LlamaBot now has StructuredBot!
Custom validation on top of structured generation -- I'm excited about this and the possibilities!
Hello fellow datanistas!
Have you ever wondered how to enhance your projects with structured outputs from language models? I've been thinking about this topic with my latest updates to LlamaBot, and I'm thrilled to share how I did it with you! In my recent blog post, I discuss the integration of StructuredBot into LlamaBot, a tool that significantly simplifies structured data generation using language models.
In the post, I detail how StructuredBot works, including its reliance on Pydantic models to ensure type-safe outputs, and how it can be used in various applications like automated documentation checks and more. This approach not only enhances reliability but also opens up new possibilities for using language models in your projects.
I invite you to read the full story and explore the technical details on my blog: LlamaBot now has StructuredBot!. I believe you'll find it both informative and inspiring, providing you with new tools and ideas for your own work.
If you find the post helpful, please feel free to share it with others who might also benefit from these insights. Your support and feedback are always appreciated as they help me improve and bring more relevant content to you.
Looking forward to hearing your thoughts and experiences with StructuredBot!
Cheers,
Eric
FWIW outlines also returns a pydantic model as a result. When you call
outlines.generate.json(model, PydanticModel)
you'll receive a pydantic model back.
This looks really great. The most promising potential LLM use cases I've seen all involve extracting structured data from free text, so this seems like a very natural and important tool to add to the toolbox.