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