b6iddlZddlmZmZddlmZddlmZddlm Z m Z ejejdZ Gd d ZGd d Zy) N)AsyncClientWrapperSyncClientWrapper)RequestOptions)LlmUsageCalculatorResponseModel)AsyncRawLlmUsageClientRawLlmUsageClient.c jeZdZdefdZedefdZdddeded e d e je de f d Zy) LlmUsageClientclient_wrapperc&t||_yN)r )r _raw_clientselfr s x/root/.openclaw/workspace/visionaryfx/venv/lib/python3.12/site-packages/elevenlabs/conversational_ai/llm_usage/client.py__init__zLlmUsageClient.__init__s,NKreturnc|jS)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawLlmUsageClient rrs rwith_raw_responsez LlmUsageClient.with_raw_responserNrequest_options prompt_lengthnumber_of_pages rag_enabledrcX|jj||||}|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 _responses rr$zLlmUsageClient.calculates8V$$..'+#+ / ~~r)__name__ __module__ __qualname__rrpropertyr rintbooltypingOptionalrrr$rrr r ssL*;L #4  <@ 11 1  1  8 1 )1rr c jeZdZdefdZedefdZdddeded e d e je de f d Zy) AsyncLlmUsageClientr c&t||_yr)r rrs rrzAsyncLlmUsageClient.__init__Rs1Prrc|jS)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawLlmUsageClient rrs rrz%AsyncLlmUsageClient.with_raw_responseUrrNrrrr rctK|jj||||d{}|jS7w)a Returns a list of LLM models and the expected cost for using them based on the provided values. Parameters ---------- prompt_length : int Length of the prompt in characters. number_of_pages : int Pages of content in PDF documents or URLs in the agent's knowledge base. rag_enabled : bool Whether RAG is enabled. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- LlmUsageCalculatorResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.llm_usage.calculate( prompt_length=1, number_of_pages=1, rag_enabled=True, ) asyncio.run(main()) r"Nr#r&s rr$zAsyncLlmUsageClient.calculate`sFf**44'+#+ 5  ~~  s #868)r(r)r*rrr+r rr,r-r.r/rrr$r0rrr2r2QssQ*<Q #9  <@ 99 9  9  8 9 )9rr2)r.core.client_wrapperrrcore.request_optionsr)types.llm_usage_calculator_response_modelr raw_clientr r castAnyOMITr r2r0rrr=sGH2XAv{{6::s#@@FHHr