hamilton
phidata
hamilton | phidata | |
---|---|---|
24 | 18 | |
2,007 | 18,501 | |
3.1% | - | |
9.7 | 9.9 | |
5 days ago | 5 days ago | |
Jupyter Notebook | Python | |
BSD 3-clause Clear License | Mozilla Public License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hamilton
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Show HN: I built an open-source data pipeline tool in Go
I always thought Hamilton [1] does a good job of giving enough visual hooks that draw you in.
I also noticed this pattern where library authors sometimes do a bit extra in terms of discussing and even promoting their competitors, and it makes me trust them more. A “heres why ours is better and everyone else sucks …” section always comes across as the infomercial character who is having quite a hard time peeling an apple to the point you wonder if this the first time they’ve used hands.
One thing wish for is a tool that’s essentially just Celery that doesn’t require a message broker (and can just use a database), and which is supported on Windows. There’s always a handful of edge cases where we’re pulling data from an old 32-bit system on Windows. And basically every system has some not-quite-ergonomic workaround that’s as much work as if you’d just built it yourself.
It seems like it’s just sending a JSON message over a queue or HTTP API and the worker receives it and runs the task. Maybe it’s way harder than I’m envisioning (but I don’t think so because I’ve already written most of it).
I guess that’s one thing I’m not clear on with Bruin, can I run workers if different physical locations and have them carry out the tasks in the right order? Or is this more of a centralized thing (meaning even if its K8s or Dask or Ray, those are all run in a cluster which happens to be distributed, but they’re all machines sitting in the same subnet, which isn’t the definition of a “distributed task” I’m going for.
[1] https://github.com/DAGWorks-Inc/hamilton
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Greppability is an underrated code metric
Yep. When I was designing https://github.com/dagworks-inc/hamilton part of the idea was to make it easy to understand what and where. That is, enable one to grep for function definitions and their downstream use easily, and where people can't screw this up. You'd be surprised how easy it is to make a code base where grep doesn't help you all that much (at least in the python data transform world) ...
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Ask HN: What are you working on (August 2024)?
Graph-based libraries for building ML/AI systems:
- Burr -- build AI applications/agents as state machines https://github.com/dagworks-inc/burr
- Hamilton -- build dataflows as DAGs: https://github.com/dagworks-inc/hamilton
Looking for feedback -- we had some good initial traction on HN, and are looking for OS users/contributors/people who are building complimentary tooling!
- Show HN: Hamilton's UI – observability, lineage, and catalog for data pipelines
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Building an Email Assistant Application with Burr
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.
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Using IPython Jupyter Magic commands to improve the notebook experience
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.
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FastUI: Build Better UIs Faster
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.
- Show HN: On Garbage Collection and Memory Optimization in Hamilton
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Facebook Prophet: library for generating forecasts from any time series data
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].
- Show HN: Declarative Spark Transformations with Hamilton
phidata
- AI and All Data Weekly - 02 December 2024
- Show HN: Phidata – Build AI Agents with memory, knowledge, tools and reasoning
- AI Agents with memory, knowledge and tools
- 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.
What are some alternatives?
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
NeumAI - Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
kaizen - Automate the tedious development tasks with AI
modelfusion - The TypeScript library for building AI applications.
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
aipl - Array-Inspired Pipeline Language
captureflow-py - CaptureFlow - LLM-powered code maintenance that delivers reliable results.
snowpark-python - Snowflake Snowpark Python API
ai-cookbook
vscode-reactive-jupyter - A simple Reactive Python Extension for Visual Studio Code
composio - Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling