Release v0.3.0.

This release focuses on enhancing the support to integrate additional LLMs with NeMo Guardrails. It adds the ability to customize the prompt for various LLMs, including support for completion and chat models. This release adds examples for using the HuggingFace pipeline and inference endpoints. Last but not least, this release provides an initial evaluation of the core prompting technique and some of the rails.

Added

• Support for defining subflows.
• Improved support for customizing LLM prompts
  • Support for using filters to change how variables are included in a prompt template.
  • Output parsers for prompt templates.
  • The verbose_v1 formatter and output parser to be used for smaller models that don't understand Colang very well in a few-shot manner.
  • Support for including context variables in prompt templates.
  • Support for chat models i.e. prompting with a sequence of messages.
• Experimental support for allowing the LLM to generate multi-step flows.
• Example of using Llama Index from a guardrails configuration (#40).
• Example for using HuggingFace Endpoint LLMs with a guardrails configuration.
• Example for using HuggingFace Pipeline LLMs with a guardrails configuration.
• Support to alter LLM parameters passed as model_kwargs in LangChain.
• CLI tool for running evaluations on the different steps (e.g., canonical form generation, next steps, bot message) and on existing rails implementation (e.g., moderation, jailbreak, fact-checking, and hallucination).
• Initial evaluation results for text-davinci-003 and gpt-3.5-turbo.
• The lowest_temperature can be set through the guardrails config (to be used for deterministic tasks).

Changed

• The core templates now use Jinja2 as the rendering engines.
• Improved the internal prompting architecture, now using an LLM Task Manager.

Fixed

• Fixed bug related to invoking a chain with multiple output keys.
• Fixed bug related to tracking the output stats.
• #51: Bug fix - avoid str concat with None when logging user_intent.
• #54: Fix UTF-8 encoding issue and add embedding model configuration.

Release v0.2.0

Update CHANGELOG and setup.py.