chore: initial public snapshot for github upload
This commit is contained in:
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@@ -0,0 +1,726 @@
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import base64
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from typing import (
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Any,
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Dict,
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Iterable,
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List,
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Optional,
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Type,
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Union,
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cast,
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get_type_hints,
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overload,
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)
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from pydantic import BaseModel
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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.types.llms.openai import (
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ResponseAPIUsage,
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ResponsesAPIOptionalRequestParams,
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ResponsesAPIResponse,
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ResponseText,
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)
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from litellm.types.responses.main import DecodedResponseId
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from litellm.types.utils import (
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CompletionTokensDetailsWrapper,
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PromptTokensDetailsWrapper,
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SpecialEnums,
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Usage,
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)
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class ResponsesAPIRequestUtils:
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"""Helper utils for constructing ResponseAPI requests"""
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@staticmethod
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def _check_valid_arg(
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supported_params: Optional[List[str]],
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non_default_params: Dict,
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drop_params: Optional[bool],
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custom_llm_provider: Optional[str],
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model: str,
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):
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if supported_params is None:
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return
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unsupported_params = {}
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for k in non_default_params.keys():
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if k not in supported_params:
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unsupported_params[k] = non_default_params[k]
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if unsupported_params:
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if litellm.drop_params is True or (
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drop_params is not None and drop_params is True
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):
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pass
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else:
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raise litellm.UnsupportedParamsError(
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status_code=500,
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message=f"{custom_llm_provider} does not support parameters: {unsupported_params}, for model={model}. To drop these, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\n",
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)
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@staticmethod
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def get_optional_params_responses_api(
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model: str,
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responses_api_provider_config: BaseResponsesAPIConfig,
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response_api_optional_params: ResponsesAPIOptionalRequestParams,
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allowed_openai_params: Optional[List[str]] = None,
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) -> Dict:
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"""
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Get optional parameters for the responses API.
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Args:
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params: Dictionary of all parameters
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model: The model name
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responses_api_provider_config: The provider configuration for responses API
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Returns:
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A dictionary of supported parameters for the responses API
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"""
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from litellm.utils import _apply_openai_param_overrides
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# Remove None values and internal parameters
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# Get supported parameters for the model
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supported_params = responses_api_provider_config.get_supported_openai_params(
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model
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)
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non_default_params = cast(Dict, response_api_optional_params)
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# Check for unsupported parameters
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ResponsesAPIRequestUtils._check_valid_arg(
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supported_params=supported_params + (allowed_openai_params or []),
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non_default_params=non_default_params,
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drop_params=litellm.drop_params,
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custom_llm_provider=responses_api_provider_config.custom_llm_provider,
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model=model,
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)
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# Map parameters to provider-specific format
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mapped_params = responses_api_provider_config.map_openai_params(
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response_api_optional_params=response_api_optional_params,
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model=model,
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drop_params=litellm.drop_params,
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)
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# add any allowed_openai_params to the mapped_params
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mapped_params = _apply_openai_param_overrides(
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optional_params=mapped_params,
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non_default_params=non_default_params,
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allowed_openai_params=allowed_openai_params or [],
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)
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return mapped_params
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@staticmethod
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def get_requested_response_api_optional_param(
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params: Dict[str, Any],
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) -> ResponsesAPIOptionalRequestParams:
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"""
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Filter parameters to only include those defined in ResponsesAPIOptionalRequestParams.
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Args:
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params: Dictionary of parameters to filter
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Returns:
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ResponsesAPIOptionalRequestParams instance with only the valid parameters
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"""
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from litellm.utils import PreProcessNonDefaultParams
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valid_keys = get_type_hints(ResponsesAPIOptionalRequestParams).keys()
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custom_llm_provider = params.pop("custom_llm_provider", None)
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special_params = params.pop("kwargs", {})
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additional_drop_params = params.pop("additional_drop_params", None)
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non_default_params = (
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PreProcessNonDefaultParams.base_pre_process_non_default_params(
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passed_params=params,
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special_params=special_params,
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custom_llm_provider=custom_llm_provider,
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additional_drop_params=additional_drop_params,
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default_param_values={k: None for k in valid_keys},
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additional_endpoint_specific_params=["input"],
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)
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)
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# decode previous_response_id if it's a litellm encoded id
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if "previous_response_id" in non_default_params:
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decoded_previous_response_id = ResponsesAPIRequestUtils.decode_previous_response_id_to_original_previous_response_id(
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non_default_params["previous_response_id"]
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)
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non_default_params["previous_response_id"] = decoded_previous_response_id
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if "metadata" in non_default_params:
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from litellm.utils import add_openai_metadata
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converted_metadata = add_openai_metadata(non_default_params["metadata"])
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if converted_metadata is not None:
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non_default_params["metadata"] = converted_metadata
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else:
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non_default_params.pop("metadata", None)
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return cast(ResponsesAPIOptionalRequestParams, non_default_params)
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# fmt: off
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@overload
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@staticmethod
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def _update_responses_api_response_id_with_model_id(
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responses_api_response: ResponsesAPIResponse,
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custom_llm_provider: Optional[str],
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litellm_metadata: Optional[Dict[str, Any]] = None,
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) -> ResponsesAPIResponse:
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...
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@overload
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@staticmethod
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def _update_responses_api_response_id_with_model_id(
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responses_api_response: Dict[str, Any],
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custom_llm_provider: Optional[str],
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litellm_metadata: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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...
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# fmt: on
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@staticmethod
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def _update_responses_api_response_id_with_model_id(
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responses_api_response: Union[ResponsesAPIResponse, Dict[str, Any]],
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custom_llm_provider: Optional[str],
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litellm_metadata: Optional[Dict[str, Any]] = None,
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) -> Union[ResponsesAPIResponse, Dict[str, Any]]:
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"""Update the responses_api_response_id with model_id and custom_llm_provider.
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Handles both ``ResponsesAPIResponse`` objects and plain dictionaries returned
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by some streaming providers.
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"""
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litellm_metadata = litellm_metadata or {}
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model_info: Dict[str, Any] = litellm_metadata.get("model_info", {}) or {}
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model_id = model_info.get("id")
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# access the response id based on the object type
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if isinstance(responses_api_response, dict):
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response_id = responses_api_response.get("id")
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else:
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response_id = getattr(responses_api_response, "id", None)
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# If no response_id, return the response as-is (likely an error response)
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if response_id is None:
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return responses_api_response
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updated_id = ResponsesAPIRequestUtils._build_responses_api_response_id(
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model_id=model_id,
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custom_llm_provider=custom_llm_provider,
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response_id=response_id,
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)
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if isinstance(responses_api_response, dict):
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responses_api_response["id"] = updated_id
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else:
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responses_api_response.id = updated_id
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if litellm_metadata.get("encrypted_content_affinity_enabled"):
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responses_api_response = (
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ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response(
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response=responses_api_response,
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model_id=model_id,
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)
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)
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return responses_api_response
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@staticmethod
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def _build_encrypted_item_id(model_id: str, item_id: str) -> str:
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"""Encode model_id into an output item ID for encrypted-content items.
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Format: ``encitem_{base64("litellm:model_id:{model_id};item_id:{original_id}")}``
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"""
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assembled = f"litellm:model_id:{model_id};item_id:{item_id}"
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encoded = base64.b64encode(assembled.encode("utf-8")).decode("utf-8")
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return f"encitem_{encoded}"
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@staticmethod
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def _decode_encrypted_item_id(encoded_id: str) -> Optional[Dict[str, str]]:
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"""Decode a litellm-encoded encrypted-content item ID.
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Returns a dict with ``model_id`` and ``item_id`` keys, or ``None`` if
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the string is not a litellm-encoded item ID.
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"""
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if not encoded_id.startswith("encitem_"):
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return None
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try:
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cleaned = encoded_id[len("encitem_") :]
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# Restore any padding that may have been stripped in transit
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missing = len(cleaned) % 4
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if missing:
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cleaned += "=" * (4 - missing)
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decoded = base64.b64decode(cleaned.encode("utf-8")).decode("utf-8")
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# Split on first ";" only so that semicolons inside item_id are preserved
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parts = decoded.split(";", 1)
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if len(parts) < 2:
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return None
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model_id = parts[0].replace("litellm:model_id:", "")
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item_id = parts[1].replace("item_id:", "")
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return {"model_id": model_id, "item_id": item_id}
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except Exception:
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return None
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@staticmethod
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def _wrap_encrypted_content_with_model_id(
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encrypted_content: str, model_id: str
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) -> str:
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"""Wrap encrypted_content with model_id metadata for affinity routing.
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When Codex or other clients send items with encrypted_content but no ID,
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we encode the model_id directly into the encrypted_content itself.
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Format: ``litellm_enc:{base64("model_id:{model_id}")};{original_encrypted_content}``
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"""
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metadata = f"model_id:{model_id}"
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encoded_metadata = base64.b64encode(metadata.encode("utf-8")).decode("utf-8")
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return f"litellm_enc:{encoded_metadata};{encrypted_content}"
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@staticmethod
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def _unwrap_encrypted_content_with_model_id(
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wrapped_content: str,
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) -> tuple[Optional[str], str]:
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"""Unwrap encrypted_content to extract model_id and original content.
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Returns:
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Tuple of (model_id, original_encrypted_content).
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If not wrapped, returns (None, original_content).
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"""
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if not wrapped_content.startswith("litellm_enc:"):
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return None, wrapped_content
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try:
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# Split on first ";" to separate metadata from content
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parts = wrapped_content.split(";", 1)
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if len(parts) < 2:
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return None, wrapped_content
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metadata_b64 = parts[0].replace("litellm_enc:", "")
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original_content = parts[1]
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# Restore padding if needed
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missing = len(metadata_b64) % 4
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if missing:
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metadata_b64 += "=" * (4 - missing)
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decoded_metadata = base64.b64decode(metadata_b64.encode("utf-8")).decode(
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"utf-8"
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)
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model_id = decoded_metadata.replace("model_id:", "")
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return model_id, original_content
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except Exception:
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return None, wrapped_content
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@staticmethod
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def _update_encrypted_content_item_ids_in_response(
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response: Union["ResponsesAPIResponse", Dict[str, Any]],
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model_id: Optional[str],
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) -> Union["ResponsesAPIResponse", Dict[str, Any]]:
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"""Rewrite item IDs for output items that contain ``encrypted_content``.
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Encodes ``model_id`` into the item ID so that follow-up requests can be
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routed back to the originating deployment without any cache lookup.
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For items without an ID (e.g., from Codex), encodes model_id directly
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into the encrypted_content itself.
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"""
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if not model_id:
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return response
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output: Optional[list] = None
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if isinstance(response, dict):
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output = response.get("output")
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else:
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output = getattr(response, "output", None)
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if not isinstance(output, list):
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return response
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for item in output:
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if isinstance(item, dict):
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item_id = item.get("id")
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encrypted_content = item.get("encrypted_content")
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if encrypted_content and isinstance(encrypted_content, str):
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# Always wrap encrypted_content with model_id for redundancy
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item[
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"encrypted_content"
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] = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
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encrypted_content, model_id
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)
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# Also encode the ID if present
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if item_id and isinstance(item_id, str):
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item["id"] = ResponsesAPIRequestUtils._build_encrypted_item_id(
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model_id, item_id
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)
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else:
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item_id = getattr(item, "id", None)
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encrypted_content = getattr(item, "encrypted_content", None)
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if encrypted_content and isinstance(encrypted_content, str):
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# Always wrap encrypted_content with model_id for redundancy
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try:
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item.encrypted_content = ResponsesAPIRequestUtils._wrap_encrypted_content_with_model_id(
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encrypted_content, model_id
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)
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except AttributeError:
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pass
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# Also encode the ID if present
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if item_id and isinstance(item_id, str):
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try:
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item.id = ResponsesAPIRequestUtils._build_encrypted_item_id(
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model_id, item_id
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)
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||||
except AttributeError:
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pass
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return response
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@staticmethod
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def _restore_encrypted_content_item_ids_in_input(request_input: Any) -> Any:
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"""Decode litellm-encoded item IDs in request input back to original IDs.
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Called before forwarding the request to the upstream provider so the
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provider receives the original item IDs and unwrapped encrypted_content.
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Handles both:
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1. Items with encoded IDs (encitem_...)
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2. Items with wrapped encrypted_content (litellm_enc:...)
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"""
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||||
if not isinstance(request_input, list):
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return request_input
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for item in request_input:
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if isinstance(item, dict):
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item_id = item.get("id")
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if item_id and isinstance(item_id, str):
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decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(
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item_id
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)
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if decoded:
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item["id"] = decoded["item_id"]
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encrypted_content = item.get("encrypted_content")
|
||||
if encrypted_content and isinstance(encrypted_content, str):
|
||||
(
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||||
_,
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unwrapped,
|
||||
) = ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
|
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encrypted_content
|
||||
)
|
||||
if unwrapped != encrypted_content:
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item["encrypted_content"] = unwrapped
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||||
|
||||
return request_input
|
||||
|
||||
@staticmethod
|
||||
def _build_responses_api_response_id(
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||||
custom_llm_provider: Optional[str],
|
||||
model_id: Optional[str],
|
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response_id: str,
|
||||
) -> str:
|
||||
"""Build the responses_api_response_id"""
|
||||
assembled_id: str = str(
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||||
SpecialEnums.LITELLM_MANAGED_RESPONSE_COMPLETE_STR.value
|
||||
).format(custom_llm_provider, model_id, response_id)
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||||
base64_encoded_id: str = base64.b64encode(assembled_id.encode("utf-8")).decode(
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"utf-8"
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||||
)
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return f"resp_{base64_encoded_id}"
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||||
@staticmethod
|
||||
def _decode_responses_api_response_id(
|
||||
response_id: str,
|
||||
) -> DecodedResponseId:
|
||||
"""
|
||||
Decode the responses_api_response_id
|
||||
|
||||
Returns:
|
||||
DecodedResponseId: Structured tuple with custom_llm_provider, model_id, and response_id
|
||||
"""
|
||||
try:
|
||||
# Remove prefix and decode
|
||||
cleaned_id = response_id.replace("resp_", "")
|
||||
decoded_id = base64.b64decode(cleaned_id.encode("utf-8")).decode("utf-8")
|
||||
|
||||
# Parse components using known prefixes
|
||||
if ";" not in decoded_id:
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=None,
|
||||
model_id=None,
|
||||
response_id=response_id,
|
||||
)
|
||||
|
||||
parts = decoded_id.split(";")
|
||||
|
||||
# Format: litellm:custom_llm_provider:{};model_id:{};response_id:{}
|
||||
custom_llm_provider = None
|
||||
model_id = None
|
||||
|
||||
if (
|
||||
len(parts) >= 3
|
||||
): # Full format with custom_llm_provider, model_id, and response_id
|
||||
custom_llm_provider_part = parts[0]
|
||||
model_id_part = parts[1]
|
||||
response_part = parts[2]
|
||||
|
||||
custom_llm_provider = custom_llm_provider_part.replace(
|
||||
"litellm:custom_llm_provider:", ""
|
||||
)
|
||||
model_id = model_id_part.replace("model_id:", "")
|
||||
decoded_response_id = response_part.replace("response_id:", "")
|
||||
else:
|
||||
decoded_response_id = response_id
|
||||
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
model_id=model_id,
|
||||
response_id=decoded_response_id,
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_logger.debug(f"Error decoding response_id '{response_id}': {e}")
|
||||
return DecodedResponseId(
|
||||
custom_llm_provider=None,
|
||||
model_id=None,
|
||||
response_id=response_id,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def get_model_id_from_response_id(response_id: Optional[str]) -> Optional[str]:
|
||||
"""Get the model_id from the response_id"""
|
||||
if response_id is None:
|
||||
return None
|
||||
decoded_response_id = (
|
||||
ResponsesAPIRequestUtils._decode_responses_api_response_id(response_id)
|
||||
)
|
||||
return decoded_response_id.get("model_id") or None
|
||||
|
||||
@staticmethod
|
||||
def decode_previous_response_id_to_original_previous_response_id(
|
||||
previous_response_id: str,
|
||||
) -> str:
|
||||
"""
|
||||
Decode the previous_response_id to the original previous_response_id
|
||||
|
||||
Why?
|
||||
- LiteLLM encodes the `custom_llm_provider` and `model_id` into the `previous_response_id` this helps with maintaining session consistency when load balancing multiple deployments of the same model.
|
||||
- We cannot send the litellm encoded b64 to the upstream llm api, hence we decode it to the original `previous_response_id`
|
||||
|
||||
Args:
|
||||
previous_response_id: The previous_response_id to decode
|
||||
|
||||
Returns:
|
||||
The original previous_response_id
|
||||
"""
|
||||
decoded_response_id = (
|
||||
ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
previous_response_id
|
||||
)
|
||||
)
|
||||
return decoded_response_id.get("response_id", previous_response_id)
|
||||
|
||||
@staticmethod
|
||||
def convert_text_format_to_text_param(
|
||||
text_format: Optional[Union[Type["BaseModel"], dict]],
|
||||
text: Optional["ResponseText"] = None,
|
||||
) -> Optional["ResponseText"]:
|
||||
"""
|
||||
Convert text_format parameter to text parameter for the responses API.
|
||||
|
||||
Args:
|
||||
text_format: Pydantic model class or dict to convert to response format
|
||||
text: Existing text parameter (if provided, text_format is ignored)
|
||||
|
||||
Returns:
|
||||
ResponseText object with the converted format, or None if conversion fails
|
||||
"""
|
||||
if text_format is not None and text is None:
|
||||
from litellm.llms.base_llm.base_utils import type_to_response_format_param
|
||||
|
||||
# Convert Pydantic model to response format
|
||||
response_format = type_to_response_format_param(text_format)
|
||||
if response_format is not None:
|
||||
# Create ResponseText object with the format
|
||||
# The responses API expects the format to have name at the top level
|
||||
text = {
|
||||
"format": {
|
||||
"type": response_format["type"],
|
||||
"name": response_format["json_schema"]["name"],
|
||||
"schema": response_format["json_schema"]["schema"],
|
||||
"strict": response_format["json_schema"]["strict"],
|
||||
}
|
||||
}
|
||||
return text
|
||||
return text
|
||||
|
||||
@staticmethod
|
||||
def extract_mcp_headers_from_request(
|
||||
secret_fields: Optional[Dict[str, Any]],
|
||||
tools: Optional[Iterable[Any]],
|
||||
) -> tuple[
|
||||
Optional[str],
|
||||
Optional[Dict[str, Dict[str, str]]],
|
||||
Optional[Dict[str, str]],
|
||||
Optional[Dict[str, str]],
|
||||
]:
|
||||
"""
|
||||
Extract MCP auth headers from the request to pass to MCP server.
|
||||
Headers from tools.headers in request body should be passed to MCP server.
|
||||
"""
|
||||
from starlette.datastructures import Headers
|
||||
|
||||
from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
|
||||
MCPRequestHandler,
|
||||
)
|
||||
|
||||
# Extract headers from secret_fields which contains the original request headers
|
||||
raw_headers_from_request: Optional[Dict[str, str]] = None
|
||||
if secret_fields and isinstance(secret_fields, dict):
|
||||
raw_headers_from_request = secret_fields.get("raw_headers")
|
||||
|
||||
# Extract MCP-specific headers using MCPRequestHandler methods
|
||||
mcp_auth_header: Optional[str] = None
|
||||
mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None
|
||||
oauth2_headers: Optional[Dict[str, str]] = None
|
||||
|
||||
if raw_headers_from_request:
|
||||
headers_obj = Headers(raw_headers_from_request)
|
||||
mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(
|
||||
headers_obj
|
||||
)
|
||||
mcp_server_auth_headers = (
|
||||
MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers_obj)
|
||||
)
|
||||
oauth2_headers = MCPRequestHandler._get_oauth2_headers_from_headers(
|
||||
headers_obj
|
||||
)
|
||||
|
||||
if tools:
|
||||
for tool in tools:
|
||||
if isinstance(tool, dict) and tool.get("type") == "mcp":
|
||||
tool_headers = tool.get("headers", {})
|
||||
if tool_headers and isinstance(tool_headers, dict):
|
||||
# Merge tool headers into mcp_server_auth_headers
|
||||
# Extract server-specific headers from tool.headers
|
||||
headers_obj_from_tool = Headers(tool_headers)
|
||||
tool_mcp_server_auth_headers = (
|
||||
MCPRequestHandler._get_mcp_server_auth_headers_from_headers(
|
||||
headers_obj_from_tool
|
||||
)
|
||||
)
|
||||
if tool_mcp_server_auth_headers:
|
||||
if mcp_server_auth_headers is None:
|
||||
mcp_server_auth_headers = {}
|
||||
# Merge the headers from tool into existing headers
|
||||
for (
|
||||
server_alias,
|
||||
headers_dict,
|
||||
) in tool_mcp_server_auth_headers.items():
|
||||
if server_alias not in mcp_server_auth_headers:
|
||||
mcp_server_auth_headers[server_alias] = {}
|
||||
mcp_server_auth_headers[server_alias].update(
|
||||
headers_dict
|
||||
)
|
||||
# Also merge raw headers (non-prefixed headers from tool.headers)
|
||||
if raw_headers_from_request is None:
|
||||
raw_headers_from_request = {}
|
||||
raw_headers_from_request.update(tool_headers)
|
||||
|
||||
return (
|
||||
mcp_auth_header,
|
||||
mcp_server_auth_headers,
|
||||
oauth2_headers,
|
||||
raw_headers_from_request,
|
||||
)
|
||||
|
||||
|
||||
class ResponseAPILoggingUtils:
|
||||
@staticmethod
|
||||
def _is_response_api_usage(usage: Union[dict, ResponseAPIUsage]) -> bool:
|
||||
"""returns True if usage is from OpenAI Response API"""
|
||||
if isinstance(usage, ResponseAPIUsage):
|
||||
return True
|
||||
if "input_tokens" in usage and "output_tokens" in usage:
|
||||
return True
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _transform_response_api_usage_to_chat_usage(
|
||||
usage_input: Optional[Union[dict, ResponseAPIUsage]],
|
||||
) -> Usage:
|
||||
"""
|
||||
Transforms ResponseAPIUsage or ImageUsage to a Usage object.
|
||||
|
||||
Both have the same spec with input_tokens, output_tokens, and
|
||||
input_tokens_details (text_tokens, image_tokens).
|
||||
"""
|
||||
if usage_input is None:
|
||||
return Usage(
|
||||
prompt_tokens=0,
|
||||
completion_tokens=0,
|
||||
total_tokens=0,
|
||||
)
|
||||
response_api_usage: ResponseAPIUsage
|
||||
if isinstance(usage_input, dict):
|
||||
total_tokens = usage_input.get("total_tokens")
|
||||
if total_tokens is None:
|
||||
input_tokens = usage_input.get("input_tokens")
|
||||
output_tokens = usage_input.get("output_tokens")
|
||||
if input_tokens is not None and output_tokens is not None:
|
||||
total_tokens = input_tokens + output_tokens
|
||||
usage_input["total_tokens"] = total_tokens
|
||||
response_api_usage = ResponseAPIUsage(**usage_input)
|
||||
else:
|
||||
response_api_usage = usage_input
|
||||
prompt_tokens: int = response_api_usage.input_tokens or 0
|
||||
completion_tokens: int = response_api_usage.output_tokens or 0
|
||||
prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
|
||||
if response_api_usage.input_tokens_details:
|
||||
if isinstance(response_api_usage.input_tokens_details, dict):
|
||||
prompt_tokens_details = PromptTokensDetailsWrapper(
|
||||
**response_api_usage.input_tokens_details
|
||||
)
|
||||
else:
|
||||
prompt_tokens_details = PromptTokensDetailsWrapper(
|
||||
cached_tokens=getattr(
|
||||
response_api_usage.input_tokens_details, "cached_tokens", None
|
||||
),
|
||||
audio_tokens=getattr(
|
||||
response_api_usage.input_tokens_details, "audio_tokens", None
|
||||
),
|
||||
text_tokens=getattr(
|
||||
response_api_usage.input_tokens_details, "text_tokens", None
|
||||
),
|
||||
image_tokens=getattr(
|
||||
response_api_usage.input_tokens_details, "image_tokens", None
|
||||
),
|
||||
)
|
||||
completion_tokens_details: Optional[CompletionTokensDetailsWrapper] = None
|
||||
output_tokens_details = getattr(
|
||||
response_api_usage, "output_tokens_details", None
|
||||
)
|
||||
if output_tokens_details:
|
||||
completion_tokens_details = CompletionTokensDetailsWrapper(
|
||||
reasoning_tokens=getattr(
|
||||
output_tokens_details, "reasoning_tokens", None
|
||||
),
|
||||
image_tokens=getattr(output_tokens_details, "image_tokens", None),
|
||||
text_tokens=getattr(output_tokens_details, "text_tokens", None),
|
||||
)
|
||||
|
||||
chat_usage = Usage(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
prompt_tokens_details=prompt_tokens_details,
|
||||
completion_tokens_details=completion_tokens_details,
|
||||
)
|
||||
|
||||
# Preserve cost attribute if it exists on ResponseAPIUsage
|
||||
if hasattr(response_api_usage, "cost") and response_api_usage.cost is not None:
|
||||
setattr(chat_usage, "cost", response_api_usage.cost)
|
||||
|
||||
return chat_usage
|
||||
Reference in New Issue
Block a user