# This file was auto-generated by Fern from our API Definition. import typing from ...core.client_wrapper import SyncClientWrapper from ...types.conversation_simulation_specification import ( ConversationSimulationSpecification, ) from ...types.prompt_evaluation_criteria import PromptEvaluationCriteria from ...core.request_options import RequestOptions from ...types.agent_simulated_chat_test_response_model import ( AgentSimulatedChatTestResponseModel, ) from ...core.jsonable_encoder import jsonable_encoder from ...core.serialization import convert_and_respect_annotation_metadata from ...core.unchecked_base_model import construct_type from ...errors.unprocessable_entity_error import UnprocessableEntityError from ...types.http_validation_error import HttpValidationError from json.decoder import JSONDecodeError from ...core.api_error import ApiError from ...core.client_wrapper import AsyncClientWrapper # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class AgentsClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def simulate_conversation( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AgentSimulatedChatTestResponseModel: """ 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- from elevenlabs import ( AgentConfigDbModel, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation( agent_id="21m00Tcm4TlvDq8ikWAM", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfigDbModel( first_message="Hello, how can I help you today?", language="en", ), ), ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/simulate-conversation", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "simulation_specification": convert_and_respect_annotation_metadata( object_=simulation_specification, annotation=ConversationSimulationSpecification, direction="write", ), "extra_evaluation_criteria": convert_and_respect_annotation_metadata( object_=extra_evaluation_criteria, annotation=typing.Sequence[PromptEvaluationCriteria], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AgentSimulatedChatTestResponseModel, construct_type( type_=AgentSimulatedChatTestResponseModel, # type: ignore object_=_response.json(), ), ) if _response.status_code == 422: raise UnprocessableEntityError( typing.cast( HttpValidationError, construct_type( type_=HttpValidationError, # type: ignore object_=_response.json(), ), ) ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) def simulate_conversation_stream( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- from elevenlabs import ( AgentConfigDbModel, ConversationSimulationSpecification, ElevenLabs, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.agents.simulate_conversation_stream( agent_id="21m00Tcm4TlvDq8ikWAM", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfigDbModel( first_message="Hello, how can I help you today?", language="en", ), ), ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/simulate-conversation/stream", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "simulation_specification": convert_and_respect_annotation_metadata( object_=simulation_specification, annotation=ConversationSimulationSpecification, direction="write", ), "extra_evaluation_criteria": convert_and_respect_annotation_metadata( object_=extra_evaluation_criteria, annotation=typing.Sequence[PromptEvaluationCriteria], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return if _response.status_code == 422: raise UnprocessableEntityError( typing.cast( HttpValidationError, construct_type( type_=HttpValidationError, # type: ignore object_=_response.json(), ), ) ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) class AsyncAgentsClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def simulate_conversation( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AgentSimulatedChatTestResponseModel: """ 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AgentSimulatedChatTestResponseModel Successful Response Examples -------- import asyncio from elevenlabs import ( AgentConfigDbModel, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation( agent_id="21m00Tcm4TlvDq8ikWAM", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfigDbModel( first_message="Hello, how can I help you today?", language="en", ), ), ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/simulate-conversation", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "simulation_specification": convert_and_respect_annotation_metadata( object_=simulation_specification, annotation=ConversationSimulationSpecification, direction="write", ), "extra_evaluation_criteria": convert_and_respect_annotation_metadata( object_=extra_evaluation_criteria, annotation=typing.Sequence[PromptEvaluationCriteria], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AgentSimulatedChatTestResponseModel, construct_type( type_=AgentSimulatedChatTestResponseModel, # type: ignore object_=_response.json(), ), ) if _response.status_code == 422: raise UnprocessableEntityError( typing.cast( HttpValidationError, construct_type( type_=HttpValidationError, # type: ignore object_=_response.json(), ), ) ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) async def simulate_conversation_stream( self, agent_id: str, *, simulation_specification: ConversationSimulationSpecification, extra_evaluation_criteria: typing.Optional[typing.Sequence[PromptEvaluationCriteria]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- None Examples -------- import asyncio from elevenlabs import ( AgentConfigDbModel, AsyncElevenLabs, ConversationSimulationSpecification, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.agents.simulate_conversation_stream( agent_id="21m00Tcm4TlvDq8ikWAM", simulation_specification=ConversationSimulationSpecification( simulated_user_config=AgentConfigDbModel( first_message="Hello, how can I help you today?", language="en", ), ), ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/simulate-conversation/stream", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "simulation_specification": convert_and_respect_annotation_metadata( object_=simulation_specification, annotation=ConversationSimulationSpecification, direction="write", ), "extra_evaluation_criteria": convert_and_respect_annotation_metadata( object_=extra_evaluation_criteria, annotation=typing.Sequence[PromptEvaluationCriteria], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return if _response.status_code == 422: raise UnprocessableEntityError( typing.cast( HttpValidationError, construct_type( type_=HttpValidationError, # type: ignore object_=_response.json(), ), ) ) _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json)