o lWi@snddlZddlmZmZddlmZddlmZddlm Z m Z e ej dZ Gd d d ZGd d d ZdS) N)AsyncClientWrapperSyncClientWrapper)RequestOptions)LlmUsageCalculatorResponseModel)AsyncRawLlmUsageClientRawLlmUsageClient.c @VeZdZdefddZedefddZddd ed ed e d e j e de f d dZdS)LlmUsageClientclient_wrappercCt|d|_dSN)r )r _raw_clientselfr rq/var/www/html/asistente-voz-ia/venv/lib/python3.10/site-packages/elevenlabs/conversational_ai/llm_usage/client.py__init__zLlmUsageClient.__init__returncC|jS)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawLlmUsageClient rrrrrwith_raw_response z LlmUsageClient.with_raw_responseNrequest_options prompt_lengthnumber_of_pages rag_enabledrcCs|jj||||d}|jS)a Returns a list of LLM models and the expected cost for using them based on the provided values. Parameters ---------- prompt_length : int Length of the prompt in characters. number_of_pages : int Pages of content in PDF documents or URLs in the agent's knowledge base. rag_enabled : bool Whether RAG is enabled. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- LlmUsageCalculatorResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.llm_usage.calculate( prompt_length=1, number_of_pages=1, rag_enabled=True, ) rrr rr calculatedatarrrr r _responserrrr#s+zLlmUsageClient.calculate)__name__ __module__ __qualname__rrpropertyr rintbooltypingOptionalrrr#rrrrr  r c @r )AsyncLlmUsageClientr cCr r)rrrrrrrRrzAsyncLlmUsageClient.__init__rcCr)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawLlmUsageClient rrrrrrUrz%AsyncLlmUsageClient.with_raw_responseNrrrr rcs"|jj||||dIdH}|jS)a Returns a list of LLM models and the expected cost for using them based on the provided values. Parameters ---------- prompt_length : int Length of the prompt in characters. number_of_pages : int Pages of content in PDF documents or URLs in the agent's knowledge base. rag_enabled : bool Whether RAG is enabled. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- LlmUsageCalculatorResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.llm_usage.calculate( prompt_length=1, number_of_pages=1, rag_enabled=True, ) asyncio.run(main()) r!Nr"r%rrrr#`s3 zAsyncLlmUsageClient.calculate)r'r(r)rrr*rrr+r,r-r.rrr#rrrrr0Qr/r0)r-core.client_wrapperrrcore.request_optionsr)types.llm_usage_calculator_response_modelr raw_clientrr castAnyOMITr r0rrrrs  C