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https://github.com/BerriAI/litellm.git
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fix acompact_responses
This commit is contained in:
Ishaan Jaffer
parent
a10fee0cef
commit
091c3a3d00
2 changed files with 284 additions and 0 deletions
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@ -698,6 +698,88 @@ async def get_response_input_items(
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)
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@router.post(
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"/v1/responses/compact",
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dependencies=[Depends(user_api_key_auth)],
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tags=["responses"],
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)
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@router.post(
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"/responses/compact",
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dependencies=[Depends(user_api_key_auth)],
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tags=["responses"],
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)
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@router.post(
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"/openai/v1/responses/compact",
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dependencies=[Depends(user_api_key_auth)],
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tags=["responses"],
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)
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async def compact_response(
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Compact a response by running a compaction pass over a conversation.
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Returns encrypted, opaque items that can be used to reduce context size.
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Follows the OpenAI Responses API spec: https://platform.openai.com/docs/api-reference/responses/compact
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```bash
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curl -X POST http://localhost:4000/v1/responses/compact \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer sk-1234" \
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-d '{
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"model": "gpt-4o",
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"input": [{"role": "user", "content": "Hello"}]
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}'
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```
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"""
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from litellm.proxy.proxy_server import (
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_read_request_body,
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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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data = await _read_request_body(request=request)
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="acompact_responses",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.post(
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"/v1/responses/{response_id}/cancel",
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dependencies=[Depends(user_api_key_auth)],
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@ -1361,3 +1361,205 @@ def cancel_responses(
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completion_kwargs=local_vars,
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extra_kwargs=kwargs,
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)
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@client
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async def acompact_responses(
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input: Union[str, ResponseInputParam],
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model: str,
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instructions: Optional[str] = None,
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previous_response_id: Optional[str] = None,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Optional[Dict[str, Any]] = None,
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extra_query: Optional[Dict[str, Any]] = None,
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extra_body: Optional[Dict[str, Any]] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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# LiteLLM specific params,
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custom_llm_provider: Optional[str] = None,
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**kwargs,
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) -> ResponsesAPIResponse:
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"""
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Async version of the POST Compact Responses API
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POST /v1/responses/compact endpoint in the responses API
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Runs a compaction pass over a conversation, returning encrypted, opaque items.
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"""
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local_vars = locals()
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try:
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loop = asyncio.get_event_loop()
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kwargs["acompact_responses"] = True
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# get custom llm provider so we can use this for mapping exceptions
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if custom_llm_provider is None:
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_, custom_llm_provider, _, _ = litellm.get_llm_provider(
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model=model, api_base=local_vars.get("base_url", None)
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)
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func = partial(
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compact_responses,
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input=input,
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model=model,
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instructions=instructions,
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previous_response_id=previous_response_id,
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extra_headers=extra_headers,
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extra_query=extra_query,
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extra_body=extra_body,
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timeout=timeout,
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custom_llm_provider=custom_llm_provider,
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**kwargs,
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)
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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init_response = await loop.run_in_executor(None, func_with_context)
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if asyncio.iscoroutine(init_response):
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response = await init_response
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else:
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response = init_response
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# Update the responses_api_response_id with the model_id
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if isinstance(response, ResponsesAPIResponse):
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response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
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responses_api_response=response,
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litellm_metadata=kwargs.get("litellm_metadata", {}),
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custom_llm_provider=custom_llm_provider,
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)
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return response
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except Exception as e:
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raise litellm.exception_type(
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model=model,
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custom_llm_provider=custom_llm_provider,
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original_exception=e,
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completion_kwargs=local_vars,
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extra_kwargs=kwargs,
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)
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@client
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def compact_responses(
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input: Union[str, ResponseInputParam],
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model: str,
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instructions: Optional[str] = None,
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previous_response_id: Optional[str] = None,
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# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
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# The extra values given here take precedence over values defined on the client or passed to this method.
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extra_headers: Optional[Dict[str, Any]] = None,
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extra_query: Optional[Dict[str, Any]] = None,
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extra_body: Optional[Dict[str, Any]] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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# LiteLLM specific params,
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custom_llm_provider: Optional[str] = None,
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**kwargs,
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) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
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"""
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Synchronous version of the POST Compact Responses API
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POST /v1/responses/compact endpoint in the responses API
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Runs a compaction pass over a conversation, returning encrypted, opaque items.
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"""
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local_vars = locals()
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try:
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litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
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litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
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_is_async = kwargs.pop("acompact_responses", False) is True
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# get llm provider logic
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litellm_params = GenericLiteLLMParams(**kwargs)
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(
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model,
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custom_llm_provider,
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dynamic_api_key,
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dynamic_api_base,
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) = litellm.get_llm_provider(
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model=model,
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custom_llm_provider=custom_llm_provider,
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api_base=litellm_params.api_base,
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api_key=litellm_params.api_key,
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)
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if custom_llm_provider is None:
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raise ValueError("custom_llm_provider is required but passed as None")
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# get provider config
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responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
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ProviderConfigManager.get_provider_responses_api_config(
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model=model,
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provider=litellm.LlmProviders(custom_llm_provider),
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)
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)
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if responses_api_provider_config is None:
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raise ValueError(
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f"COMPACT responses is not supported for {custom_llm_provider}"
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)
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local_vars.update(kwargs)
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# Build optional params for compact endpoint
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response_api_optional_params: ResponsesAPIOptionalRequestParams = (
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ResponsesAPIRequestUtils.get_requested_response_api_optional_param(
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local_vars
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)
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)
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# Get optional parameters for the responses API
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responses_api_request_params: Dict = (
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ResponsesAPIRequestUtils.get_optional_params_responses_api(
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model=model,
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responses_api_provider_config=responses_api_provider_config,
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response_api_optional_params=response_api_optional_params,
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allowed_openai_params=None,
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)
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)
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# Pre Call logging
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litellm_logging_obj.update_environment_variables(
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model=model,
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optional_params=dict(responses_api_request_params),
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litellm_params={
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**responses_api_request_params,
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"litellm_call_id": litellm_call_id,
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},
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custom_llm_provider=custom_llm_provider,
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)
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# Call the handler with _is_async flag instead of directly calling the async handler
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response = base_llm_http_handler.compact_response_api_handler(
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model=model,
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input=input,
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responses_api_provider_config=responses_api_provider_config,
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response_api_optional_request_params=responses_api_request_params,
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litellm_params=litellm_params,
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logging_obj=litellm_logging_obj,
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custom_llm_provider=custom_llm_provider,
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extra_headers=extra_headers,
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extra_body=extra_body,
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timeout=timeout or request_timeout,
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_is_async=_is_async,
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client=kwargs.get("client"),
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shared_session=kwargs.get("shared_session"),
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)
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# Update the responses_api_response_id with the model_id
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if isinstance(response, ResponsesAPIResponse):
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response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
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responses_api_response=response,
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litellm_metadata=kwargs.get("litellm_metadata", {}),
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custom_llm_provider=custom_llm_provider,
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)
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return response
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except Exception as e:
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raise litellm.exception_type(
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model=model,
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custom_llm_provider=custom_llm_provider,
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original_exception=e,
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completion_kwargs=local_vars,
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extra_kwargs=kwargs,
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)
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