id stringUnique identifier of the NLU engine in the workspace. |
engine_version stringVersion of the specified NLU engine. Since multiple deployments are feasible, this specifies the exact image which will be used when using an external NLU engine. This parameter has no impact for the internal engine. |
is_default booleanInternally managed flag to indicate that this is the default engine of the workspace. This should not be modified via the API as it is enforced by the backend. |
seq_id uint32Internally managed non-zero unique sequential number assigned to the engine. This should not be modified via the API as it is enforced by the backend. |
on_demand booleanOnly use the NLU engine when it is explicitely triggered. Useful to prevent expensive NLU engines (ex: DialogFlow) from being triggered automatically. |
max_retry UInt32ValueWrapper message for uint32 . The JSON representation for UInt32Value is JSON number. |
integration_id string(Optional) Unique identifier of the integration if the NLU engine is linked to an external integration. |
training_tag_predicate TagPredicaterequire_ids string[]Only include objects with ALL of the given tag ids. | include_ids string[]Only include objects with ANY of the given tag ids. | exclude_ids string[]Exclude objects with ANY of the given tag ids. |
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intent_tag_predicate TagPredicaterequire_ids string[]Only include objects with ALL of the given tag ids. | include_ids string[]Only include objects with ANY of the given tag ids. | exclude_ids string[]Exclude objects with ANY of the given tag ids. |
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internal NluEngineInternal |
rasa NluEngineRasapipeline_config stringContents of the config.yml to be used for training |
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dialogflow_cx NluEngineDialogflowCxproject_id stringGCP project of the agent. | location stringGCP location of the agent (ex: northamerica-northeast1) If empty, global is assumed. | credential_id stringThe id of the GCP credential to use |
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huggingface NluEngineHuggingFacebase_model stringThe base model to start from. See https://huggingface.co/models The model needs to use a supported architecture and support TensorFlow (currently) e.g bert-base-uncased | config_json string(Optional) A json configuration to be merged with the base model's default configuration | training_args_json string(Optional) A json object containing training (hyper-) parameters |
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