# This file was auto-generated by Fern from our API Definition. import typing from ..core.client_wrapper import SyncClientWrapper from .agents.client import AgentsClient from .sip_trunk.client import SipTrunkClient from .knowledge_base.client import KnowledgeBaseClient from ..core.request_options import RequestOptions from ..types.conversation_signed_url_response_model import ( ConversationSignedUrlResponseModel, ) 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 ..types.conversation_initiation_client_data_request_input import ( ConversationInitiationClientDataRequestInput, ) from ..types.twilio_outbound_call_response import TwilioOutboundCallResponse from ..core.serialization import convert_and_respect_annotation_metadata from ..types.conversational_config import ConversationalConfig from ..types.agent_platform_settings_request_model import ( AgentPlatformSettingsRequestModel, ) from ..types.create_agent_response_model import CreateAgentResponseModel from ..types.get_agent_response_model import GetAgentResponseModel from ..core.jsonable_encoder import jsonable_encoder from ..types.get_agent_embed_response_model import GetAgentEmbedResponseModel from ..types.get_agent_link_response_model import GetAgentLinkResponseModel from .. import core from ..types.post_agent_avatar_response_model import PostAgentAvatarResponseModel from ..types.get_agents_page_response_model import GetAgentsPageResponseModel from ..types.evaluation_success_result import EvaluationSuccessResult from ..types.get_conversations_page_response_model import ( GetConversationsPageResponseModel, ) from ..types.get_conversation_response_model import GetConversationResponseModel from ..types.user_feedback_score import UserFeedbackScore from .types.conversational_ai_create_phone_number_request_body import ( ConversationalAiCreatePhoneNumberRequestBody, ) from ..types.create_phone_number_response_model import CreatePhoneNumberResponseModel from .types.conversational_ai_get_phone_number_response import ( ConversationalAiGetPhoneNumberResponse, ) from .types.conversational_ai_update_phone_number_response import ( ConversationalAiUpdatePhoneNumberResponse, ) from .types.conversational_ai_get_phone_numbers_response_item import ( ConversationalAiGetPhoneNumbersResponseItem, ) from ..types.knowledge_base_document_type import KnowledgeBaseDocumentType from ..types.get_knowledge_base_list_response_model import ( GetKnowledgeBaseListResponseModel, ) from ..types.add_knowledge_base_response_model import AddKnowledgeBaseResponseModel from .types.conversational_ai_get_knowledge_base_document_by_id_response import ( ConversationalAiGetKnowledgeBaseDocumentByIdResponse, ) from .types.conversational_ai_update_knowledge_base_document_response import ( ConversationalAiUpdateKnowledgeBaseDocumentResponse, ) from ..types.get_knowledge_base_dependent_agents_response_model import ( GetKnowledgeBaseDependentAgentsResponseModel, ) from ..types.knowledge_base_document_chunk_response_model import ( KnowledgeBaseDocumentChunkResponseModel, ) from ..types.get_conv_ai_settings_response_model import GetConvAiSettingsResponseModel from ..types.conversation_initiation_client_data_webhook import ( ConversationInitiationClientDataWebhook, ) from ..types.conv_ai_webhooks import ConvAiWebhooks from ..types.get_conv_ai_dashboard_settings_response_model import ( GetConvAiDashboardSettingsResponseModel, ) from .types.patch_conv_ai_dashboard_settings_request_charts_item import ( PatchConvAiDashboardSettingsRequestChartsItem, ) from ..types.get_workspace_secrets_response_model import ( GetWorkspaceSecretsResponseModel, ) from ..types.post_workspace_secret_response_model import ( PostWorkspaceSecretResponseModel, ) from ..types.batch_call_detailed_response import BatchCallDetailedResponse from ..core.client_wrapper import AsyncClientWrapper from .agents.client import AsyncAgentsClient from .sip_trunk.client import AsyncSipTrunkClient from .knowledge_base.client import AsyncKnowledgeBaseClient # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class ConversationalAiClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper self.agents = AgentsClient(client_wrapper=self._client_wrapper) self.sip_trunk = SipTrunkClient(client_wrapper=self._client_wrapper) self.knowledge_base = KnowledgeBaseClient(client_wrapper=self._client_wrapper) def get_signed_url( self, *, agent_id: str, request_options: typing.Optional[RequestOptions] = None ) -> ConversationSignedUrlResponseModel: """ Get a signed url to start a conversation with an agent with an agent that requires authorization Parameters ---------- agent_id : str The id of the agent you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationSignedUrlResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_signed_url( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/conversation/get-signed-url", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "agent_id": agent_id, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationSignedUrlResponseModel, construct_type( type_=ConversationSignedUrlResponseModel, # 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 twilio_outbound_call( self, *, agent_id: str, agent_phone_number_id: str, to_number: str, conversation_initiation_client_data: typing.Optional[ConversationInitiationClientDataRequestInput] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> TwilioOutboundCallResponse: """ Handle an outbound call via Twilio Parameters ---------- agent_id : str agent_phone_number_id : str to_number : str conversation_initiation_client_data : typing.Optional[ConversationInitiationClientDataRequestInput] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- TwilioOutboundCallResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.twilio_outbound_call( agent_id="agent_id", agent_phone_number_id="agent_phone_number_id", to_number="to_number", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/twilio/outbound-call", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "agent_id": agent_id, "agent_phone_number_id": agent_phone_number_id, "to_number": to_number, "conversation_initiation_client_data": convert_and_respect_annotation_metadata( object_=conversation_initiation_client_data, annotation=ConversationInitiationClientDataRequestInput, direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( TwilioOutboundCallResponse, construct_type( type_=TwilioOutboundCallResponse, # 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 create_agent( self, *, conversation_config: ConversationalConfig, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ 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. 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.create_agent( conversation_config=ConversationalConfig(), ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/agents/create", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "conversation_config": convert_and_respect_annotation_metadata( object_=conversation_config, annotation=ConversationalConfig, direction="write", ), "platform_settings": convert_and_respect_annotation_metadata( object_=platform_settings, annotation=AgentPlatformSettingsRequestModel, direction="write", ), "name": name, "tags": tags, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( CreateAgentResponseModel, construct_type( type_=CreateAgentResponseModel, # 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 get_agent( self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> GetAgentResponseModel: """ 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.get_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentResponseModel, construct_type( type_=GetAgentResponseModel, # 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 delete_agent(self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ 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.delete_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) 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) def update_agent( self, agent_id: str, *, conversation_config: typing.Optional[typing.Optional[typing.Any]] = OMIT, platform_settings: typing.Optional[typing.Optional[typing.Any]] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentResponseModel: """ Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[typing.Optional[typing.Any]] platform_settings : typing.Optional[typing.Optional[typing.Any]] 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.update_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "conversation_config": conversation_config, "platform_settings": platform_settings, "name": name, "tags": tags, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentResponseModel, construct_type( type_=GetAgentResponseModel, # 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 get_agent_widget( self, agent_id: str, *, conversation_signature: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentEmbedResponseModel: """ Retrieve the widget configuration for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_signature : typing.Optional[str] An expiring token that enables a websocket conversation to start. These can be generated for an agent using the /v1/convai/conversation/get-signed-url endpoint request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentEmbedResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_agent_widget( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/widget", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "conversation_signature": conversation_signature, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentEmbedResponseModel, construct_type( type_=GetAgentEmbedResponseModel, # 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 get_agent_link( self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> GetAgentLinkResponseModel: """ Get the current link used to share the agent with others Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentLinkResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_agent_link( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/link", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentLinkResponseModel, construct_type( type_=GetAgentLinkResponseModel, # 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 post_agent_avatar( self, agent_id: str, *, avatar_file: core.File, request_options: typing.Optional[RequestOptions] = None, ) -> PostAgentAvatarResponseModel: """ Sets the avatar for an agent displayed in the widget Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. avatar_file : core.File See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- PostAgentAvatarResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.post_agent_avatar( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/avatar", base_url=self._client_wrapper.get_environment().base, method="POST", data={}, files={ "avatar_file": avatar_file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( PostAgentAvatarResponseModel, construct_type( type_=PostAgentAvatarResponseModel, # 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 get_agents( self, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentsPageResponseModel: """ Returns a list of your agents and their metadata. Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. 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. 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.get_agents() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/agents", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentsPageResponseModel, construct_type( type_=GetAgentsPageResponseModel, # 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 get_conversations( self, *, cursor: typing.Optional[str] = None, agent_id: typing.Optional[str] = None, call_successful: typing.Optional[EvaluationSuccessResult] = None, call_start_before_unix: typing.Optional[int] = None, call_start_after_unix: typing.Optional[int] = None, page_size: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetConversationsPageResponseModel: """ Get all conversations of agents that user owns. With option to restrict to a specific agent. Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. agent_id : typing.Optional[str] The id of the agent you're taking the action on. call_successful : typing.Optional[EvaluationSuccessResult] The result of the success evaluation call_start_before_unix : typing.Optional[int] Unix timestamp (in seconds) to filter conversations up to this start date. call_start_after_unix : typing.Optional[int] Unix timestamp (in seconds) to filter conversations after to this start date. page_size : typing.Optional[int] How many conversations to return at maximum. Can not exceed 100, defaults to 30. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConversationsPageResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_conversations() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/conversations", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "agent_id": agent_id, "call_successful": call_successful, "call_start_before_unix": call_start_before_unix, "call_start_after_unix": call_start_after_unix, "page_size": page_size, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConversationsPageResponseModel, construct_type( type_=GetConversationsPageResponseModel, # 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 get_conversation( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> GetConversationResponseModel: """ Get the details of a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConversationResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_conversation( conversation_id="123", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConversationResponseModel, construct_type( type_=GetConversationResponseModel, # 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 delete_conversation( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.delete_conversation( conversation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 get_conversation_audio( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Iterator[bytes]: """ Get the audio recording of a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Yields ------ typing.Iterator[bytes] Successful Response """ with self._client_wrapper.httpx_client.stream( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) as _response: try: if 200 <= _response.status_code < 300: _chunk_size = request_options.get("chunk_size", 1024) if request_options is not None else 1024 for _chunk in _response.iter_bytes(chunk_size=_chunk_size): yield _chunk return _response.read() 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 post_conversation_feedback( self, conversation_id: str, *, feedback: UserFeedbackScore, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Send the feedback for the given conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. feedback : UserFeedbackScore Either 'like' or 'dislike' to indicate the feedback for the conversation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.post_conversation_feedback( conversation_id="21m00Tcm4TlvDq8ikWAM", feedback="like", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/feedback", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "feedback": feedback, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 create_phone_number( self, *, request: ConversationalAiCreatePhoneNumberRequestBody, request_options: typing.Optional[RequestOptions] = None, ) -> CreatePhoneNumberResponseModel: """ Import Phone Number from provider configuration (Twilio or SIP trunk) Parameters ---------- request : ConversationalAiCreatePhoneNumberRequestBody request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreatePhoneNumberResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs from elevenlabs.conversational_ai import ( ConversationalAiCreatePhoneNumberRequestBody_Twilio, ) client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_phone_number( request=ConversationalAiCreatePhoneNumberRequestBody_Twilio( phone_number="phone_number", label="label", sid="sid", token="token", ), ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/phone-numbers/create", base_url=self._client_wrapper.get_environment().base, method="POST", json=convert_and_respect_annotation_metadata( object_=request, annotation=ConversationalAiCreatePhoneNumberRequestBody, direction="write", ), request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( CreatePhoneNumberResponseModel, construct_type( type_=CreatePhoneNumberResponseModel, # 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 get_phone_number( self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiGetPhoneNumberResponse: """ Retrieve Phone Number details by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiGetPhoneNumberResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiGetPhoneNumberResponse, construct_type( type_=ConversationalAiGetPhoneNumberResponse, # 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 delete_phone_number( self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete Phone Number by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.delete_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 update_phone_number( self, phone_number_id: str, *, agent_id: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiUpdatePhoneNumberResponse: """ Update Phone Number details by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. agent_id : typing.Optional[str] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiUpdatePhoneNumberResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.update_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "agent_id": agent_id, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiUpdatePhoneNumberResponse, construct_type( type_=ConversationalAiUpdatePhoneNumberResponse, # 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 get_phone_numbers( self, *, request_options: typing.Optional[RequestOptions] = None ) -> typing.List[ConversationalAiGetPhoneNumbersResponseItem]: """ Retrieve all Phone Numbers Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.List[ConversationalAiGetPhoneNumbersResponseItem] Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_phone_numbers() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/phone-numbers/", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.List[ConversationalAiGetPhoneNumbersResponseItem], construct_type( type_=typing.List[ConversationalAiGetPhoneNumbersResponseItem], # 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 get_knowledge_base_list( self, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, show_only_owned_documents: typing.Optional[bool] = None, types: typing.Optional[ typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]] ] = None, use_typesense: typing.Optional[bool] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetKnowledgeBaseListResponseModel: """ Get a list of available knowledge base documents Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. page_size : typing.Optional[int] How many documents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] If specified, the endpoint returns only such knowledge base documents whose names start with this string. show_only_owned_documents : typing.Optional[bool] If set to true, the endpoint will return only documents owned by you (and not shared from somebody else). types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]] If present, the endpoint will return only documents of the given types. use_typesense : typing.Optional[bool] If set to true, the endpoint will use typesense DB to search for the documents). request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetKnowledgeBaseListResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_knowledge_base_list() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, "show_only_owned_documents": show_only_owned_documents, "types": types, "use_typesense": use_typesense, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseListResponseModel, construct_type( type_=GetKnowledgeBaseListResponseModel, # 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 add_to_knowledge_base( self, *, name: typing.Optional[str] = OMIT, url: typing.Optional[str] = OMIT, file: typing.Optional[core.File] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Upload a file or webpage URL to create a knowledge base document.
After creating the document, update the agent's knowledge base by calling [Update agent](/docs/conversational-ai/api-reference/agents/update-agent). Parameters ---------- name : typing.Optional[str] A custom, human-readable name for the document. url : typing.Optional[str] URL to a page of documentation that the agent will have access to in order to interact with users. file : typing.Optional[core.File] See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.add_to_knowledge_base() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base", base_url=self._client_wrapper.get_environment().base, method="POST", data={ "name": name, "url": url, }, files={ "file": file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_url_document( self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document generated by scraping the given webpage. Parameters ---------- url : str URL to a page of documentation that the agent will have access to in order to interact with users. name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_knowledge_base_url_document( url="url", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/url", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "url": url, "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_file_document( self, *, file: core.File, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document generated form the uploaded file. Parameters ---------- file : core.File See core.File for more documentation name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_knowledge_base_file_document() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/file", base_url=self._client_wrapper.get_environment().base, method="POST", data={ "name": name, }, files={ "file": file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_text_document( self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document containing the provided text. Parameters ---------- text : str Text content to be added to the knowledge base. name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_knowledge_base_text_document( text="text", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/text", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "text": text, "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 get_knowledge_base_document_by_id( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiGetKnowledgeBaseDocumentByIdResponse: """ Get details about a specific documentation making up the agent's knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiGetKnowledgeBaseDocumentByIdResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_knowledge_base_document_by_id( documentation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiGetKnowledgeBaseDocumentByIdResponse, construct_type( type_=ConversationalAiGetKnowledgeBaseDocumentByIdResponse, # 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 delete_knowledge_base_document( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete a document from the knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.delete_knowledge_base_document( documentation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 update_knowledge_base_document( self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiUpdateKnowledgeBaseDocumentResponse: """ Update the name of a document Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. name : str A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiUpdateKnowledgeBaseDocumentResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.update_knowledge_base_document( documentation_id="21m00Tcm4TlvDq8ikWAM", name="name", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiUpdateKnowledgeBaseDocumentResponse, construct_type( type_=ConversationalAiUpdateKnowledgeBaseDocumentResponse, # 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 get_dependent_agents( self, documentation_id: str, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetKnowledgeBaseDependentAgentsResponseModel: """ Get a list of agents depending on this knowledge base document Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. page_size : typing.Optional[int] How many documents to return at maximum. Can not exceed 100, defaults to 30. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetKnowledgeBaseDependentAgentsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_dependent_agents( documentation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/dependent-agents", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseDependentAgentsResponseModel, construct_type( type_=GetKnowledgeBaseDependentAgentsResponseModel, # 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 get_knowledge_base_document_content( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ Get the entire content of a document from the knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. 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.get_knowledge_base_document_content( documentation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/content", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) 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) def get_knowledge_base_document_part_by_id( self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> KnowledgeBaseDocumentChunkResponseModel: """ Get details about a specific documentation part used by RAG. Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. chunk_id : str The id of a document RAG chunk from the knowledge base. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- KnowledgeBaseDocumentChunkResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_knowledge_base_document_part_by_id( documentation_id="21m00Tcm4TlvDq8ikWAM", chunk_id="chunk_id", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/chunk/{jsonable_encoder(chunk_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( KnowledgeBaseDocumentChunkResponseModel, construct_type( type_=KnowledgeBaseDocumentChunkResponseModel, # 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 get_settings( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetConvAiSettingsResponseModel: """ Retrieve Convai settings for the workspace Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiSettingsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_settings() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/settings", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiSettingsResponseModel, construct_type( type_=GetConvAiSettingsResponseModel, # 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 update_settings( self, *, conversation_initiation_client_data_webhook: typing.Optional[ConversationInitiationClientDataWebhook] = OMIT, webhooks: typing.Optional[ConvAiWebhooks] = OMIT, rag_retention_period_days: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetConvAiSettingsResponseModel: """ Update Convai settings for the workspace Parameters ---------- conversation_initiation_client_data_webhook : typing.Optional[ConversationInitiationClientDataWebhook] webhooks : typing.Optional[ConvAiWebhooks] rag_retention_period_days : typing.Optional[int] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiSettingsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.update_settings() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/settings", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "conversation_initiation_client_data_webhook": convert_and_respect_annotation_metadata( object_=conversation_initiation_client_data_webhook, annotation=ConversationInitiationClientDataWebhook, direction="write", ), "webhooks": convert_and_respect_annotation_metadata( object_=webhooks, annotation=ConvAiWebhooks, direction="write" ), "rag_retention_period_days": rag_retention_period_days, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiSettingsResponseModel, construct_type( type_=GetConvAiSettingsResponseModel, # 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 get_dashboard_settings( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetConvAiDashboardSettingsResponseModel: """ Retrieve Convai dashboard settings for the workspace Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiDashboardSettingsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_dashboard_settings() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/settings/dashboard", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiDashboardSettingsResponseModel, construct_type( type_=GetConvAiDashboardSettingsResponseModel, # 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 update_dashboard_settings( self, *, charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetConvAiDashboardSettingsResponseModel: """ Update Convai dashboard settings for the workspace Parameters ---------- charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiDashboardSettingsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.update_dashboard_settings() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/settings/dashboard", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "charts": convert_and_respect_annotation_metadata( object_=charts, annotation=typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiDashboardSettingsResponseModel, construct_type( type_=GetConvAiDashboardSettingsResponseModel, # 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 get_secrets( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetWorkspaceSecretsResponseModel: """ Get all workspace secrets for the user Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetWorkspaceSecretsResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_secrets() """ _response = self._client_wrapper.httpx_client.request( "v1/convai/secrets", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetWorkspaceSecretsResponseModel, construct_type( type_=GetWorkspaceSecretsResponseModel, # 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 create_secret( self, *, name: str, value: str, request_options: typing.Optional[RequestOptions] = None, ) -> PostWorkspaceSecretResponseModel: """ Create a new secret for the workspace Parameters ---------- name : str value : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- PostWorkspaceSecretResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_secret( name="name", value="value", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/secrets", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "name": name, "value": value, "type": "new", }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( PostWorkspaceSecretResponseModel, construct_type( type_=PostWorkspaceSecretResponseModel, # 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 delete_secret(self, secret_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ Delete a workspace secret if it's not in use Parameters ---------- secret_id : str 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.delete_secret( secret_id="secret_id", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/secrets/{jsonable_encoder(secret_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) 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) def get_batch_call( self, batch_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> BatchCallDetailedResponse: """ Get detailed information about a batch call including all recipients. Parameters ---------- batch_id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- BatchCallDetailedResponse Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_batch_call( batch_id="batch_id", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/batch-calling/{jsonable_encoder(batch_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( BatchCallDetailedResponse, construct_type( type_=BatchCallDetailedResponse, # 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) class AsyncConversationalAiClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper self.agents = AsyncAgentsClient(client_wrapper=self._client_wrapper) self.sip_trunk = AsyncSipTrunkClient(client_wrapper=self._client_wrapper) self.knowledge_base = AsyncKnowledgeBaseClient(client_wrapper=self._client_wrapper) async def get_signed_url( self, *, agent_id: str, request_options: typing.Optional[RequestOptions] = None ) -> ConversationSignedUrlResponseModel: """ Get a signed url to start a conversation with an agent with an agent that requires authorization Parameters ---------- agent_id : str The id of the agent you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationSignedUrlResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_signed_url( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/conversation/get-signed-url", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "agent_id": agent_id, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationSignedUrlResponseModel, construct_type( type_=ConversationSignedUrlResponseModel, # 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 twilio_outbound_call( self, *, agent_id: str, agent_phone_number_id: str, to_number: str, conversation_initiation_client_data: typing.Optional[ConversationInitiationClientDataRequestInput] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> TwilioOutboundCallResponse: """ Handle an outbound call via Twilio Parameters ---------- agent_id : str agent_phone_number_id : str to_number : str conversation_initiation_client_data : typing.Optional[ConversationInitiationClientDataRequestInput] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- TwilioOutboundCallResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.twilio_outbound_call( agent_id="agent_id", agent_phone_number_id="agent_phone_number_id", to_number="to_number", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/twilio/outbound-call", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "agent_id": agent_id, "agent_phone_number_id": agent_phone_number_id, "to_number": to_number, "conversation_initiation_client_data": convert_and_respect_annotation_metadata( object_=conversation_initiation_client_data, annotation=ConversationInitiationClientDataRequestInput, direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( TwilioOutboundCallResponse, construct_type( type_=TwilioOutboundCallResponse, # 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 create_agent( self, *, conversation_config: ConversationalConfig, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ 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. 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.create_agent( conversation_config=ConversationalConfig(), ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/agents/create", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "conversation_config": convert_and_respect_annotation_metadata( object_=conversation_config, annotation=ConversationalConfig, direction="write", ), "platform_settings": convert_and_respect_annotation_metadata( object_=platform_settings, annotation=AgentPlatformSettingsRequestModel, direction="write", ), "name": name, "tags": tags, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( CreateAgentResponseModel, construct_type( type_=CreateAgentResponseModel, # 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 get_agent( self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> GetAgentResponseModel: """ 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.get_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentResponseModel, construct_type( type_=GetAgentResponseModel, # 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 delete_agent(self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ 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.delete_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) 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) async def update_agent( self, agent_id: str, *, conversation_config: typing.Optional[typing.Optional[typing.Any]] = OMIT, platform_settings: typing.Optional[typing.Optional[typing.Any]] = OMIT, name: typing.Optional[str] = OMIT, tags: typing.Optional[typing.Sequence[str]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentResponseModel: """ Patches an Agent settings Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_config : typing.Optional[typing.Optional[typing.Any]] platform_settings : typing.Optional[typing.Optional[typing.Any]] 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.update_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "conversation_config": conversation_config, "platform_settings": platform_settings, "name": name, "tags": tags, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentResponseModel, construct_type( type_=GetAgentResponseModel, # 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 get_agent_widget( self, agent_id: str, *, conversation_signature: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentEmbedResponseModel: """ Retrieve the widget configuration for an agent Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. conversation_signature : typing.Optional[str] An expiring token that enables a websocket conversation to start. These can be generated for an agent using the /v1/convai/conversation/get-signed-url endpoint request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentEmbedResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_agent_widget( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/widget", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "conversation_signature": conversation_signature, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentEmbedResponseModel, construct_type( type_=GetAgentEmbedResponseModel, # 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 get_agent_link( self, agent_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> GetAgentLinkResponseModel: """ Get the current link used to share the agent with others Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetAgentLinkResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_agent_link( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/link", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentLinkResponseModel, construct_type( type_=GetAgentLinkResponseModel, # 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 post_agent_avatar( self, agent_id: str, *, avatar_file: core.File, request_options: typing.Optional[RequestOptions] = None, ) -> PostAgentAvatarResponseModel: """ Sets the avatar for an agent displayed in the widget Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. avatar_file : core.File See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- PostAgentAvatarResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.post_agent_avatar( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/avatar", base_url=self._client_wrapper.get_environment().base, method="POST", data={}, files={ "avatar_file": avatar_file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( PostAgentAvatarResponseModel, construct_type( type_=PostAgentAvatarResponseModel, # 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 get_agents( self, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetAgentsPageResponseModel: """ Returns a list of your agents and their metadata. Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. 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. 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.get_agents() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/agents", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetAgentsPageResponseModel, construct_type( type_=GetAgentsPageResponseModel, # 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 get_conversations( self, *, cursor: typing.Optional[str] = None, agent_id: typing.Optional[str] = None, call_successful: typing.Optional[EvaluationSuccessResult] = None, call_start_before_unix: typing.Optional[int] = None, call_start_after_unix: typing.Optional[int] = None, page_size: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetConversationsPageResponseModel: """ Get all conversations of agents that user owns. With option to restrict to a specific agent. Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. agent_id : typing.Optional[str] The id of the agent you're taking the action on. call_successful : typing.Optional[EvaluationSuccessResult] The result of the success evaluation call_start_before_unix : typing.Optional[int] Unix timestamp (in seconds) to filter conversations up to this start date. call_start_after_unix : typing.Optional[int] Unix timestamp (in seconds) to filter conversations after to this start date. page_size : typing.Optional[int] How many conversations to return at maximum. Can not exceed 100, defaults to 30. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConversationsPageResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_conversations() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/conversations", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "agent_id": agent_id, "call_successful": call_successful, "call_start_before_unix": call_start_before_unix, "call_start_after_unix": call_start_after_unix, "page_size": page_size, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConversationsPageResponseModel, construct_type( type_=GetConversationsPageResponseModel, # 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 get_conversation( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> GetConversationResponseModel: """ Get the details of a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConversationResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_conversation( conversation_id="123", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConversationResponseModel, construct_type( type_=GetConversationResponseModel, # 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 delete_conversation( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.delete_conversation( conversation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 get_conversation_audio( self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.AsyncIterator[bytes]: """ Get the audio recording of a particular conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Yields ------ typing.AsyncIterator[bytes] Successful Response """ async with self._client_wrapper.httpx_client.stream( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) as _response: try: if 200 <= _response.status_code < 300: _chunk_size = request_options.get("chunk_size", 1024) if request_options is not None else 1024 async for _chunk in _response.aiter_bytes(chunk_size=_chunk_size): yield _chunk return await _response.aread() 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 post_conversation_feedback( self, conversation_id: str, *, feedback: UserFeedbackScore, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Send the feedback for the given conversation Parameters ---------- conversation_id : str The id of the conversation you're taking the action on. feedback : UserFeedbackScore Either 'like' or 'dislike' to indicate the feedback for the conversation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.post_conversation_feedback( conversation_id="21m00Tcm4TlvDq8ikWAM", feedback="like", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/feedback", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "feedback": feedback, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 create_phone_number( self, *, request: ConversationalAiCreatePhoneNumberRequestBody, request_options: typing.Optional[RequestOptions] = None, ) -> CreatePhoneNumberResponseModel: """ Import Phone Number from provider configuration (Twilio or SIP trunk) Parameters ---------- request : ConversationalAiCreatePhoneNumberRequestBody request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreatePhoneNumberResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs from elevenlabs.conversational_ai import ( ConversationalAiCreatePhoneNumberRequestBody_Twilio, ) client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_phone_number( request=ConversationalAiCreatePhoneNumberRequestBody_Twilio( phone_number="phone_number", label="label", sid="sid", token="token", ), ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/phone-numbers/create", base_url=self._client_wrapper.get_environment().base, method="POST", json=convert_and_respect_annotation_metadata( object_=request, annotation=ConversationalAiCreatePhoneNumberRequestBody, direction="write", ), request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( CreatePhoneNumberResponseModel, construct_type( type_=CreatePhoneNumberResponseModel, # 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 get_phone_number( self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiGetPhoneNumberResponse: """ Retrieve Phone Number details by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiGetPhoneNumberResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiGetPhoneNumberResponse, construct_type( type_=ConversationalAiGetPhoneNumberResponse, # 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 delete_phone_number( self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete Phone Number by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.delete_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 update_phone_number( self, phone_number_id: str, *, agent_id: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiUpdatePhoneNumberResponse: """ Update Phone Number details by ID Parameters ---------- phone_number_id : str The id of an agent. This is returned on agent creation. agent_id : typing.Optional[str] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiUpdatePhoneNumberResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.update_phone_number( phone_number_id="TeaqRRdTcIfIu2i7BYfT", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "agent_id": agent_id, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiUpdatePhoneNumberResponse, construct_type( type_=ConversationalAiUpdatePhoneNumberResponse, # 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 get_phone_numbers( self, *, request_options: typing.Optional[RequestOptions] = None ) -> typing.List[ConversationalAiGetPhoneNumbersResponseItem]: """ Retrieve all Phone Numbers Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.List[ConversationalAiGetPhoneNumbersResponseItem] Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_phone_numbers() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/phone-numbers/", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.List[ConversationalAiGetPhoneNumbersResponseItem], construct_type( type_=typing.List[ConversationalAiGetPhoneNumbersResponseItem], # 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 get_knowledge_base_list( self, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, search: typing.Optional[str] = None, show_only_owned_documents: typing.Optional[bool] = None, types: typing.Optional[ typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]] ] = None, use_typesense: typing.Optional[bool] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetKnowledgeBaseListResponseModel: """ Get a list of available knowledge base documents Parameters ---------- cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. page_size : typing.Optional[int] How many documents to return at maximum. Can not exceed 100, defaults to 30. search : typing.Optional[str] If specified, the endpoint returns only such knowledge base documents whose names start with this string. show_only_owned_documents : typing.Optional[bool] If set to true, the endpoint will return only documents owned by you (and not shared from somebody else). types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]] If present, the endpoint will return only documents of the given types. use_typesense : typing.Optional[bool] If set to true, the endpoint will use typesense DB to search for the documents). request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetKnowledgeBaseListResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_knowledge_base_list() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, "show_only_owned_documents": show_only_owned_documents, "types": types, "use_typesense": use_typesense, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseListResponseModel, construct_type( type_=GetKnowledgeBaseListResponseModel, # 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 add_to_knowledge_base( self, *, name: typing.Optional[str] = OMIT, url: typing.Optional[str] = OMIT, file: typing.Optional[core.File] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Upload a file or webpage URL to create a knowledge base document.
After creating the document, update the agent's knowledge base by calling [Update agent](/docs/conversational-ai/api-reference/agents/update-agent). Parameters ---------- name : typing.Optional[str] A custom, human-readable name for the document. url : typing.Optional[str] URL to a page of documentation that the agent will have access to in order to interact with users. file : typing.Optional[core.File] See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.add_to_knowledge_base() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base", base_url=self._client_wrapper.get_environment().base, method="POST", data={ "name": name, "url": url, }, files={ "file": file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_url_document( self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document generated by scraping the given webpage. Parameters ---------- url : str URL to a page of documentation that the agent will have access to in order to interact with users. name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_knowledge_base_url_document( url="url", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/url", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "url": url, "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_file_document( self, *, file: core.File, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document generated form the uploaded file. Parameters ---------- file : core.File See core.File for more documentation name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_knowledge_base_file_document() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/file", base_url=self._client_wrapper.get_environment().base, method="POST", data={ "name": name, }, files={ "file": file, }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 create_knowledge_base_text_document( self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AddKnowledgeBaseResponseModel: """ Create a knowledge base document containing the provided text. Parameters ---------- text : str Text content to be added to the knowledge base. name : typing.Optional[str] A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddKnowledgeBaseResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_knowledge_base_text_document( text="text", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/knowledge-base/text", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "text": text, "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddKnowledgeBaseResponseModel, construct_type( type_=AddKnowledgeBaseResponseModel, # 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 get_knowledge_base_document_by_id( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiGetKnowledgeBaseDocumentByIdResponse: """ Get details about a specific documentation making up the agent's knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiGetKnowledgeBaseDocumentByIdResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_knowledge_base_document_by_id( documentation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiGetKnowledgeBaseDocumentByIdResponse, construct_type( type_=ConversationalAiGetKnowledgeBaseDocumentByIdResponse, # 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 delete_knowledge_base_document( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Optional[typing.Any]: """ Delete a document from the knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Optional[typing.Any] Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.delete_knowledge_base_document( documentation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.Optional[typing.Any], construct_type( type_=typing.Optional[typing.Any], # 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 update_knowledge_base_document( self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None, ) -> ConversationalAiUpdateKnowledgeBaseDocumentResponse: """ Update the name of a document Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. name : str A custom, human-readable name for the document. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ConversationalAiUpdateKnowledgeBaseDocumentResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.update_knowledge_base_document( documentation_id="21m00Tcm4TlvDq8ikWAM", name="name", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "name": name, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( ConversationalAiUpdateKnowledgeBaseDocumentResponse, construct_type( type_=ConversationalAiUpdateKnowledgeBaseDocumentResponse, # 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 get_dependent_agents( self, documentation_id: str, *, cursor: typing.Optional[str] = None, page_size: typing.Optional[int] = None, request_options: typing.Optional[RequestOptions] = None, ) -> GetKnowledgeBaseDependentAgentsResponseModel: """ Get a list of agents depending on this knowledge base document Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. cursor : typing.Optional[str] Used for fetching next page. Cursor is returned in the response. page_size : typing.Optional[int] How many documents to return at maximum. Can not exceed 100, defaults to 30. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetKnowledgeBaseDependentAgentsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_dependent_agents( documentation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/dependent-agents", base_url=self._client_wrapper.get_environment().base, method="GET", params={ "cursor": cursor, "page_size": page_size, }, request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseDependentAgentsResponseModel, construct_type( type_=GetKnowledgeBaseDependentAgentsResponseModel, # 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 get_knowledge_base_document_content( self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> None: """ Get the entire content of a document from the knowledge base Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. 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.get_knowledge_base_document_content( documentation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/content", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) 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) async def get_knowledge_base_document_part_by_id( self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None, ) -> KnowledgeBaseDocumentChunkResponseModel: """ Get details about a specific documentation part used by RAG. Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. chunk_id : str The id of a document RAG chunk from the knowledge base. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- KnowledgeBaseDocumentChunkResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_knowledge_base_document_part_by_id( documentation_id="21m00Tcm4TlvDq8ikWAM", chunk_id="chunk_id", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/chunk/{jsonable_encoder(chunk_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( KnowledgeBaseDocumentChunkResponseModel, construct_type( type_=KnowledgeBaseDocumentChunkResponseModel, # 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 get_settings( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetConvAiSettingsResponseModel: """ Retrieve Convai settings for the workspace Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiSettingsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_settings() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/settings", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiSettingsResponseModel, construct_type( type_=GetConvAiSettingsResponseModel, # 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 update_settings( self, *, conversation_initiation_client_data_webhook: typing.Optional[ConversationInitiationClientDataWebhook] = OMIT, webhooks: typing.Optional[ConvAiWebhooks] = OMIT, rag_retention_period_days: typing.Optional[int] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetConvAiSettingsResponseModel: """ Update Convai settings for the workspace Parameters ---------- conversation_initiation_client_data_webhook : typing.Optional[ConversationInitiationClientDataWebhook] webhooks : typing.Optional[ConvAiWebhooks] rag_retention_period_days : typing.Optional[int] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiSettingsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.update_settings() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/settings", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "conversation_initiation_client_data_webhook": convert_and_respect_annotation_metadata( object_=conversation_initiation_client_data_webhook, annotation=ConversationInitiationClientDataWebhook, direction="write", ), "webhooks": convert_and_respect_annotation_metadata( object_=webhooks, annotation=ConvAiWebhooks, direction="write" ), "rag_retention_period_days": rag_retention_period_days, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiSettingsResponseModel, construct_type( type_=GetConvAiSettingsResponseModel, # 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 get_dashboard_settings( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetConvAiDashboardSettingsResponseModel: """ Retrieve Convai dashboard settings for the workspace Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiDashboardSettingsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_dashboard_settings() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/settings/dashboard", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiDashboardSettingsResponseModel, construct_type( type_=GetConvAiDashboardSettingsResponseModel, # 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 update_dashboard_settings( self, *, charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> GetConvAiDashboardSettingsResponseModel: """ Update Convai dashboard settings for the workspace Parameters ---------- charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetConvAiDashboardSettingsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.update_dashboard_settings() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/settings/dashboard", base_url=self._client_wrapper.get_environment().base, method="PATCH", json={ "charts": convert_and_respect_annotation_metadata( object_=charts, annotation=typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem], direction="write", ), }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetConvAiDashboardSettingsResponseModel, construct_type( type_=GetConvAiDashboardSettingsResponseModel, # 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 get_secrets( self, *, request_options: typing.Optional[RequestOptions] = None ) -> GetWorkspaceSecretsResponseModel: """ Get all workspace secrets for the user Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- GetWorkspaceSecretsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_secrets() asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/secrets", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetWorkspaceSecretsResponseModel, construct_type( type_=GetWorkspaceSecretsResponseModel, # 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 create_secret( self, *, name: str, value: str, request_options: typing.Optional[RequestOptions] = None, ) -> PostWorkspaceSecretResponseModel: """ Create a new secret for the workspace Parameters ---------- name : str value : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- PostWorkspaceSecretResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_secret( name="name", value="value", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/secrets", base_url=self._client_wrapper.get_environment().base, method="POST", json={ "name": name, "value": value, "type": "new", }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( PostWorkspaceSecretResponseModel, construct_type( type_=PostWorkspaceSecretResponseModel, # 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 delete_secret(self, secret_id: str, *, request_options: typing.Optional[RequestOptions] = None) -> None: """ Delete a workspace secret if it's not in use Parameters ---------- secret_id : str 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.delete_secret( secret_id="secret_id", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/secrets/{jsonable_encoder(secret_id)}", base_url=self._client_wrapper.get_environment().base, method="DELETE", request_options=request_options, ) 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) async def get_batch_call( self, batch_id: str, *, request_options: typing.Optional[RequestOptions] = None ) -> BatchCallDetailedResponse: """ Get detailed information about a batch call including all recipients. Parameters ---------- batch_id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- BatchCallDetailedResponse Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.get_batch_call( batch_id="batch_id", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/batch-calling/{jsonable_encoder(batch_id)}", base_url=self._client_wrapper.get_environment().base, method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( BatchCallDetailedResponse, construct_type( type_=BatchCallDetailedResponse, # 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)