Authorizations
The Authentication API is used by multiple Lucidworks products such as Lucidworks AI. The scope value in the request is unique to the product accessing the API.
The Client ID is used for the Username value, and the Client Secret is used for the Password value.
How do I regenerate my client secret?
Contact support if you need to regenerate your client secret.
Headers
The no-cache
request directive asks caches to validate the response with the origin server before reuse.
no-cache
The Accept request HTTP header indicates which content types the client can understand.
application/json
The Content-Type header indicates the original media type of the resource.
application/x-www-form-urlencoded
Path Parameters
The authorization code used to generate an access token.
ausao8uveaPmyhv0v357
Query Parameters
The value defines the scope of access requested. For the token used by the Lucidworks AI API usecase
, prediction
, async-prediction
, and async-chunking
endpoints, use "scope": "machinelearning.predict"
. For the token used by the Lucidworks AI API model
endpoint, use "scope": "machinelearning.model"
.
machinelearning.predict
, machinelearning.model
The client_credentials
grant type is used by clients to obtain an access token in the context of the client itself, not the user.
client_credentials
Response
OK
The token response object, which contains the ACCESS_TOKEN
required by operation APIs.
The token type, which is always a bearer token.
Bearer
The number of seconds before the ACCESS_TOKEN
expires. Auth tokens expire in 1 hour. A new token is required after that time.
3600
This value is used for the Token parameter required by the API.
500 - 1000
The value defines the scope of access granted and matches the scope
value from the request.
For the token used by the Lucidworks AI API usecase
, prediction
, async-prediction
, and async-chunking
endpoints, use "scope": "machinelearning.predict"
.
For the token used by the Lucidworks AI API model
endpoint, use "scope": "machinelearning.model"
.
machinelearning.predict
, machinelearning.model