hamilton VS hamilton

Note that this uses simple OpenAI calls — you can replace this with Langchain, LlamaIndex, Hamilton (or something else) if you prefer more abstraction, and delegate to whatever LLM you like to use. And, you should probably use something a little more concrete (E.G. instructor) to guarantee output shape.

In this post, we’ll show how your team can turn any utility function(s) into reusable IPython Jupyter magics for a better notebook experience. As an example, we’ll use Hamilton, my open source library, to motivate the creation of a magic that facilitates better development ergonomics for using it. You needn’t know what Hamilton is to understand this post.

We built an app with it -- https://blog.dagworks.io/p/building-a-lightweight-experiment. You can see the code here https://github.com/DAGWorks-Inc/hamilton/blob/main/hamilton/....

Usually we've been prototyping with streamlit, but found that at times to be clunky. FastUI still has rough edges, but we made it work for our lightweight app.

This library is old news? Is there anything new that they've added that's noteworthy to take it for another spin?

[disclaimer I'm a maintainer of Hamilton] Otherwise FYI Prophet gels well with https://github.com/DAGWorks-Inc/hamilton for setting up your features and dataset for fitting & prediction[/disclaimer].

I had been hearing these pains from Langchain users for quite a while. Suffice to say I think:

1. too many layers of OO abstractions are a liability in production contexts. I'm biased, but a more functional approach is a better way to model what's going on. It's easier to test, wrap a function with concerns, and therefore reason about.

2. as fast as the field is moving, the layers of abstractions actually hurt your ability to customize without really diving into the details of the framework, or requiring you to step outside it -- in which case, why use it?

Otherwise I definitely love the small amount of code you need to write to get an LLM application up with Langchain. However you read code more often than you write it, in which case this brevity is a trade-off. Would you prefer to reduce your time debugging a production outage? or building the application? There's no right answer, other than "it depends".

To that end - we've come up with a post showing how one might use Hamilton (https://github.com/dagWorks-Inc/hamilton) to easily create a workflow to ingest data into a vector database that I think has a great production story. https://open.substack.com/pub/dagworks/p/building-a-maintain...

Note: Hamilton can cover your MLOps as well as LLMOps needs; you'll invariably be connecting LLM applications with traditional data/ML pipelines because LLMs don't solve everything -- but that's a post for another day.

This is me. I drive an open source library Hamilton that people doing time-series/ML work love to use. I'm building a paid product around it at DAGWorks, and I'm after feedback on our current version. Can I entice anyone to:

From a nuts and bolts perspective, I've been thinking of building some reactivity on top of https://github.com/dagworks-inc/hamilton (author here) that could get at this. (If you have a use case that could be documented, I'd appreciate it.)

And find the repository here: https://github.com/dagworks-inc/hamilton/

Python - https://github.com/stitchfix/hamilton (author here). It's great if you want your code to be always unit testable and documentation friendly, and you want to be able to visualize execution. Blog post on using it with Pandas https://link.medium.com/XhyYD9BAntb.

Yes! As one of the creators of https://github.com/stitchfix/hamilton this was one of the aims. Simplifying the cognitive burden for those developing and managing data transforms over the course of years, and in particular for ones they didn't write!

For example in Hamilton -- we force people to write "declarative functions" which then are stitched together to create a dataflow.

E.g. example function -- my guess is that you can read and understand/guess what it does very easily.

For (1) there are quite a few options - prefect is one, metaflow is another, airflow, dagster, even https://github.com/stitchfix/hamilton (core contributor here), etc.

If you're want to do more python https://github.com/stitchfix/hamilton allows you to model dependencies at a columnar (field) level.

Take a look at https://github.com/stitchfix/hamilton - we're after contributors who can help us grow the project, e.g. make documentation great, dog fooding features and suggesting/contributing usability improvements.

For a real world example of their power, we built an entire framework (https://github.com/stitchfix/hamilton) at Stitch Fix, where a lot of cool magic is provide via decorators - see https://hamilton-docs.gitbook.io/docs/reference/api-reference/available-decorators and these two source files (https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers_base.py, https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers.py ). Note we do some non-trivial stuff via them.

For data processing/transform code, I would recommend looking at https://github.com/stitchfix/hamilton, especially if you're trying to test pandas code. Short getting started here - https://towardsdatascience.com/how-to-use-hamilton-with-pandas-in-5-minutes-89f63e5af8f5 (disclaimer: I'm one of the authors).

The python package, hamilton, from Stitch Fix (https://hamilton-docs.gitbook.io/docs/) can help manage transformations on pandas dataframes. This DAG of transformations is managed separately in a file - so it can be versioned, in case the transformations change. The memory required is reduced, because only the API call tables and mapping parameter table have to be in memory. The calculated columns can be produced as needed. Just like dbt, transformations are separate from the source tables - but hamilton can be used on any python object - not just dataframes. dbt is SQL based.