API Overview#
Modules#
nemoguardrails.rails.llm.config: Module for the configuration of rails.nemoguardrails.rails.llm.llmrails: LLM Rails entry point.
Classes#
basic.BasicEmbeddingsIndex: Basic implementation of an embeddings index.basic.OpenAIEmbeddingModel: Embedding model using OpenAI API.basic.SentenceTransformerEmbeddingModel: Embedding model using sentence-transformers.index.EmbeddingModel: The embedding model is responsible for creating the embeddings.index.EmbeddingsIndex: The embeddings index is responsible for computing and searching a set of embeddings.index.IndexItem: IndexItem(text: str, meta: Dict =) config.CoreConfig: Settings for core internal mechanics.config.DialogRails: Configuration of topical rails.config.Document: Configuration for documents that should be used for question answering.config.EmbeddingSearchProvider: Configuration of a embedding search provider.config.FactCheckingRailConfig: Configuration data for the fact-checking rail.config.InputRails: Configuration of input rails.config.Instruction: Configuration for instructions in natural language that should be passed to the LLM.config.MessageTemplate: Template for a message structure.config.Model: Configuration of a model used by the rails engine.config.OutputRails: Configuration of output rails.config.Rails: Configuration of specific rails.config.RailsConfig: Configuration object for the models and the rails.config.RailsConfigData: Configuration data for specific rails that are supported out-of-the-box.config.RetrievalRails: Configuration of retrieval rails.config.SensitiveDataDetection: Configuration of what sensitive data should be detected.config.SingleCallConfig: Configuration for the single LLM call option for topical rails.config.TaskPrompt: Configuration for prompts that will be used for a specific task.config.UserMessagesConfig: Configuration for how the user messages are interpreted.llmrails.LLMRails: Rails based on a given configuration.streaming.StreamingHandler: Streaming async handler.
Functions#
basic.init_embedding_model: Initialize the embedding model.