# This file was auto-generated by Fern from our API Definition. import typing from ..core.client_wrapper import SyncClientWrapper 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.conversational_config_api_model import ConversationalConfigApiModel from ..types.agent_platform_settings_request_model import AgentPlatformSettingsRequestModel from ..types.create_agent_response_model import CreateAgentResponseModel from ..core.serialization import convert_and_respect_annotation_metadata 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.add_agent_secret_response_model import AddAgentSecretResponseModel 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.create_phone_number_response_model import CreatePhoneNumberResponseModel from ..types.get_phone_number_response_model import GetPhoneNumberResponseModel from ..types.get_knowledge_base_list_response_model import GetKnowledgeBaseListResponseModel from ..types.add_knowledge_base_response_model import AddKnowledgeBaseResponseModel from ..types.embedding_model_enum import EmbeddingModelEnum from ..types.rag_index_response_model import RagIndexResponseModel from ..types.get_knowledge_base_response_model import GetKnowledgeBaseResponseModel from ..types.get_knowledge_base_dependent_agents_response_model import GetKnowledgeBaseDependentAgentsResponseModel 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_workspace_secrets_response_model import GetWorkspaceSecretsResponseModel from ..types.post_workspace_secret_response_model import PostWorkspaceSecretResponseModel from ..core.client_wrapper import AsyncClientWrapper # 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 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", 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 create_agent( self, *, conversation_config: ConversationalConfigApiModel, use_tool_ids: typing.Optional[bool] = None, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfigApiModel Conversation configuration for an agent use_tool_ids : typing.Optional[bool] Use tool ids instead of tools specs from request payload. 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- from elevenlabs import ConversationalConfigApiModel, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_agent( conversation_config=ConversationalConfigApiModel(), ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/agents/create", method="POST", params={ "use_tool_ids": use_tool_ids, }, json={ "conversation_config": convert_and_respect_annotation_metadata( object_=conversation_config, annotation=ConversationalConfigApiModel, direction="write" ), "platform_settings": convert_and_respect_annotation_metadata( object_=platform_settings, annotation=AgentPlatformSettingsRequestModel, direction="write" ), "name": name, }, 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)}", 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 ) -> typing.Optional[typing.Any]: """ 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 ------- typing.Optional[typing.Any] Successful Response 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)}", 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_agent( self, agent_id: str, *, use_tool_ids: typing.Optional[bool] = None, conversation_config: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = OMIT, platform_settings: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = OMIT, name: typing.Optional[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. use_tool_ids : typing.Optional[bool] Use tool ids instead of tools specs from request payload. conversation_config : typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] Conversation configuration for an agent platform_settings : typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] 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 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)}", method="PATCH", params={ "use_tool_ids": use_tool_ids, }, json={ "conversation_config": conversation_config, "platform_settings": platform_settings, "name": name, }, 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 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", 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", 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", 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 add_agent_secret( self, agent_id: str, *, name: str, secret_value: str, request_options: typing.Optional[RequestOptions] = None ) -> AddAgentSecretResponseModel: """ Uploads a file or reference a webpage for the agent to use as part of it's knowledge base Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : str A name to help identify a particular agent secret secret_value : str A value to be encrypted and used by the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddAgentSecretResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.add_agent_secret( agent_id="21m00Tcm4TlvDq8ikWAM", name="MY API KEY", secret_value="sk_api_12354abc", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/add-secret", method="POST", json={ "name": name, "secret_value": secret_value, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddAgentSecretResponseModel, construct_type( type_=AddAgentSecretResponseModel, # 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 page 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", 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, 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 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( agent_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/conversations", method="GET", params={ "cursor": cursor, "agent_id": agent_id, "call_successful": call_successful, "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="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", 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)}", 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 ) -> None: """ 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. Returns ------- None Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.get_conversation_audio( conversation_id="21m00Tcm4TlvDq8ikWAM", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio", 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 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", 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, *, phone_number: str, label: str, sid: str, token: str, request_options: typing.Optional[RequestOptions] = None, ) -> CreatePhoneNumberResponseModel: """ Import Phone Number from Twilio configuration Parameters ---------- phone_number : str Phone number label : str Label for the phone number sid : str Twilio Account SID token : str Twilio Auth Token request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreatePhoneNumberResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.create_phone_number( phone_number="phone_number", label="label", sid="sid", token="token", ) """ _response = self._client_wrapper.httpx_client.request( "v1/convai/phone-numbers/create", method="POST", json={ "phone_number": phone_number, "label": label, "sid": sid, "token": token, "provider": "twilio", }, headers={ "content-type": "application/json", }, 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 ) -> GetPhoneNumberResponseModel: """ 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 ------- GetPhoneNumberResponseModel 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)}", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetPhoneNumberResponseModel, construct_type( type_=GetPhoneNumberResponseModel, # 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)}", 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, ) -> GetPhoneNumberResponseModel: """ 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 ------- GetPhoneNumberResponseModel 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)}", 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( GetPhoneNumberResponseModel, construct_type( type_=GetPhoneNumberResponseModel, # 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[GetPhoneNumberResponseModel]: """ Retrieve all Phone Numbers Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.List[GetPhoneNumberResponseModel] 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/", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.List[GetPhoneNumberResponseModel], construct_type( type_=typing.List[GetPhoneNumberResponseModel], # 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, 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). 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", method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, "show_only_owned_documents": show_only_owned_documents, "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: """ Uploads a file or reference a webpage to use as part of the shared knowledge base 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", 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 rag_index_status( self, documentation_id: str, *, model: EmbeddingModelEnum, force_reindex: typing.Optional[bool] = None, request_options: typing.Optional[RequestOptions] = None, ) -> RagIndexResponseModel: """ In case the document is not RAG indexed, it triggers rag indexing task, otherwise it just returns the current status. Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. model : EmbeddingModelEnum force_reindex : typing.Optional[bool] In case the document is indexed and for some reason you want to reindex it, set this param as true. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- RagIndexResponseModel Successful Response Examples -------- from elevenlabs import ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.conversational_ai.rag_index_status( documentation_id="21m00Tcm4TlvDq8ikWAM", model="e5_mistral_7b_instruct", ) """ _response = self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/rag-index", method="POST", params={ "force_reindex": force_reindex, }, json={ "model": model, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( RagIndexResponseModel, construct_type( type_=RagIndexResponseModel, # 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 ) -> GetKnowledgeBaseResponseModel: """ 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 ------- GetKnowledgeBaseResponseModel 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)}", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseResponseModel, construct_type( type_=GetKnowledgeBaseResponseModel, # 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)}", 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_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", 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_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", 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, 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] 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", 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" ), }, 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_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", 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", 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)}", 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) class AsyncConversationalAiClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = 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", 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 create_agent( self, *, conversation_config: ConversationalConfigApiModel, use_tool_ids: typing.Optional[bool] = None, platform_settings: typing.Optional[AgentPlatformSettingsRequestModel] = OMIT, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> CreateAgentResponseModel: """ Create an agent from a config object Parameters ---------- conversation_config : ConversationalConfigApiModel Conversation configuration for an agent use_tool_ids : typing.Optional[bool] Use tool ids instead of tools specs from request payload. 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 request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreateAgentResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, ConversationalConfigApiModel client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.create_agent( conversation_config=ConversationalConfigApiModel(), ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/agents/create", method="POST", params={ "use_tool_ids": use_tool_ids, }, json={ "conversation_config": convert_and_respect_annotation_metadata( object_=conversation_config, annotation=ConversationalConfigApiModel, direction="write" ), "platform_settings": convert_and_respect_annotation_metadata( object_=platform_settings, annotation=AgentPlatformSettingsRequestModel, direction="write" ), "name": name, }, 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)}", 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 ) -> typing.Optional[typing.Any]: """ 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 ------- 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_agent( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}", 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_agent( self, agent_id: str, *, use_tool_ids: typing.Optional[bool] = None, conversation_config: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = OMIT, platform_settings: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = OMIT, name: typing.Optional[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. use_tool_ids : typing.Optional[bool] Use tool ids instead of tools specs from request payload. conversation_config : typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] Conversation configuration for an agent platform_settings : typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] 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 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)}", method="PATCH", params={ "use_tool_ids": use_tool_ids, }, json={ "conversation_config": conversation_config, "platform_settings": platform_settings, "name": name, }, 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 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", 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", 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", 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 add_agent_secret( self, agent_id: str, *, name: str, secret_value: str, request_options: typing.Optional[RequestOptions] = None ) -> AddAgentSecretResponseModel: """ Uploads a file or reference a webpage for the agent to use as part of it's knowledge base Parameters ---------- agent_id : str The id of an agent. This is returned on agent creation. name : str A name to help identify a particular agent secret secret_value : str A value to be encrypted and used by the agent request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AddAgentSecretResponseModel 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_agent_secret( agent_id="21m00Tcm4TlvDq8ikWAM", name="MY API KEY", secret_value="sk_api_12354abc", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/agents/{jsonable_encoder(agent_id)}/add-secret", method="POST", json={ "name": name, "secret_value": secret_value, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( AddAgentSecretResponseModel, construct_type( type_=AddAgentSecretResponseModel, # 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 page 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", 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, 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 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( agent_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( "v1/convai/conversations", method="GET", params={ "cursor": cursor, "agent_id": agent_id, "call_successful": call_successful, "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="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}", 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)}", 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 ) -> None: """ 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. 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_conversation_audio( conversation_id="21m00Tcm4TlvDq8ikWAM", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio", 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 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", 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, *, phone_number: str, label: str, sid: str, token: str, request_options: typing.Optional[RequestOptions] = None, ) -> CreatePhoneNumberResponseModel: """ Import Phone Number from Twilio configuration Parameters ---------- phone_number : str Phone number label : str Label for the phone number sid : str Twilio Account SID token : str Twilio Auth Token request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- CreatePhoneNumberResponseModel 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_phone_number( 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", method="POST", json={ "phone_number": phone_number, "label": label, "sid": sid, "token": token, "provider": "twilio", }, headers={ "content-type": "application/json", }, 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 ) -> GetPhoneNumberResponseModel: """ 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 ------- GetPhoneNumberResponseModel 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)}", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetPhoneNumberResponseModel, construct_type( type_=GetPhoneNumberResponseModel, # 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)}", 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, ) -> GetPhoneNumberResponseModel: """ 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 ------- GetPhoneNumberResponseModel 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)}", 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( GetPhoneNumberResponseModel, construct_type( type_=GetPhoneNumberResponseModel, # 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[GetPhoneNumberResponseModel]: """ Retrieve all Phone Numbers Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.List[GetPhoneNumberResponseModel] 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/", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( typing.List[GetPhoneNumberResponseModel], construct_type( type_=typing.List[GetPhoneNumberResponseModel], # 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, 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). 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", method="GET", params={ "cursor": cursor, "page_size": page_size, "search": search, "show_only_owned_documents": show_only_owned_documents, "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: """ Uploads a file or reference a webpage to use as part of the shared knowledge base 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", 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 rag_index_status( self, documentation_id: str, *, model: EmbeddingModelEnum, force_reindex: typing.Optional[bool] = None, request_options: typing.Optional[RequestOptions] = None, ) -> RagIndexResponseModel: """ In case the document is not RAG indexed, it triggers rag indexing task, otherwise it just returns the current status. Parameters ---------- documentation_id : str The id of a document from the knowledge base. This is returned on document addition. model : EmbeddingModelEnum force_reindex : typing.Optional[bool] In case the document is indexed and for some reason you want to reindex it, set this param as true. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- RagIndexResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.conversational_ai.rag_index_status( documentation_id="21m00Tcm4TlvDq8ikWAM", model="e5_mistral_7b_instruct", ) asyncio.run(main()) """ _response = await self._client_wrapper.httpx_client.request( f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/rag-index", method="POST", params={ "force_reindex": force_reindex, }, json={ "model": model, }, headers={ "content-type": "application/json", }, request_options=request_options, omit=OMIT, ) try: if 200 <= _response.status_code < 300: return typing.cast( RagIndexResponseModel, construct_type( type_=RagIndexResponseModel, # 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 ) -> GetKnowledgeBaseResponseModel: """ 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 ------- GetKnowledgeBaseResponseModel 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)}", method="GET", request_options=request_options, ) try: if 200 <= _response.status_code < 300: return typing.cast( GetKnowledgeBaseResponseModel, construct_type( type_=GetKnowledgeBaseResponseModel, # 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)}", 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_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", 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_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", 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, 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] 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", 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" ), }, 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_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", 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", 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)}", 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)