code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
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.conversations.get_signed_url(
agent_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.get_signed_url(agent_id=agent_id, request_options=request_options)
return _response.data |
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.conversations.get_signed_url(
agent_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| get_signed_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/client.py | MIT |
async def list(
self,
*,
cursor: typing.Optional[str] = None,
agent_id: typing.Optional[str] = None,
call_successful: typing.Optional[EvaluationSuccessResult] = None,
call_start_before_unix: typing.Optional[int] = None,
call_start_after_unix: typing.Optional[int] = None,
page_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> GetConversationsPageResponseModel:
"""
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConversationsPageResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.conversations.list()
asyncio.run(main())
"""
_response = await self._raw_client.list(
cursor=cursor,
agent_id=agent_id,
call_successful=call_successful,
call_start_before_unix=call_start_before_unix,
call_start_after_unix=call_start_after_unix,
page_size=page_size,
request_options=request_options,
)
return _response.data |
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConversationsPageResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.conversations.list()
asyncio.run(main())
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/client.py | MIT |
async def get(
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.conversations.get(
conversation_id="123",
)
asyncio.run(main())
"""
_response = await self._raw_client.get(conversation_id, request_options=request_options)
return _response.data |
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.conversations.get(
conversation_id="123",
)
asyncio.run(main())
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/client.py | MIT |
async def delete(
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.conversations.delete(
conversation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.delete(conversation_id, request_options=request_options)
return _response.data |
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.conversations.delete(
conversation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/client.py | MIT |
def get_signed_url(
self, *, agent_id: str, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[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
-------
HttpResponse[ConversationSignedUrlResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/conversation/get-signed-url",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"agent_id": agent_id,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ConversationSignedUrlResponseModel,
construct_type(
type_=ConversationSignedUrlResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[ConversationSignedUrlResponseModel]
Successful Response
| get_signed_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
def list(
self,
*,
cursor: typing.Optional[str] = None,
agent_id: typing.Optional[str] = None,
call_successful: typing.Optional[EvaluationSuccessResult] = None,
call_start_before_unix: typing.Optional[int] = None,
call_start_after_unix: typing.Optional[int] = None,
page_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[GetConversationsPageResponseModel]:
"""
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConversationsPageResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/conversations",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"agent_id": agent_id,
"call_successful": call_successful,
"call_start_before_unix": call_start_before_unix,
"call_start_after_unix": call_start_after_unix,
"page_size": page_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConversationsPageResponseModel,
construct_type(
type_=GetConversationsPageResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConversationsPageResponseModel]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[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
-------
HttpResponse[GetConversationResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConversationResponseModel,
construct_type(
type_=GetConversationResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[GetConversationResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
def delete(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
async def get_signed_url(
self, *, agent_id: str, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[ConversationSignedUrlResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/conversation/get-signed-url",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"agent_id": agent_id,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
ConversationSignedUrlResponseModel,
construct_type(
type_=ConversationSignedUrlResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[ConversationSignedUrlResponseModel]
Successful Response
| get_signed_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
async def list(
self,
*,
cursor: typing.Optional[str] = None,
agent_id: typing.Optional[str] = None,
call_successful: typing.Optional[EvaluationSuccessResult] = None,
call_start_before_unix: typing.Optional[int] = None,
call_start_after_unix: typing.Optional[int] = None,
page_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[GetConversationsPageResponseModel]:
"""
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConversationsPageResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/conversations",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"agent_id": agent_id,
"call_successful": call_successful,
"call_start_before_unix": call_start_before_unix,
"call_start_after_unix": call_start_after_unix,
"page_size": page_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConversationsPageResponseModel,
construct_type(
type_=GetConversationsPageResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get all conversations of agents that user owns. With option to restrict to a specific agent.
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
agent_id : typing.Optional[str]
The id of the agent you're taking the action on.
call_successful : typing.Optional[EvaluationSuccessResult]
The result of the success evaluation
call_start_before_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations up to this start date.
call_start_after_unix : typing.Optional[int]
Unix timestamp (in seconds) to filter conversations after to this start date.
page_size : typing.Optional[int]
How many conversations to return at maximum. Can not exceed 100, defaults to 30.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConversationsPageResponseModel]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
async def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[GetConversationResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConversationResponseModel,
construct_type(
type_=GetConversationResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[GetConversationResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
async def delete(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/raw_client.py | MIT |
def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.Iterator[bytes]:
"""
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.Iterator[bytes]
Successful Response
"""
with self._raw_client.get(conversation_id, request_options=request_options) as r:
yield from r.data |
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.Iterator[bytes]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/audio/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/audio/client.py | MIT |
async def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.AsyncIterator[bytes]:
"""
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.AsyncIterator[bytes]
Successful Response
"""
async with self._raw_client.get(conversation_id, request_options=request_options) as r:
async for _chunk in r.data:
yield _chunk |
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.AsyncIterator[bytes]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/audio/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/audio/client.py | MIT |
def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.Iterator[HttpResponse[typing.Iterator[bytes]]]:
"""
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.Iterator[HttpResponse[typing.Iterator[bytes]]]
Successful Response
"""
with self._client_wrapper.httpx_client.stream(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
) as _response:
def _stream() -> HttpResponse[typing.Iterator[bytes]]:
try:
if 200 <= _response.status_code < 300:
_chunk_size = request_options.get("chunk_size", 1024) if request_options is not None else 1024
return HttpResponse(
response=_response, data=(_chunk for _chunk in _response.iter_bytes(chunk_size=_chunk_size))
)
_response.read()
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text
)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
yield _stream() |
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.Iterator[HttpResponse[typing.Iterator[bytes]]]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/audio/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/audio/raw_client.py | MIT |
async def get(
self, conversation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.AsyncIterator[AsyncHttpResponse[typing.AsyncIterator[bytes]]]:
"""
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.AsyncIterator[AsyncHttpResponse[typing.AsyncIterator[bytes]]]
Successful Response
"""
async with self._client_wrapper.httpx_client.stream(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/audio",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
) as _response:
async def _stream() -> AsyncHttpResponse[typing.AsyncIterator[bytes]]:
try:
if 200 <= _response.status_code < 300:
_chunk_size = request_options.get("chunk_size", 1024) if request_options is not None else 1024
return AsyncHttpResponse(
response=_response,
data=(_chunk async for _chunk in _response.aiter_bytes(chunk_size=_chunk_size)),
)
await _response.aread()
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text
)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json)
yield await _stream() |
Get the audio recording of a particular conversation
Parameters
----------
conversation_id : str
The id of the conversation you're taking the action on.
request_options : typing.Optional[RequestOptions]
Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response.
Returns
-------
typing.AsyncIterator[AsyncHttpResponse[typing.AsyncIterator[bytes]]]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/audio/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/audio/raw_client.py | MIT |
def create(
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.conversations.feedback.create(
conversation_id="21m00Tcm4TlvDq8ikWAM",
feedback="like",
)
"""
_response = self._raw_client.create(conversation_id, feedback=feedback, request_options=request_options)
return _response.data |
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.conversations.feedback.create(
conversation_id="21m00Tcm4TlvDq8ikWAM",
feedback="like",
)
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/feedback/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/feedback/client.py | MIT |
async def create(
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.conversations.feedback.create(
conversation_id="21m00Tcm4TlvDq8ikWAM",
feedback="like",
)
asyncio.run(main())
"""
_response = await self._raw_client.create(conversation_id, feedback=feedback, request_options=request_options)
return _response.data |
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.conversations.feedback.create(
conversation_id="21m00Tcm4TlvDq8ikWAM",
feedback="like",
)
asyncio.run(main())
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/feedback/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/feedback/client.py | MIT |
def create(
self,
conversation_id: str,
*,
feedback: UserFeedbackScore,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/feedback",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"feedback": feedback,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/feedback/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/feedback/raw_client.py | MIT |
async def create(
self,
conversation_id: str,
*,
feedback: UserFeedbackScore,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/conversations/{jsonable_encoder(conversation_id)}/feedback",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"feedback": feedback,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/conversations/feedback/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/conversations/feedback/raw_client.py | MIT |
def get(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> GetConvAiDashboardSettingsResponseModel:
"""
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.dashboard.settings.get()
"""
_response = self._raw_client.get(request_options=request_options)
return _response.data |
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.dashboard.settings.get()
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/client.py | MIT |
def update(
self,
*,
charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> GetConvAiDashboardSettingsResponseModel:
"""
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.dashboard.settings.update()
"""
_response = self._raw_client.update(charts=charts, request_options=request_options)
return _response.data |
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.dashboard.settings.update()
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/client.py | MIT |
async def get(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> GetConvAiDashboardSettingsResponseModel:
"""
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.dashboard.settings.get()
asyncio.run(main())
"""
_response = await self._raw_client.get(request_options=request_options)
return _response.data |
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.dashboard.settings.get()
asyncio.run(main())
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/client.py | MIT |
async def update(
self,
*,
charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> GetConvAiDashboardSettingsResponseModel:
"""
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.dashboard.settings.update()
asyncio.run(main())
"""
_response = await self._raw_client.update(charts=charts, request_options=request_options)
return _response.data |
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetConvAiDashboardSettingsResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.dashboard.settings.update()
asyncio.run(main())
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/client.py | MIT |
def get(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[GetConvAiDashboardSettingsResponseModel]:
"""
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/settings/dashboard",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConvAiDashboardSettingsResponseModel,
construct_type(
type_=GetConvAiDashboardSettingsResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | MIT |
def update(
self,
*,
charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[GetConvAiDashboardSettingsResponseModel]:
"""
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/settings/dashboard",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"charts": convert_and_respect_annotation_metadata(
object_=charts,
annotation=typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem],
direction="write",
),
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConvAiDashboardSettingsResponseModel,
construct_type(
type_=GetConvAiDashboardSettingsResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | MIT |
async def get(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]:
"""
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/settings/dashboard",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConvAiDashboardSettingsResponseModel,
construct_type(
type_=GetConvAiDashboardSettingsResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Retrieve Convai dashboard settings for the workspace
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | MIT |
async def update(
self,
*,
charts: typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]:
"""
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/settings/dashboard",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"charts": convert_and_respect_annotation_metadata(
object_=charts,
annotation=typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem],
direction="write",
),
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetConvAiDashboardSettingsResponseModel,
construct_type(
type_=GetConvAiDashboardSettingsResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Update Convai dashboard settings for the workspace
Parameters
----------
charts : typing.Optional[typing.Sequence[PatchConvAiDashboardSettingsRequestChartsItem]]
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetConvAiDashboardSettingsResponseModel]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/dashboard/settings/raw_client.py | MIT |
def list(
self,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
search: typing.Optional[str] = None,
show_only_owned_documents: typing.Optional[bool] = None,
types: typing.Optional[
typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]
] = None,
use_typesense: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> GetKnowledgeBaseListResponseModel:
"""
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetKnowledgeBaseListResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.list()
"""
_response = self._raw_client.list(
cursor=cursor,
page_size=page_size,
search=search,
show_only_owned_documents=show_only_owned_documents,
types=types,
use_typesense=use_typesense,
request_options=request_options,
)
return _response.data |
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetKnowledgeBaseListResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.list()
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/client.py | MIT |
async def list(
self,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
search: typing.Optional[str] = None,
show_only_owned_documents: typing.Optional[bool] = None,
types: typing.Optional[
typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]
] = None,
use_typesense: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> GetKnowledgeBaseListResponseModel:
"""
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetKnowledgeBaseListResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.list()
asyncio.run(main())
"""
_response = await self._raw_client.list(
cursor=cursor,
page_size=page_size,
search=search,
show_only_owned_documents=show_only_owned_documents,
types=types,
use_typesense=use_typesense,
request_options=request_options,
)
return _response.data |
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
GetKnowledgeBaseListResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.list()
asyncio.run(main())
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/client.py | MIT |
def list(
self,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
search: typing.Optional[str] = None,
show_only_owned_documents: typing.Optional[bool] = None,
types: typing.Optional[
typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]
] = None,
use_typesense: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[GetKnowledgeBaseListResponseModel]:
"""
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetKnowledgeBaseListResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"page_size": page_size,
"search": search,
"show_only_owned_documents": show_only_owned_documents,
"types": types,
"use_typesense": use_typesense,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetKnowledgeBaseListResponseModel,
construct_type(
type_=GetKnowledgeBaseListResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetKnowledgeBaseListResponseModel]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/raw_client.py | MIT |
async def list(
self,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
search: typing.Optional[str] = None,
show_only_owned_documents: typing.Optional[bool] = None,
types: typing.Optional[
typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]
] = None,
use_typesense: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[GetKnowledgeBaseListResponseModel]:
"""
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetKnowledgeBaseListResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"page_size": page_size,
"search": search,
"show_only_owned_documents": show_only_owned_documents,
"types": types,
"use_typesense": use_typesense,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetKnowledgeBaseListResponseModel,
construct_type(
type_=GetKnowledgeBaseListResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get a list of available knowledge base documents
Parameters
----------
cursor : typing.Optional[str]
Used for fetching next page. Cursor is returned in the response.
page_size : typing.Optional[int]
How many documents to return at maximum. Can not exceed 100, defaults to 30.
search : typing.Optional[str]
If specified, the endpoint returns only such knowledge base documents whose names start with this string.
show_only_owned_documents : typing.Optional[bool]
If set to true, the endpoint will return only documents owned by you (and not shared from somebody else).
types : typing.Optional[typing.Union[KnowledgeBaseDocumentType, typing.Sequence[KnowledgeBaseDocumentType]]]
If present, the endpoint will return only documents of the given types.
use_typesense : typing.Optional[bool]
If set to true, the endpoint will use typesense DB to search for the documents).
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[GetKnowledgeBaseListResponseModel]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/raw_client.py | MIT |
def compute_rag_index(
self,
documentation_id: str,
*,
model: EmbeddingModelEnum,
request_options: typing.Optional[RequestOptions] = None,
) -> RagDocumentIndexResponseModel:
"""
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
RagDocumentIndexResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.document.compute_rag_index(
documentation_id="21m00Tcm4TlvDq8ikWAM",
model="e5_mistral_7b_instruct",
)
"""
_response = self._raw_client.compute_rag_index(documentation_id, model=model, request_options=request_options)
return _response.data |
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
RagDocumentIndexResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.document.compute_rag_index(
documentation_id="21m00Tcm4TlvDq8ikWAM",
model="e5_mistral_7b_instruct",
)
| compute_rag_index | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/document/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/document/client.py | MIT |
async def compute_rag_index(
self,
documentation_id: str,
*,
model: EmbeddingModelEnum,
request_options: typing.Optional[RequestOptions] = None,
) -> RagDocumentIndexResponseModel:
"""
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
RagDocumentIndexResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.document.compute_rag_index(
documentation_id="21m00Tcm4TlvDq8ikWAM",
model="e5_mistral_7b_instruct",
)
asyncio.run(main())
"""
_response = await self._raw_client.compute_rag_index(
documentation_id, model=model, request_options=request_options
)
return _response.data |
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
RagDocumentIndexResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.document.compute_rag_index(
documentation_id="21m00Tcm4TlvDq8ikWAM",
model="e5_mistral_7b_instruct",
)
asyncio.run(main())
| compute_rag_index | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/document/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/document/client.py | MIT |
def compute_rag_index(
self,
documentation_id: str,
*,
model: EmbeddingModelEnum,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[RagDocumentIndexResponseModel]:
"""
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[RagDocumentIndexResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/rag-index",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"model": model,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
RagDocumentIndexResponseModel,
construct_type(
type_=RagDocumentIndexResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[RagDocumentIndexResponseModel]
Successful Response
| compute_rag_index | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/document/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/document/raw_client.py | MIT |
async def compute_rag_index(
self,
documentation_id: str,
*,
model: EmbeddingModelEnum,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[RagDocumentIndexResponseModel]:
"""
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[RagDocumentIndexResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/rag-index",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"model": model,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
RagDocumentIndexResponseModel,
construct_type(
type_=RagDocumentIndexResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[RagDocumentIndexResponseModel]
Successful Response
| compute_rag_index | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/document/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/document/raw_client.py | MIT |
def create_from_url(
self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_url(
url="url",
)
"""
_response = self._raw_client.create_from_url(url=url, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_url(
url="url",
)
| create_from_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def create_from_file(
self,
*,
file: core.File,
name: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_file()
"""
_response = self._raw_client.create_from_file(file=file, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_file()
| create_from_file | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def create_from_text(
self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_text(
text="text",
)
"""
_response = self._raw_client.create_from_text(text=text, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.create_from_text(
text="text",
)
| create_from_text | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def get(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> DocumentsGetResponse:
"""
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
-------
DocumentsGetResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
"""
_response = self._raw_client.get(documentation_id, request_options=request_options)
return _response.data |
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
-------
DocumentsGetResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def delete(
self,
documentation_id: str,
*,
force: typing.Optional[bool] = None,
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
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.knowledge_base.documents.delete(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
"""
_response = self._raw_client.delete(documentation_id, force=force, request_options=request_options)
return _response.data |
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
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.knowledge_base.documents.delete(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def update(
self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None
) -> DocumentsUpdateResponse:
"""
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DocumentsUpdateResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.update(
documentation_id="21m00Tcm4TlvDq8ikWAM",
name="name",
)
"""
_response = self._raw_client.update(documentation_id, name=name, request_options=request_options)
return _response.data |
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DocumentsUpdateResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.update(
documentation_id="21m00Tcm4TlvDq8ikWAM",
name="name",
)
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def get_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.knowledge_base.documents.get_agents(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
"""
_response = self._raw_client.get_agents(
documentation_id, cursor=cursor, page_size=page_size, request_options=request_options
)
return _response.data |
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.knowledge_base.documents.get_agents(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
| get_agents | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def create_from_url(
self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_url(
url="url",
)
asyncio.run(main())
"""
_response = await self._raw_client.create_from_url(url=url, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_url(
url="url",
)
asyncio.run(main())
| create_from_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def create_from_file(
self,
*,
file: core.File,
name: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_file()
asyncio.run(main())
"""
_response = await self._raw_client.create_from_file(file=file, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_file()
asyncio.run(main())
| create_from_file | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def create_from_text(
self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AddKnowledgeBaseResponseModel:
"""
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_text(
text="text",
)
asyncio.run(main())
"""
_response = await self._raw_client.create_from_text(text=text, name=name, request_options=request_options)
return _response.data |
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AddKnowledgeBaseResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.create_from_text(
text="text",
)
asyncio.run(main())
| create_from_text | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def get(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> DocumentsGetResponse:
"""
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
-------
DocumentsGetResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.get(documentation_id, request_options=request_options)
return _response.data |
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
-------
DocumentsGetResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def delete(
self,
documentation_id: str,
*,
force: typing.Optional[bool] = None,
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
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.knowledge_base.documents.delete(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.delete(documentation_id, force=force, request_options=request_options)
return _response.data |
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
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.knowledge_base.documents.delete(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def update(
self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None
) -> DocumentsUpdateResponse:
"""
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DocumentsUpdateResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.update(
documentation_id="21m00Tcm4TlvDq8ikWAM",
name="name",
)
asyncio.run(main())
"""
_response = await self._raw_client.update(documentation_id, name=name, request_options=request_options)
return _response.data |
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
DocumentsUpdateResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.update(
documentation_id="21m00Tcm4TlvDq8ikWAM",
name="name",
)
asyncio.run(main())
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def get_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.knowledge_base.documents.get_agents(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.get_agents(
documentation_id, cursor=cursor, page_size=page_size, request_options=request_options
)
return _response.data |
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.knowledge_base.documents.get_agents(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| get_agents | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
async def get_content(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> None:
"""
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
None
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.get_content(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
"""
_response = await self._raw_client.get_content(documentation_id, request_options=request_options)
return _response.data |
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
None
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.get_content(
documentation_id="21m00Tcm4TlvDq8ikWAM",
)
asyncio.run(main())
| get_content | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/client.py | MIT |
def create_from_url(
self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/url",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"url": url,
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def create_from_file(
self,
*,
file: core.File,
name: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/file",
base_url=self._client_wrapper.get_environment().base,
method="POST",
data={
"name": name,
},
files={
"file": file,
},
request_options=request_options,
omit=OMIT,
force_multipart=True,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_file | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def create_from_text(
self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/text",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"text": text,
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_text | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def get(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[DocumentsGetResponse]:
"""
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
-------
HttpResponse[DocumentsGetResponse]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
DocumentsGetResponse,
construct_type(
type_=DocumentsGetResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[DocumentsGetResponse]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def delete(
self,
documentation_id: str,
*,
force: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
params={
"force": force,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def update(
self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[DocumentsUpdateResponse]:
"""
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[DocumentsUpdateResponse]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
DocumentsUpdateResponse,
construct_type(
type_=DocumentsUpdateResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[DocumentsUpdateResponse]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def get_agents(
self,
documentation_id: str,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[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
-------
HttpResponse[GetKnowledgeBaseDependentAgentsResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/dependent-agents",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"page_size": page_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetKnowledgeBaseDependentAgentsResponseModel,
construct_type(
type_=GetKnowledgeBaseDependentAgentsResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[GetKnowledgeBaseDependentAgentsResponseModel]
Successful Response
| get_agents | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def get_content(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[None]:
"""
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/content",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return HttpResponse(response=_response, data=None)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[None]
| get_content | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def create_from_url(
self, *, url: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/url",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"url": url,
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document generated by scraping the given webpage.
Parameters
----------
url : str
URL to a page of documentation that the agent will have access to in order to interact with users.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_url | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def create_from_file(
self,
*,
file: core.File,
name: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/file",
base_url=self._client_wrapper.get_environment().base,
method="POST",
data={
"name": name,
},
files={
"file": file,
},
request_options=request_options,
omit=OMIT,
force_multipart=True,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document generated form the uploaded file.
Parameters
----------
file : core.File
See core.File for more documentation
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_file | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def create_from_text(
self, *, text: str, name: typing.Optional[str] = OMIT, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[AddKnowledgeBaseResponseModel]:
"""
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/knowledge-base/text",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"text": text,
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
AddKnowledgeBaseResponseModel,
construct_type(
type_=AddKnowledgeBaseResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Create a knowledge base document containing the provided text.
Parameters
----------
text : str
Text content to be added to the knowledge base.
name : typing.Optional[str]
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[AddKnowledgeBaseResponseModel]
Successful Response
| create_from_text | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def get(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[DocumentsGetResponse]:
"""
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
-------
AsyncHttpResponse[DocumentsGetResponse]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
DocumentsGetResponse,
construct_type(
type_=DocumentsGetResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[DocumentsGetResponse]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def delete(
self,
documentation_id: str,
*,
force: typing.Optional[bool] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
params={
"force": force,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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.
force : typing.Optional[bool]
If set to true, the document will be deleted regardless of whether it is used by any agents and it will be deleted from the dependent agents.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def update(
self, documentation_id: str, *, name: str, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[DocumentsUpdateResponse]:
"""
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[DocumentsUpdateResponse]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"name": name,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
DocumentsUpdateResponse,
construct_type(
type_=DocumentsUpdateResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Update the name of a document
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
name : str
A custom, human-readable name for the document.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[DocumentsUpdateResponse]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def get_agents(
self,
documentation_id: str,
*,
cursor: typing.Optional[str] = None,
page_size: typing.Optional[int] = None,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[GetKnowledgeBaseDependentAgentsResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/dependent-agents",
base_url=self._client_wrapper.get_environment().base,
method="GET",
params={
"cursor": cursor,
"page_size": page_size,
},
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetKnowledgeBaseDependentAgentsResponseModel,
construct_type(
type_=GetKnowledgeBaseDependentAgentsResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[GetKnowledgeBaseDependentAgentsResponseModel]
Successful Response
| get_agents | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
async def get_content(
self, documentation_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[None]:
"""
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/content",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
return AsyncHttpResponse(response=_response, data=None)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get the entire content of a document from the knowledge base
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[None]
| get_content | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/raw_client.py | MIT |
def get(
self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> KnowledgeBaseDocumentChunkResponseModel:
"""
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
KnowledgeBaseDocumentChunkResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.chunk.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
chunk_id="chunk_id",
)
"""
_response = self._raw_client.get(documentation_id, chunk_id, request_options=request_options)
return _response.data |
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
KnowledgeBaseDocumentChunkResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.knowledge_base.documents.chunk.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
chunk_id="chunk_id",
)
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/client.py | MIT |
async def get(
self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> KnowledgeBaseDocumentChunkResponseModel:
"""
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
KnowledgeBaseDocumentChunkResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.chunk.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
chunk_id="chunk_id",
)
asyncio.run(main())
"""
_response = await self._raw_client.get(documentation_id, chunk_id, request_options=request_options)
return _response.data |
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
KnowledgeBaseDocumentChunkResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.knowledge_base.documents.chunk.get(
documentation_id="21m00Tcm4TlvDq8ikWAM",
chunk_id="chunk_id",
)
asyncio.run(main())
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/client.py | MIT |
def get(
self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[KnowledgeBaseDocumentChunkResponseModel]:
"""
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[KnowledgeBaseDocumentChunkResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/chunk/{jsonable_encoder(chunk_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
KnowledgeBaseDocumentChunkResponseModel,
construct_type(
type_=KnowledgeBaseDocumentChunkResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[KnowledgeBaseDocumentChunkResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/raw_client.py | MIT |
async def get(
self, documentation_id: str, chunk_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[KnowledgeBaseDocumentChunkResponseModel]:
"""
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[KnowledgeBaseDocumentChunkResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/knowledge-base/{jsonable_encoder(documentation_id)}/chunk/{jsonable_encoder(chunk_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
KnowledgeBaseDocumentChunkResponseModel,
construct_type(
type_=KnowledgeBaseDocumentChunkResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get details about a specific documentation part used by RAG.
Parameters
----------
documentation_id : str
The id of a document from the knowledge base. This is returned on document addition.
chunk_id : str
The id of a document RAG chunk from the knowledge base.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[KnowledgeBaseDocumentChunkResponseModel]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/knowledge_base/documents/chunk/raw_client.py | MIT |
def calculate(
self,
*,
prompt_length: int,
number_of_pages: int,
rag_enabled: bool,
request_options: typing.Optional[RequestOptions] = None,
) -> LlmUsageCalculatorResponseModel:
"""
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
LlmUsageCalculatorResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.llm_usage.calculate(
prompt_length=1,
number_of_pages=1,
rag_enabled=True,
)
"""
_response = self._raw_client.calculate(
prompt_length=prompt_length,
number_of_pages=number_of_pages,
rag_enabled=rag_enabled,
request_options=request_options,
)
return _response.data |
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
LlmUsageCalculatorResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.llm_usage.calculate(
prompt_length=1,
number_of_pages=1,
rag_enabled=True,
)
| calculate | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/llm_usage/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/llm_usage/client.py | MIT |
async def calculate(
self,
*,
prompt_length: int,
number_of_pages: int,
rag_enabled: bool,
request_options: typing.Optional[RequestOptions] = None,
) -> LlmUsageCalculatorResponseModel:
"""
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
LlmUsageCalculatorResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.llm_usage.calculate(
prompt_length=1,
number_of_pages=1,
rag_enabled=True,
)
asyncio.run(main())
"""
_response = await self._raw_client.calculate(
prompt_length=prompt_length,
number_of_pages=number_of_pages,
rag_enabled=rag_enabled,
request_options=request_options,
)
return _response.data |
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
LlmUsageCalculatorResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.llm_usage.calculate(
prompt_length=1,
number_of_pages=1,
rag_enabled=True,
)
asyncio.run(main())
| calculate | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/llm_usage/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/llm_usage/client.py | MIT |
def calculate(
self,
*,
prompt_length: int,
number_of_pages: int,
rag_enabled: bool,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[LlmUsageCalculatorResponseModel]:
"""
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[LlmUsageCalculatorResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/llm-usage/calculate",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"prompt_length": prompt_length,
"number_of_pages": number_of_pages,
"rag_enabled": rag_enabled,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
LlmUsageCalculatorResponseModel,
construct_type(
type_=LlmUsageCalculatorResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[LlmUsageCalculatorResponseModel]
Successful Response
| calculate | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/llm_usage/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/llm_usage/raw_client.py | MIT |
async def calculate(
self,
*,
prompt_length: int,
number_of_pages: int,
rag_enabled: bool,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[LlmUsageCalculatorResponseModel]:
"""
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[LlmUsageCalculatorResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/llm-usage/calculate",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json={
"prompt_length": prompt_length,
"number_of_pages": number_of_pages,
"rag_enabled": rag_enabled,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
LlmUsageCalculatorResponseModel,
construct_type(
type_=LlmUsageCalculatorResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Returns a list of LLM models and the expected cost for using them based on the provided values.
Parameters
----------
prompt_length : int
Length of the prompt in characters.
number_of_pages : int
Pages of content in PDF documents or URLs in the agent's knowledge base.
rag_enabled : bool
Whether RAG is enabled.
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[LlmUsageCalculatorResponseModel]
Successful Response
| calculate | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/llm_usage/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/llm_usage/raw_client.py | MIT |
def create(
self, *, request: PhoneNumbersCreateRequestBody, request_options: typing.Optional[RequestOptions] = None
) -> CreatePhoneNumberResponseModel:
"""
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CreatePhoneNumberResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
from elevenlabs.conversational_ai.phone_numbers import (
PhoneNumbersCreateRequestBody_Twilio,
)
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.create(
request=PhoneNumbersCreateRequestBody_Twilio(
phone_number="phone_number",
label="label",
sid="sid",
token="token",
),
)
"""
_response = self._raw_client.create(request=request, request_options=request_options)
return _response.data |
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CreatePhoneNumberResponseModel
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
from elevenlabs.conversational_ai.phone_numbers import (
PhoneNumbersCreateRequestBody_Twilio,
)
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.create(
request=PhoneNumbersCreateRequestBody_Twilio(
phone_number="phone_number",
label="label",
sid="sid",
token="token",
),
)
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
def get(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> PhoneNumbersGetResponse:
"""
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
-------
PhoneNumbersGetResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.get(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
"""
_response = self._raw_client.get(phone_number_id, request_options=request_options)
return _response.data |
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
-------
PhoneNumbersGetResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.get(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
def delete(
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.phone_numbers.delete(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
"""
_response = self._raw_client.delete(phone_number_id, request_options=request_options)
return _response.data |
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.phone_numbers.delete(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
def update(
self,
phone_number_id: str,
*,
agent_id: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> PhoneNumbersUpdateResponse:
"""
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
-------
PhoneNumbersUpdateResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.update(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
"""
_response = self._raw_client.update(phone_number_id, agent_id=agent_id, request_options=request_options)
return _response.data |
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
-------
PhoneNumbersUpdateResponse
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.update(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
def list(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.List[PhoneNumbersListResponseItem]:
"""
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[PhoneNumbersListResponseItem]
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.list()
"""
_response = self._raw_client.list(request_options=request_options)
return _response.data |
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[PhoneNumbersListResponseItem]
Successful Response
Examples
--------
from elevenlabs import ElevenLabs
client = ElevenLabs(
api_key="YOUR_API_KEY",
)
client.conversational_ai.phone_numbers.list()
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
async def create(
self, *, request: PhoneNumbersCreateRequestBody, request_options: typing.Optional[RequestOptions] = None
) -> CreatePhoneNumberResponseModel:
"""
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CreatePhoneNumberResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
from elevenlabs.conversational_ai.phone_numbers import (
PhoneNumbersCreateRequestBody_Twilio,
)
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.create(
request=PhoneNumbersCreateRequestBody_Twilio(
phone_number="phone_number",
label="label",
sid="sid",
token="token",
),
)
asyncio.run(main())
"""
_response = await self._raw_client.create(request=request, request_options=request_options)
return _response.data |
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
CreatePhoneNumberResponseModel
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
from elevenlabs.conversational_ai.phone_numbers import (
PhoneNumbersCreateRequestBody_Twilio,
)
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.create(
request=PhoneNumbersCreateRequestBody_Twilio(
phone_number="phone_number",
label="label",
sid="sid",
token="token",
),
)
asyncio.run(main())
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
async def get(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> PhoneNumbersGetResponse:
"""
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
-------
PhoneNumbersGetResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.get(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
"""
_response = await self._raw_client.get(phone_number_id, request_options=request_options)
return _response.data |
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
-------
PhoneNumbersGetResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.get(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
async def delete(
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.phone_numbers.delete(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
"""
_response = await self._raw_client.delete(phone_number_id, request_options=request_options)
return _response.data |
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.phone_numbers.delete(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
async def update(
self,
phone_number_id: str,
*,
agent_id: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> PhoneNumbersUpdateResponse:
"""
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
-------
PhoneNumbersUpdateResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.update(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
"""
_response = await self._raw_client.update(phone_number_id, agent_id=agent_id, request_options=request_options)
return _response.data |
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
-------
PhoneNumbersUpdateResponse
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.update(
phone_number_id="TeaqRRdTcIfIu2i7BYfT",
)
asyncio.run(main())
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
async def list(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> typing.List[PhoneNumbersListResponseItem]:
"""
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[PhoneNumbersListResponseItem]
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.list()
asyncio.run(main())
"""
_response = await self._raw_client.list(request_options=request_options)
return _response.data |
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
typing.List[PhoneNumbersListResponseItem]
Successful Response
Examples
--------
import asyncio
from elevenlabs import AsyncElevenLabs
client = AsyncElevenLabs(
api_key="YOUR_API_KEY",
)
async def main() -> None:
await client.conversational_ai.phone_numbers.list()
asyncio.run(main())
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/client.py | MIT |
def create(
self, *, request: PhoneNumbersCreateRequestBody, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[CreatePhoneNumberResponseModel]:
"""
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[CreatePhoneNumberResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/phone-numbers/create",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json=convert_and_respect_annotation_metadata(
object_=request, annotation=PhoneNumbersCreateRequestBody, direction="write"
),
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
CreatePhoneNumberResponseModel,
construct_type(
type_=CreatePhoneNumberResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[CreatePhoneNumberResponseModel]
Successful Response
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
def get(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[PhoneNumbersGetResponse]:
"""
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
-------
HttpResponse[PhoneNumbersGetResponse]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
PhoneNumbersGetResponse,
construct_type(
type_=PhoneNumbersGetResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[PhoneNumbersGetResponse]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
def delete(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
def update(
self,
phone_number_id: str,
*,
agent_id: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> HttpResponse[PhoneNumbersUpdateResponse]:
"""
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
-------
HttpResponse[PhoneNumbersUpdateResponse]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"agent_id": agent_id,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
PhoneNumbersUpdateResponse,
construct_type(
type_=PhoneNumbersUpdateResponse, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
HttpResponse[PhoneNumbersUpdateResponse]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
def list(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[typing.List[PhoneNumbersListResponseItem]]:
"""
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.List[PhoneNumbersListResponseItem]]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/phone-numbers/",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[PhoneNumbersListResponseItem],
construct_type(
type_=typing.List[PhoneNumbersListResponseItem], # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[typing.List[PhoneNumbersListResponseItem]]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
async def create(
self, *, request: PhoneNumbersCreateRequestBody, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[CreatePhoneNumberResponseModel]:
"""
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[CreatePhoneNumberResponseModel]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/phone-numbers/create",
base_url=self._client_wrapper.get_environment().base,
method="POST",
json=convert_and_respect_annotation_metadata(
object_=request, annotation=PhoneNumbersCreateRequestBody, direction="write"
),
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
CreatePhoneNumberResponseModel,
construct_type(
type_=CreatePhoneNumberResponseModel, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Import Phone Number from provider configuration (Twilio or SIP trunk)
Parameters
----------
request : PhoneNumbersCreateRequestBody
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[CreatePhoneNumberResponseModel]
Successful Response
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
async def get(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[PhoneNumbersGetResponse]:
"""
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
-------
AsyncHttpResponse[PhoneNumbersGetResponse]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
PhoneNumbersGetResponse,
construct_type(
type_=PhoneNumbersGetResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[PhoneNumbersGetResponse]
Successful Response
| get | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
async def delete(
self, phone_number_id: str, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="DELETE",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.Optional[typing.Any],
construct_type(
type_=typing.Optional[typing.Any], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[typing.Optional[typing.Any]]
Successful Response
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
async def update(
self,
phone_number_id: str,
*,
agent_id: typing.Optional[str] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> AsyncHttpResponse[PhoneNumbersUpdateResponse]:
"""
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
-------
AsyncHttpResponse[PhoneNumbersUpdateResponse]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
f"v1/convai/phone-numbers/{jsonable_encoder(phone_number_id)}",
base_url=self._client_wrapper.get_environment().base,
method="PATCH",
json={
"agent_id": agent_id,
},
headers={
"content-type": "application/json",
},
request_options=request_options,
omit=OMIT,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
PhoneNumbersUpdateResponse,
construct_type(
type_=PhoneNumbersUpdateResponse, # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
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
-------
AsyncHttpResponse[PhoneNumbersUpdateResponse]
Successful Response
| update | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
async def list(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> AsyncHttpResponse[typing.List[PhoneNumbersListResponseItem]]:
"""
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.List[PhoneNumbersListResponseItem]]
Successful Response
"""
_response = await self._client_wrapper.httpx_client.request(
"v1/convai/phone-numbers/",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
typing.List[PhoneNumbersListResponseItem],
construct_type(
type_=typing.List[PhoneNumbersListResponseItem], # type: ignore
object_=_response.json(),
),
)
return AsyncHttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Retrieve all Phone Numbers
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
AsyncHttpResponse[typing.List[PhoneNumbersListResponseItem]]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/phone_numbers/raw_client.py | MIT |
def create(
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.secrets.create(
name="name",
value="value",
)
"""
_response = self._raw_client.create(name=name, value=value, request_options=request_options)
return _response.data |
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.secrets.create(
name="name",
value="value",
)
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/secrets/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/secrets/client.py | MIT |
async def list(
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.secrets.list()
asyncio.run(main())
"""
_response = await self._raw_client.list(request_options=request_options)
return _response.data |
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.secrets.list()
asyncio.run(main())
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/secrets/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/secrets/client.py | MIT |
async def create(
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.secrets.create(
name="name",
value="value",
)
asyncio.run(main())
"""
_response = await self._raw_client.create(name=name, value=value, request_options=request_options)
return _response.data |
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.secrets.create(
name="name",
value="value",
)
asyncio.run(main())
| create | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/secrets/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/secrets/client.py | MIT |
async def delete(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.secrets.delete(
secret_id="secret_id",
)
asyncio.run(main())
"""
_response = await self._raw_client.delete(secret_id, request_options=request_options)
return _response.data |
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.secrets.delete(
secret_id="secret_id",
)
asyncio.run(main())
| delete | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/secrets/client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/secrets/client.py | MIT |
def list(
self, *, request_options: typing.Optional[RequestOptions] = None
) -> HttpResponse[GetWorkspaceSecretsResponseModel]:
"""
Get all workspace secrets for the user
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetWorkspaceSecretsResponseModel]
Successful Response
"""
_response = self._client_wrapper.httpx_client.request(
"v1/convai/secrets",
base_url=self._client_wrapper.get_environment().base,
method="GET",
request_options=request_options,
)
try:
if 200 <= _response.status_code < 300:
_data = typing.cast(
GetWorkspaceSecretsResponseModel,
construct_type(
type_=GetWorkspaceSecretsResponseModel, # type: ignore
object_=_response.json(),
),
)
return HttpResponse(response=_response, data=_data)
if _response.status_code == 422:
raise UnprocessableEntityError(
headers=dict(_response.headers),
body=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, headers=dict(_response.headers), body=_response.text)
raise ApiError(status_code=_response.status_code, headers=dict(_response.headers), body=_response_json) |
Get all workspace secrets for the user
Parameters
----------
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
Returns
-------
HttpResponse[GetWorkspaceSecretsResponseModel]
Successful Response
| list | python | elevenlabs/elevenlabs-python | src/elevenlabs/conversational_ai/secrets/raw_client.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/src/elevenlabs/conversational_ai/secrets/raw_client.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.