hamilton
fastapi-azure-auth
hamilton | fastapi-azure-auth | |
---|---|---|
21 | 17 | |
1,373 | 391 | |
7.4% | 2.3% | |
9.8 | 7.6 | |
6 days ago | 12 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT License |
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
- 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
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Langchain Is Pointless
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.
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Free access to beta product I'm building that I'd love feedback on
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:
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IPyflow: Reactive Python Notebooks in Jupyter(Lab)
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.)
fastapi-azure-auth
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FastUI: Build Better UIs Faster
I'm under the impression that you work for a company that sells services related to FastAPI? https://github.com/Intility/fastapi-azure-auth
I maintain an open source library in my spare time for free, that you are welcome to ignore if you find better alternatives.
- Implement AzureAD in 10 minutes with FastAPI-Azure-Auth - full tutorial in the documentation
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FastAPI Azure Auth đź”’ Now supports B2C (as well as single- and multi-tenant applications)
The documentation has a full tutorial in “Tiangolo-style”, which means it guided through setting up a project from scratch, and how to configure Azure appregs from scratch.
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Ask HN: Good Python projects to read for modern Python?
I think, in general, most FastAPI and Pydantic related libraries are heavily typed, use poetry, GitHub pipelines, black, isort, flake8 etc. so if you want to look at the ecosystem around a package I’ll recommend a few here, that has a smaller scope than the huge libraries Pydantic/FastAPI are. All packages listed below has all these things.
FastAPI-Azure-Auth [0] is a library to do authentication and authorization through Azure AD using tokens.
ASGI—Correlation-ID[1] is a package that utilizes contextvars to store information through the asyncio stack, in order to attach correlation/request ID to every log message from a request. Available for Django in [2].
Pydantic-factories [3] is an awesome library to mock data for your pydantic models.
[0] https://github.com/Intility/fastapi-azure-auth
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OAuth2 authorization with other flows beyond password.
If you want to use an external auth provider, I have written a library called FastAPI-Azure-Auth for authentication and authorization using Azure AD (which is free for something like 10.000 users). The tutorial should get you up and running quickly. Please note that this library is only intended to use for APIs (such as I sing a SPA frontend), so if you use jinja templates or render HTML from FastAPI this might not be the solution for you.
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FastAPI Azure AD Authentication đź”’ Now supports both single- and multi-tenants applications
Hi! I’m the author of FastAPI-Azure-Auth, a package to handle Azure AD authentication and authorization for your FastAPI APIs. It’s a heavily tested package, supports trio, and the documentation has a full tutorial on how to set up both Azure and FastAPI from scratch.
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Tips for Making a Popular Open-Source Project in 2021 [Ultimate Guide]
I agree with you. Most my packages are around ~100 stars, and I'm met with a lot of respect and appreciatio.n[1][2]
My library for Correlation-IDs in Django[3] got implemented by AWX, which also was a nice experience![4] I maintain a lot of small packages, and maybe it is the Django/FastAPI community, but "you'll get a load of entitled users" is straight up not true in my experience.
[1] https://github.com/Intility/fastapi-azure-auth/issues/24
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Show HN: Implement Azure AD auth for your FastAPI
The documentation[1] contains a full tutorial on how to configure Azure AD and FastAPI for both single- and multi-tenant applications. It includes examples on how to lock down your APIs to certain scopes, tenants, roles etc.
[1] https://intility.github.io/fastapi-azure-auth/)
- Azure AD authentication for FastAPI đź”’ Now supports both single- and multi-tenants. Documentation includes a full tutorial on how to set it up from scratch
- Azure AD authentication đź”’ Now supports both single- and multi-tenants, and has a full setup tutorial for both FastAPI and Azure.
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
azure-functions-python-samples - Azure Functions Python Sample Codes. NOTE: The project, hosted in a repository, is no longer actively maintained by its creators or contributors. There won't be any further updates, bug fixes, or support from the original developers in the project.
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
best-of-web-python - 🏆 A ranked list of awesome python libraries for web development. Updated weekly.
snowpark-python - Snowflake Snowpark Python API
uvicorn-gunicorn-fastapi-docker - Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python with performance auto-tuning.
aipl - Array-Inspired Pipeline Language
asgi-correlation-id - Request ID propagation for ASGI apps
vscode-reactive-jupyter - A simple Reactive Python Extension for Visual Studio Code
Installation - The premier source of truth powering network automation. Open source under Apache 2. Public demo: https://demo.netbox.dev