacheDiagnostics: TypeAlias = Annotated[ Union[ PromptCacheDiagnosticsCacheMiss, PromptCacheDiagnosticsCacheHit, PromptCacheDiagnosticsComparisonResponseNotFound, PromptCacheDiagnosticsUnavailable, ], PropertyInfo(discriminator="type"), ] class PromptCacheOptions(BaseModel): """The prompt-caching options that were applied to the response. Supported for `gpt-5.6` and later models. """ mode: Literal["implicit", "explicit"] """Whether implicit prompt-cache breakpoints were enabled.""" ttl: Literal["30m", "12h"] """The minimum lifetime applied to each cache breakpoint.""" comparison_response_id: Optional[str] = None """The response ID supplied as the prompt cache diagnostics comparison.""" class ToolUsageImageGenInputTokensDetails(BaseModel): """The input tokens detailed information for the image generation.""" image_tokens: int """The number of image tokens in the input prompt.""" text_tokens: int """The number of text tokens in the input prompt.""" class ToolUsageImageGenCachedTokensDetails(BaseModel): cached_image_tokens: int cached_text_tokens: int class ToolUsageImageGenOutputTokensDetails(BaseModel): """The output token details for the image generation.""" image_tokens: int """The number of image output tokens generated by the model.""" text_tokens: int """The number of text output tokens generated by the model.""" class ToolUsageImageGen(BaseModel): """Aggregated usage statistics for image generation tool calls.""" input_tokens: int """The number of tokens (images and text) in the input prompt.""" input_tokens_details: ToolUsageImageGenInputTokensDetails """The input tokens detailed information for the image generation.""" output_tokens: int """The number of output tokens generated by the model.""" total_tokens: int """The total number of tokens (images and text) used for the image generation.""" cached_tokens: Optional[int] = None cached_tokens_details: Optional[ToolUsageImageGenCachedTokensDetails] = None output_tokens_details: Optional[ToolUsageImageGenOutputTokensDetails] = None """The output token details for the image generation.""" class ToolUsageWebSearch(BaseModel): """Aggregated billable usage statistics for web search tool calls.""" num_requests: int """The number of billable web search requests issued.""" class ToolUsage(BaseModel): """Usage reported by hosted tools, separate from base model `usage`. The field can be omitted when tool usage is unavailable. When present, tool families with no recorded usage have zero counts. `image_gen` sums available token counts across image generation calls. Calls with missing or incomplete usage records may be skipped. Output token details and cached input counts can be omitted. An omitted cache count means cache usage is unknown. `web_search.num_requests` counts billable searches. Discounted searches and actions such as opening a page are excluded. Use billing records to reconcile final charges. """ image_gen: ToolUsageImageGen """Aggregated usage statistics for image generation tool calls.""" web_search: ToolUsageWebSearch """Aggregated billable usage statistics for web search tool calls.""" class Response(BaseModel): id: str """Unique identifier for this Response.""" access_programs: Optional[AccessPrograms] = None created_at: float """Unix timestamp (in seconds) of when this Response was created.""" error: Optional[ResponseError] = None """An error object returned when the model fails to generate a Response.""" incomplete_details: Optional[IncompleteDetails] = None """Details about why the response is incomplete.""" instructions: Union[str, List[ResponseInputItem], None] = None """A system (or developer) message inserted into the model's context. When using along with `previous_response_id`, the instructions from a previous response will not be carried over to the next response. This makes it simple to swap out system (or developer) messages in new responses. """ metadata: Optional[Metadata] = None """Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters. """ model: ResponsesModel """Model ID used to generate the response, like `gpt-6-astra`. OpenAI offers a wide range of models with different capabilities, performance characteristics, and price points. Refer to the [model guide](https://developers.openai.com/api/docs/models) to browse and compare available models. """ object: Literal["response"] """The object type of this resource - always set to `response`.""" output: List[ResponseOutputItem] """An array of content items generated by the model. - The length and order of items in the `output` array is dependent on the model's response. - Rather than accessing the first item in the `output` array and assuming it's an `assistant` message with the content generated by the model, you might consider using the `output_text` property where supported in SDKs. """ parallel_tool_calls: bool """Whether to allow the model to run tool calls in parallel.""" temperature: Optional[float] = None """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. We generally recommend altering this or `top_p` but not both. """ tool_choice: ToolChoice """ How the model should select which tool (or tools) to use when generating a response. See the `tools` parameter to see how to specify which tools the model can call. """ tools: List[Tool] """An array of tools the model may call while generating a response. You can specify which tool to use by setting the `tool_choice` parameter. We support the following categories of tools: - **Built-in tools**: Tools that are provided by OpenAI that extend the model's capabilities, like [web search](https://developers.openai.com/api/docs/guides/tools-web-search) or [file search](https://developers.openai.com/api/docs/guides/tools-file-search). Learn more about [built-in tools](https://developers.openai.com/api/docs/guides/tools). - **MCP Tools**: Integrations with third-party systems via custom MCP servers or predefined connectors such as Google Drive and SharePoint. Learn more about [MCP Tools](https://developers.openai.com/api/docs/guides/tools-connectors-mcp). - **Function calls (custom tools)**: Functions that are defined by you, enabling the model to call your own code with strongly typed arguments and outputs. Learn more about [function calling](https://developers.openai.com/api/docs/guides/function-calling). You can also use custom tools to call your own code. """ top_p: Optional[float] = None """ 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. """ background: Optional[bool] = None """ Whether to run the model response in the background. [Learn more](https://developers.openai.com/api/docs/guides/background). """ completed_at: Optional[float] = None """ Unix timestamp (in seconds) of when this Response was completed. Only present when the status is `completed`. """ conversation: Optional[Conversation] = None """The conversation that this response belonged to. Input items and output items from this response were automatically added to this conversation. """ image: Optional[Image] = None input_transformations: Optional[List[InputTransformation]] = None """Reasoning inputs withheld from the model, in input order.""" max_output_tokens: Optional[int] = None """ An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](https://developers.openai.com/api/docs/guides/reasoning). """ max_tool_calls: Optional[int] = None """ The maximum number of total calls to built-in tools that can be processed in a response. This maximum number applies across all built-in tool calls, not per individual tool. Any further attempts to call a tool by the model will be ignored. """ moderation: Optional[Moderation] = None """ Moderation results for the response input and output, if moderated completions were requested. """ previous_response_id: Optional[str] = None """The unique ID of the previous response to the model. Use this to create multi-turn conversations. Learn more about [conversation state](https://developers.openai.com/api/docs/guides/conversation-state). Cannot be used in conjunction with `conversation`. """ prompt: Optional[ResponsePrompt] = None """ Reference to a prompt template and its variables. [Learn more](https://developers.openai.com/api/docs/guides/text?api-mode=responses#version-prompts-in-code). """ prompt_cache_diagnostics: Optional[PromptCacheDiagnostics] = None """Prompt cache diagnostics requested for this response.""" prompt_cache_key: Optional[str] = None """ Used by OpenAI to cache responses for similar requests to optimize your cache hit rates. Replaces the `user` field. [Learn more](https://developers.openai.com/api/docs/guides/prompt-caching). """ prompt_cache_options: Optional[PromptCacheOptions] = None """The prompt-caching options that were applied to the response. Supported for `gpt-5.6` and later models. """ prompt_cache_retention: Optional[Literal["in_memory", "24h"]] = None """Deprecated. Use `prompt_cache_options.ttl` instead. The retention policy for the prompt cache. Set to `24h` to enable extended prompt caching, which keeps cached prefixes active for longer, up to a maximum of 24 hours. [Learn more](https://developers.openai.com/api/docs/guides/prompt-caching#prompt-cache-retention). This field expresses a maximum retention policy, while `prompt_cache_options.ttl` expresses a minimum cache lifetime. The two fields are independent and do not interact. For `gpt-5.5`, `gpt-5.5-pro`, and future models, only `24h` is supported. For older models that support both `in_memory` and `24h`, the default depends on your organization's data retention policy: - Organizations without ZDR enabled default to `24h`. - Organizations with ZDR enabled default to `in_memory` when `prompt_cache_retention` is not specified. """ reasoning: Optional[Reasoning] = None """ Configuration options for [reasoning models](https://developers.openai.com/api/docs/guides/reasoning). """ safety_identifier: Optional[str] = None """ A stable identifier used to help detect users of your application that may be violating OpenAI's usage policies. The IDs should be a string that uniquely identifies each user, with a maximum length of 128 characters. We recommend hashing their username or email address, in order to avoid sending us any identifying information. [Learn more](https://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers). """ service_tier: Optional[ServiceTier] = None """Specifies the processing type used for serving the request. - If set to 'auto', then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use 'default'. - If set to 'default', then the request will be processed with the standard pricing and performance for the selected model. - If set to '[flex](https://developers.openai.com/api/docs/guides/flex-processing)', then the request will be processed with the Flex Processing service tier. - To opt-in to [Fast mode](https://developers.openai.com/api/docs/guides/fast-mode) at the request level, include the `service_tier=fast` or `service_tier=priority` parameter for Responses or Chat Completions. The response will show `service_tier=priority` regardless of if you specify `service_tier=fast` or `priority` in your request. - If set to 'ultrafast', then the request will be processed with the access-controlled Ultrafast Processing service tier. This tier is currently available for `gpt-5.6-sol`; a response served through it will show `service_tier=ultrafast`. - When not set, the default behavior is 'auto'. When the `service_tier` parameter is set, the response body will include the `service_tier` value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter. """ status: Optional[ResponseStatus] = None """The status of the response generation. One of `completed`, `failed`, `in_progress`, `cancelled`, `queued`, or `incomplete`. """ text: Optional[ResponseTextConfig] = None """Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more: - [Text inputs and outputs](https://developers.openai.com/api/docs/guides/text) - [Structured Outputs](https://developers.openai.com/api/docs/guides/structured-outputs) """ tool_usage: Optional[ToolUsage] = None """Usage reported by hosted tools, separate from base model `usage`. The field can be omitted when tool usage is unavailable. When present, tool families with no recorded usage have zero counts. `image_gen` sums available token counts across image generation calls. Calls with missing or incomplete usage records may be skipped. Output token details and cached input counts can be omitted. An omitted cache count means cache usage is unknown. `web_search.num_requests` counts billable searches. Discounted searches and actions such as opening a page are excluded. Use billing records to reconcile final charges. """ top_logprobs: Optional[int] = None """ An integer between 0 and 20 specifying the maximum number of most likely tokens to return at each token position, each with an associated log probability. In some cases, the number of returned tokens may be fewer than requested. """ truncation: Optional[Literal["auto", "disabled"]] = None """The truncation strategy to use for the model response. - `auto`: If the input to this Response exceeds the model's context window size, the model will truncate the response to fit the context window by dropping items from the beginning of the conversation. - `disabled` (default): If the input size will exceed the context window size for a model, the request will fail with a 400 error. """ usage: Optional[ResponseUsage] = None """ Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used. """ user: Optional[str] = None """This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use `prompt_cache_key` instead to maintain caching optimizations. A stable identifier for your end-users. Used to boost cache hit rates by better bucketing similar requests and to help OpenAI detect and prevent abuse. [Learn more](https://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers). """ @property def output_text(self) -> str: """Convenience property that aggregates all `output_text` items from the `output` list. If no `output_text` content blocks exist, then an empty string is returned. """ texts: list[str] = [] for output in self.output: if output.type == "message": for content in output.content: if content.type == "output_text": text = cast(Optional[str], content.text) if text is not None: texts.append(text) return "".join(texts)