original-demo-ui VS parser

Hi HN, we are Nick and Ben, creators of Axilla.

Axilla is an open source TypeScript framework to develop LLM applications.

It’s in the early stages but you can use it today: we’ve already published 2 modules and have more coming soon!

Ben and I met while working at Cruise on the ML platform for self-driving cars. We spent many years there and learned the hard way that shipping AI is not quite the same as shipping regular code. There are many parts of the ML lifecycle, e.g., mining, processing, and labeling data and training, evaluating, and deploying models. Although none of them are rocket science, most of the inefficiencies tend to come from integrating them together. At Cruise, we built an integrated framework that accelerated the speed of shipping models to the car by 80%.

With the explosion of generative AI, we are seeing software teams building applications and features with the same inefficiencies we experienced at Cruise.

This got us excited about building an opinionated, end-to-end platform. We started building in Python but quickly noticed that most of the teams we talked to weren’t using Python, but instead building in TypeScript. This is because most teams are not training their own models, but rather using foundational ones served by third parties over HTTP, like openAI, anthropic or even OSS ones from hugging face.

Because of this, we’ve decided to build Axilla as a TypeScript first library.

Our goal is to build a modular framework that can be adopted incrementally yet benefits from full integration. For example, the production responses coming from the LLM should be able to be sent — with all necessary metadata — to the eval module or the labeling tooling.

So far, we’ve shipped 2 modules, that are available to use today on npm:

* *axgen*: focused on RAG type workflows. Useful if you want to ingest data, get the embeddings, store it in a vector store and then do similarity search retrieval. It’s how you give LLMs memory or more context about private data sources.

* *axeval*: a lightweight evaluation library, that feels like jest (so, like unit tests). In our experience, evaluation should be really easy to setup, to encourage continuous quality monitoring, and slowly build ground truth datasets of edge cases that can be used for regression testing, and fine-tuning.

We are working on a serving module and a data processing one next and would love to hear what functionality you need us to prioritize!

We built an open-source demo UI for you to discover the framework more: https://github.com/axilla-io/demo-ui

And here's a video of Nicholas walking through the UI that gives an idea of what axgen can do: https://www.loom.com/share/458f9b6679b740f0a5c78a33fffee3dc

We’d love to hear your feedback on the framework, you can let us know here, create an issue on the GitHub repo or send me an email at [email protected]

And of course, contributions welcome!