This section covers how to use the NeMo Guardrails library Python API to run guardrailed inference and integrate the guardrails into your application.
RailsConfig and LLMRails core classes for generating guarded responses.
ConceptRailsConfig and LLMRails class reference for loading and running guardrails.
ReferenceConfigure logging, LLM parameters, and rail selection for generation.
ReferenceStream LLM responses in real-time with the stream_async method.
TutorialValidate messages against input and output rails using check_async and check methods.
ReferenceUse generate_events for low-level control over guardrails execution.
Reference