o lWi@s\ddlmZddlZddlmZmZddlmZddlm Z ddl m Z ddl m Z dd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddl m!Z!ddl"m#Z#ddl$m%Z%m&Z&ej'rddl(m)Z)m*Z*ddl+m,Z,m-Z-ddl.m/Z/m0Z0ddl1m2Z2m3Z3e4ej5dZ6GdddZ7GdddZ8dS)) annotationsN)AsyncClientWrapperSyncClientWrapper)RequestOptions)+AdhocAgentConfigOverrideForTestRequestModel)!AgentPlatformSettingsRequestModel)#AgentSimulatedChatTestResponseModel) AgentSortBy)AgentWorkflowRequestModel)#ConversationSimulationSpecification)ConversationalConfig)CreateAgentResponseModel)GetAgentResponseModel)GetAgentsPageResponseModel)#GetTestSuiteInvocationResponseModel)PromptEvaluationCriteria)SingleTestRunRequestModel) SortDirection)AsyncRawAgentsClientRawAgentsClient)AsyncKnowledgeBaseClientKnowledgeBaseClient)AsyncLinkClient LinkClient)AsyncLlmUsageClientLlmUsageClient)AsyncWidgetClient WidgetClient.c@eZdZdTddZedUddZeeeed d dVddZd ddWddZd ddXd!d"Z eeeeed d#dYd%d&Z d d d d d d d d'dZd3d4Z ed d5d[d6d7Z eed d8d\d?d@Z eed d8d]dAdBZeed dCd^dJdKZedLdMZedNdOZedPdQZedRdSZd S)_ AgentsClientclient_wrapperrcC.t|d|_||_d|_d|_d|_d|_dSNr")r _raw_client_client_wrapper_widget_link_knowledge_base _llm_usageselfr"r.n/var/www/html/asistente-voz-ia/venv/lib/python3.10/site-packages/elevenlabs/conversational_ai/agents/client.py__init__#  zAgentsClient.__init__returnrcC|jS)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawAgentsClient r&r-r.r.r/with_raw_response+ zAgentsClient.with_raw_responseNplatform_settingsworkflownametagsrequest_optionsconversation_configr r92typing.Optional[AgentPlatformSettingsRequestModel]r:*typing.Optional[AgentWorkflowRequestModel]r;typing.Optional[str]r<%typing.Optional[typing.Sequence[str]]r=typing.Optional[RequestOptions]rcCs|jj||||||d}|jS)a Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfig Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- from elevenlabs import ConversationalConfig, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.create( conversation_config=ConversationalConfig(), ) r>r9r:r;r<r=r&createdatar-r>r9r:r;r<r= _responser.r.r/rF6s1zAgentsClient.creater=agent_idstrrcC|jj||d}|jS)a Retrieve config for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.get( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) rJr&getrGr-rKr=rIr.r.r/rOqszAgentsClient.getNonecCrM)aF Delete an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.delete( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) rJr&deleterGrPr.r.r/rSszAgentsClient.deleterD%typing.Optional[ConversationalConfig]c C |jj|||||||d}|jS)aO Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[ConversationalConfig] Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.update( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) rDr&updaterG r-rKr>r9r:r;r<r=rIr.r.r/rWs5 zAgentsClient.update page_sizesearcharchivedsort_directionsort_bycursorr=rZtyping.Optional[int]r[r\typing.Optional[bool]r]typing.Optional[SortDirection]r^typing.Optional[AgentSortBy]r_rc CrU)a  Returns a list of your agents and their metadata. Parameters ---------- page_size : typing.Optional[int] How many Agents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] Search by agents name. archived : typing.Optional[bool] Filter agents by archived status sort_direction : typing.Optional[SortDirection] The direction to sort the results sort_by : typing.Optional[AgentSortBy] The field to sort the results by cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentsPageResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.list( page_size=1, search="search", archived=True, sort_direction="asc", sort_by="name", cursor="cursor", ) rYr&listrG r-rZr[r\r]r^r_r=rIr.r.r/res: zAgentsClient.listr;r=cCs|jj|||d}|jS)a Create a new agent by duplicating an existing one Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : typing.Optional[str] A name to make the agent easier to find request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.duplicate( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) rgr& duplicaterGr-rKr;r=rIr.r.r/ri3s%zAgentsClient.duplicateextra_evaluation_criterianew_turns_limitr=simulation_specificationr rl:typing.Optional[typing.Sequence[PromptEvaluationCriteria]]rmr cC|jj|||||d}|jS)a Run a conversation between the agent and a simulated user. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- from elevenlabs import ( AgentConfig, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) rnrlrmr=r&simulate_conversationrGr-rKrnrlrmr=rIr.r.r/rs[s8z"AgentsClient.simulate_conversationcCrp)a Run a conversation between the agent and a simulated user and stream back the response. Response is streamed back as partial lists of messages that should be concatenated and once the conversation has complete a single final message with the conversation analysis will be sent. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- from elevenlabs import ( AgentConfig, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation_stream( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) rqr&simulate_conversation_streamrGrtr.r.r/rvs7z)AgentsClient.simulate_conversation_streamagent_config_override branch_idr=tests*typing.Sequence[SingleTestRunRequestModel]rxr r9r?r:r@r;rAr<rBr=rCr2rrKrLr=rCr2rrKrLr=rCr2rQrKrLr>rTr9r?r:r@r;rAr<rBr=rCr2rrZr`r[rAr\rar]rbr^rcr_rAr=rCr2rrKrLr;rAr=rCr2r rKrLrnr rlrormr`r=rCr2r rKrLrnr rlrormr`r=rCr2rQ rKrLrzr{rxr|ryrAr=rCr2r__name__ __module__ __qualname__r0propertyr6OMITrFrOrSrWrerirsrvrrrrrr.r.r.r/r!"sd  ;"CI-FE;   r!c@r )_AsyncAgentsClientr"rcCr#r$)rr&r'r(r)r*r+r,r.r.r/r09r1zAsyncAgentsClient.__init__r2rcCr3)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawAgentsClient r4r5r.r.r/r6Ar7z#AsyncAgentsClient.with_raw_responseNr8r>r r9r?r:r@r;rAr<rBr=rCrcs&|jj||||||dIdH}|jS)ay Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfig Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, ConversationalConfig client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.create( conversation_config=ConversationalConfig(), ) asyncio.run(main()) rDNrErHr.r.r/rFLs9 zAsyncAgentsClient.createrJrKrLrc|jj||dIdH}|jS)a Retrieve config for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.get( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) rJNrNrPr.r.r/rOs&zAsyncAgentsClient.getrQcr)a Delete an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.delete( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) rJNrRrPr.r.r/rSs#zAsyncAgentsClient.deleterDrTc (|jj|||||||dIdH}|jS)a Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[ConversationalConfig] Conversation configuration for an agent platform_settings : typing.Optional[AgentPlatformSettingsRequestModel] Platform settings for the agent are all settings that aren't related to the conversation orchestration and content. workflow : typing.Optional[AgentWorkflowRequestModel] Workflow for the agent. This is used to define the flow of the conversation and how the agent interacts with tools. name : typing.Optional[str] A name to make the agent easier to find tags : typing.Optional[typing.Sequence[str]] Tags to help classify and filter the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.update( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) rDNrVrXr.r.r/rWs=  zAsyncAgentsClient.updaterYrZr`r[r\rar]rbr^rcr_rc r)a Returns a list of your agents and their metadata. Parameters ---------- page_size : typing.Optional[int] How many Agents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] Search by agents name. archived : typing.Optional[bool] Filter agents by archived status sort_direction : typing.Optional[SortDirection] The direction to sort the results sort_by : typing.Optional[AgentSortBy] The field to sort the results by cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentsPageResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.list( page_size=1, search="search", archived=True, sort_direction="asc", sort_by="name", cursor="cursor", ) asyncio.run(main()) rYNrdrfr.r.r/re&sB  zAsyncAgentsClient.listrgcs |jj|||dIdH}|jS)an Create a new agent by duplicating an existing one Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : typing.Optional[str] A name to make the agent easier to find request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.duplicate( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", ) asyncio.run(main()) rgNrhrjr.r.r/riss-zAsyncAgentsClient.duplicaterkrnr rlrormr c$|jj|||||dIdH}|jS)a Run a conversation between the agent and a simulated user. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- import asyncio from elevenlabs import ( AgentConfig, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) asyncio.run(main()) rqNrrrtr.r.r/rss@ z'AsyncAgentsClient.simulate_conversationcr)aJ Run a conversation between the agent and a simulated user and stream back the response. Response is streamed back as partial lists of messages that should be concatenated and once the conversation has complete a single final message with the conversation analysis will be sent. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. simulation_specification : ConversationSimulationSpecification A specification detailing how the conversation should be simulated extra_evaluation_criteria : typing.Optional[typing.Sequence[PromptEvaluationCriteria]] A list of evaluation criteria to test new_turns_limit : typing.Optional[int] Maximum number of new turns to generate in the conversation simulation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- import asyncio from elevenlabs import ( AgentConfig, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation_stream( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfig( first_message="Hello, how can I help you today?", language="en", disable_first_message_interruptions=False, ), ), ) asyncio.run(main()) rqNrurtr.r.r/rvs? z.AsyncAgentsClient.simulate_conversation_streamrwrzr{rxr|ryrcr)a* Run selected tests on the agent with provided configuration. If the agent configuration is provided, it will be used to override default agent configuration. Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. tests : typing.Sequence[SingleTestRunRequestModel] List of tests to run on the agent agent_config_override : typing.Optional[AdhocAgentConfigOverrideForTestRequestModel] Configuration overrides to use for testing. If not provided, the agent's default configuration will be used. branch_id : typing.Optional[str] ID of the branch to run the tests on. If not provided, the tests will be run on the agent default configuration. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetTestSuiteInvocationResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, SingleTestRunRequestModel client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.run_tests( agent_id="agent_3701k3ttaq12ewp8b7qv5rfyszkz", tests=[ SingleTestRunRequestModel( test_id="test_id", ) ], ) asyncio.run(main()) r}Nr~rr.r.r/r4s: zAsyncAgentsClient.run_testscCr)Nr)rr%)r(rrr')r-rr.r.r/rwrzAsyncAgentsClient.widgetcCr)Nr)rr%)r)rrr')r-rr.r.r/rrzAsyncAgentsClient.linkcCr)Nr)rr%)r*rrr')r-rr.r.r/rrz AsyncAgentsClient.knowledge_basecCr)Nr)rr%)r+rrr')r-rr.r.r/rrzAsyncAgentsClient.llm_usage)r"r)r2rrrrrrrrrrrr.r.r.r/r8sf  D)*KQ5NMC   r)9 __future__rtypingcore.client_wrapperrrcore.request_optionsr8types.adhoc_agent_config_override_for_test_request_modelr+types.agent_platform_settings_request_modelr.types.agent_simulated_chat_test_response_modelr types.agent_sort_byr "types.agent_workflow_request_modelr +types.conversation_simulation_specificationr types.conversational_configr !types.create_agent_response_modelrtypes.get_agent_response_modelr$types.get_agents_page_response_modelr.types.get_test_suite_invocation_response_modelr types.prompt_evaluation_criteriar#types.single_test_run_request_modelrtypes.sort_directionr raw_clientrr TYPE_CHECKINGrrrrrrrrrrrrcastAnyrr!rr.r.r.r/s>