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Using LLMs with Rasa

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Rasa Labs
Rasa Labs access - New in 3.7.0b1

Rasa Labs features are experimental. We introduce experimental features to co-create with our customers. To find out more about how to participate in our Labs program visit our Rasa Labs page.

We are continuously improving Rasa Labs features based on customer feedback. To benefit from the latest bug fixes and feature improvements, please install the latest pre-release using:

pip install 'rasa-plus>3.6' --pre --upgrade

As part of a beta release, we have released multiple components which make use of the latest generation of Large Language Models (LLMs). This document offers an overview of what you can do with them. We encourage you to experiment with these components and share your findings with us. We are working on some larger changes to the platform that leverage LLMs natively. Please reach out to us if you'd like to learn more about upcoming changes.

LLMs can do more than just NLU

The recent advances in large language models (LLMs) have opened up new possibilities for conversational AI. LLMs are pretrained models that can be used to perform a variety of tasks, including intent classification, dialogue handling, and natural language generation (NLG). The components described here all use in-context learning. In other words, instructions and examples are provided in a prompt which are sent to a general-purpose LLM. They do not require fine-tuning of large models.

Plug & Play LLMs of your choice

Just like our NLU pipeline, the LLM components here can be configured to use different LLMs. There is no one-size-fits-all best model, and new models are being released every week. We encourage you to try out different models and evaluate their performance on different languages in terms of fluency, accuracy, and latency.

An adjustable risk profile

The potential and risks of LLMs vary per use case. For customer-facing use cases, you may not ever want to send generated text to your users. Rasa gives you full control over where and when you want to make use of LLMs. You can use LLMs for NLU and dialogue, and still only send messages that were authored by a human. You can also allow an LLM to rephrase your existing messages to account for context.

It's essential that your system provides full control over these processes. Understanding how LLMs and other components behave and have the power to override any decision.

Where to go from here

This section of the documentation guides you through the diverse ways you can integrate LLMs into Rasa. We will delve into the following topics:

  1. Setting up LLMs
  2. Intentless Policy
  3. LLM Intent Classification
  4. Response Rephrasing

Each link will direct you to a detailed guide on the respective topic, offering further depth and information about using LLMs with Rasa. By the end of this series, you'll be equipped to effectively use LLMs to augment your Rasa applications.