phidata
hamilton
phidata | hamilton | |
---|---|---|
15 | 26 | |
5,340 | 878 | |
47.1% | - | |
9.9 | 8.1 | |
4 days ago | about 1 year ago | |
Python | Python | |
Mozilla Public License 2.0 | BSD 3-clause Clear License |
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phidata
- Phidata: Add memory, knowledge and tools to LLMs
- Show HN: Use function calling to build AI Assistants
- Phidata: Build AI Assistants using function calling
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Chat with ArXiv Papers
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
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Chat with PDFs using function calling
- I used phidata to build this: https://github.com/phidatahq/phidata
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Show HN: Hacker News AI built using function calling
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.
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Show HN: Hacker News AI
- 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.
- Show HN: Build AI Assistants using LLM function calling
- AI App Templates pre-built
- Build Autonomous Assistants using LLM function calling
hamilton
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Write production grade pandas (and other libraries!) with Hamilton
And find the repository here: https://github.com/dagworks-inc/hamilton/
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Useful libraries for data engineering in various programming languages
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.
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Cognitive Loads in Programming
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.
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Prefect vs other things question
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.
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Field Lineage
If you're want to do more python https://github.com/stitchfix/hamilton allows you to model dependencies at a columnar (field) level.
- Show HN
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[D] Is anyone working on interesting ML libraries and looking for contributors?
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.
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Useful Python decorators for Data Scientists
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.
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unit tests
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).
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Dealing with hundreds of customer/computed columns
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.