structure saas with tools
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from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast
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import httpx
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import litellm
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from litellm._logging import verbose_logger
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from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import *
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from litellm.types.responses.main import *
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from litellm.types.router import GenericLiteLLMParams
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from ..common_utils import OpenAIError
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
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def get_supported_openai_params(self, model: str) -> list:
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"""
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All OpenAI Responses API params are supported
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"""
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return [
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"input",
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"model",
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"include",
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"instructions",
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"max_output_tokens",
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"metadata",
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"parallel_tool_calls",
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"previous_response_id",
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"reasoning",
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"store",
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"stream",
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"temperature",
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"text",
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"tool_choice",
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"tools",
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"top_p",
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"truncation",
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"user",
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"extra_headers",
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"extra_query",
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"extra_body",
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"timeout",
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]
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def map_openai_params(
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self,
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response_api_optional_params: ResponsesAPIOptionalRequestParams,
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model: str,
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drop_params: bool,
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) -> Dict:
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"""No mapping applied since inputs are in OpenAI spec already"""
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return dict(response_api_optional_params)
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def transform_responses_api_request(
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self,
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model: str,
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input: Union[str, ResponseInputParam],
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response_api_optional_request_params: Dict,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Dict:
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"""No transform applied since inputs are in OpenAI spec already"""
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return dict(
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ResponsesAPIRequestParams(
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model=model, input=input, **response_api_optional_request_params
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)
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)
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def transform_response_api_response(
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self,
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model: str,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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) -> ResponsesAPIResponse:
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"""No transform applied since outputs are in OpenAI spec already"""
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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise OpenAIError(
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message=raw_response.text, status_code=raw_response.status_code
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)
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return ResponsesAPIResponse(**raw_response_json)
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def validate_environment(
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self,
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headers: dict,
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model: str,
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api_key: Optional[str] = None,
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) -> dict:
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api_key = (
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api_key
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or litellm.api_key
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or litellm.openai_key
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or get_secret_str("OPENAI_API_KEY")
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)
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headers.update(
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{
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"Authorization": f"Bearer {api_key}",
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}
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)
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return headers
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def get_complete_url(
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self,
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api_base: Optional[str],
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litellm_params: dict,
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) -> str:
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"""
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Get the endpoint for OpenAI responses API
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"""
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api_base = (
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api_base
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or litellm.api_base
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or get_secret_str("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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# Remove trailing slashes
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api_base = api_base.rstrip("/")
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return f"{api_base}/responses"
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def transform_streaming_response(
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self,
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model: str,
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parsed_chunk: dict,
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logging_obj: LiteLLMLoggingObj,
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) -> ResponsesAPIStreamingResponse:
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"""
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Transform a parsed streaming response chunk into a ResponsesAPIStreamingResponse
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"""
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# Convert the dictionary to a properly typed ResponsesAPIStreamingResponse
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verbose_logger.debug("Raw OpenAI Chunk=%s", parsed_chunk)
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event_type = str(parsed_chunk.get("type"))
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event_pydantic_model = OpenAIResponsesAPIConfig.get_event_model_class(
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event_type=event_type
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)
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return event_pydantic_model(**parsed_chunk)
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@staticmethod
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def get_event_model_class(event_type: str) -> Any:
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"""
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Returns the appropriate event model class based on the event type.
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Args:
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event_type (str): The type of event from the response chunk
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Returns:
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Any: The corresponding event model class
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Raises:
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ValueError: If the event type is unknown
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"""
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event_models = {
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ResponsesAPIStreamEvents.RESPONSE_CREATED: ResponseCreatedEvent,
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ResponsesAPIStreamEvents.RESPONSE_IN_PROGRESS: ResponseInProgressEvent,
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ResponsesAPIStreamEvents.RESPONSE_COMPLETED: ResponseCompletedEvent,
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ResponsesAPIStreamEvents.RESPONSE_FAILED: ResponseFailedEvent,
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ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE: ResponseIncompleteEvent,
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ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED: OutputItemAddedEvent,
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ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE: OutputItemDoneEvent,
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ResponsesAPIStreamEvents.CONTENT_PART_ADDED: ContentPartAddedEvent,
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ResponsesAPIStreamEvents.CONTENT_PART_DONE: ContentPartDoneEvent,
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ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA: OutputTextDeltaEvent,
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ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED: OutputTextAnnotationAddedEvent,
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ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE: OutputTextDoneEvent,
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ResponsesAPIStreamEvents.REFUSAL_DELTA: RefusalDeltaEvent,
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ResponsesAPIStreamEvents.REFUSAL_DONE: RefusalDoneEvent,
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ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA: FunctionCallArgumentsDeltaEvent,
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ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DONE: FunctionCallArgumentsDoneEvent,
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ResponsesAPIStreamEvents.FILE_SEARCH_CALL_IN_PROGRESS: FileSearchCallInProgressEvent,
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ResponsesAPIStreamEvents.FILE_SEARCH_CALL_SEARCHING: FileSearchCallSearchingEvent,
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ResponsesAPIStreamEvents.FILE_SEARCH_CALL_COMPLETED: FileSearchCallCompletedEvent,
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ResponsesAPIStreamEvents.WEB_SEARCH_CALL_IN_PROGRESS: WebSearchCallInProgressEvent,
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ResponsesAPIStreamEvents.WEB_SEARCH_CALL_SEARCHING: WebSearchCallSearchingEvent,
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ResponsesAPIStreamEvents.WEB_SEARCH_CALL_COMPLETED: WebSearchCallCompletedEvent,
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ResponsesAPIStreamEvents.ERROR: ErrorEvent,
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}
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model_class = event_models.get(cast(ResponsesAPIStreamEvents, event_type))
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if not model_class:
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return GenericEvent
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return model_class
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def should_fake_stream(
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self,
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model: Optional[str],
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stream: Optional[bool],
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custom_llm_provider: Optional[str] = None,
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) -> bool:
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if stream is not True:
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return False
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if model is not None:
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try:
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if (
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litellm.utils.supports_native_streaming(
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model=model,
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custom_llm_provider=custom_llm_provider,
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)
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is False
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):
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return True
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except Exception as e:
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verbose_logger.debug(
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f"Error getting model info in OpenAIResponsesAPIConfig: {e}"
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)
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return False
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#########################################################
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########## DELETE RESPONSE API TRANSFORMATION ##############
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#########################################################
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def transform_delete_response_api_request(
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self,
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response_id: str,
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api_base: str,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[str, Dict]:
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"""
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Transform the delete response API request into a URL and data
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OpenAI API expects the following request
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- DELETE /v1/responses/{response_id}
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"""
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url = f"{api_base}/{response_id}"
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data: Dict = {}
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return url, data
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def transform_delete_response_api_response(
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self,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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) -> DeleteResponseResult:
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"""
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Transform the delete response API response into a DeleteResponseResult
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"""
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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise OpenAIError(
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message=raw_response.text, status_code=raw_response.status_code
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)
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return DeleteResponseResult(**raw_response_json)
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