Rigging

Rigging is a lightweight LLM interaction framework built on Pydantic XML. The goal is to make leveraging LLMs in production pipelines as simple and effictive as possible. Here are the highlights:

• Structured Pydantic models can be used interchangably with unstructured text output.
• LiteLLM as the default generator giving you instant access to a huge array of models.
• Add easy tool calling abilities to models which don't natively support it.
• Store different models and configs as simple connection strings just like databases.
• Chat templating, forking, continuations, generation parameter overloads, stripping segments, etc.
• Modern python with type hints, async support, pydantic validation, serialization, etc.

import rigging as rg
from rigging.model import CommaDelimitedAnswer as Answer

chat = rg.get_generator('gpt-4') \
    .chat(f"Give me 3 famous authors between {Answer.xml_tags()} tags.") \
    .until_parsed_as(Answer) \
    .run()

answer = chat.last.parse(Answer)
print(answer.items)

# ['J. R. R. Tolkien', 'Stephen King', 'George Orwell']

Rigging is built and maintained by dreadnode where we use it daily for our work.

Installation

We publish every version to Pypi:

pip install rigging

If you want to build from source:

cd rigging/
poetry install

Getting Started

Head over to our documentation for more information.