phidata VS hamilton

Hi HN, I built an app to chat with arXiv papers: https://arxiv.aidev.run

I’m using function calling to interact with the arXiv api, here’s the general flow:

> For a users question, search the knowledge base (pgvector) for the topic/paper

> If knowledge base results are not relevant, search arXiv api for paper, parse it and store it in the knowledge base

> Answer questions or summarize using contents from the knowledge base.

Give it a spin at: https://arxiv.aidev.run and let me know what you think.

Its a work in progress and I’m looking for feedback on how to improve. The read time from the arXiv api is a bit slow – but not much I can do about it.

I used phidata to build this: https://github.com/phidatahq/phidata

Here’s the code if you’re interested: https://github.com/phidatahq/ai-cookbook/blob/main/arxiv_ai/assistant.py

- I used phidata to build this: https://github.com/phidatahq/phidata

Hi HN, I built an AI that can interact with the Hacker News API and answer questions about hackernews stories, whats trending, what on show etc..

Check it out here: https://hn.aidev.run

You can ask questions like:

- What on hackernews about AI?

- What on hackernews about iPhone?

- What's trending on hackernews?

- What are users showing on hackernews?

- What are users asking on hackernews?

- Summarize this story: https://news.ycombinator.com/item?id=39156778

It uses function calling to query the HN api.

To answer questions about a particular topic, it’ll search its knowledge base (a vector db that is periodically updated with the “top stories”) and get details about those stories from the API.

This is pretty barebones and I built it today in < 2 hours, so it probably won’t meet your high standards. If you give it a try, I’d love your feedback on how I can improve it.

If you’re interested, I built this using phidata: https://github.com/phidatahq/phidata

Thanks for reading and would love to hear what you think.

- Summarize this story: https://news.ycombinator.com/item?id=39156778

It uses function calling to query the HN api.

To answer questions about a particular topic, it’ll search its knowledge base (a vector db that is periodically updated with the “top stories”) and get details about those stories from the API.

This is pretty barebones and I built it today in < 2 hours, so it probably won’t meet your high standards. If you give it a try, I’d love your feedback on how I can improve it.

If you’re interested, I built this using phidata: https://github.com/phidatahq/phidata

Thanks for reading and would love to hear what you think.

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.)