dispatch
haystack
dispatch | haystack | |
---|---|---|
20 | 55 | |
4,723 | 14,197 | |
2.6% | 4.0% | |
9.9 | 9.9 | |
8 days ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Apache 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.
dispatch
- Netflix Dispatch
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Is there any open source project that uses FasAPI?
They use only sync routes in the project and can’t explain why https://github.com/Netflix/dispatch/issues/1073
- Is it really advisable to try to run fastapi with predominantly sync routes in a real world application?
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How to build a scalable project file structure for a beginner.
By far my favorite production FastAPI app to use as a references of how to use these technologies well is NetFlix Dispatch: https://github.com/Netflix/dispatch
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FastAPI Boilerplate using MongoDB, Motor, Docker
Hey, I have a lot of opinions about this template, but these are just my opinions based on my own experiences being burned by these things so take from them what you will: 1. Your version of poetry is outdated, dependency groups don't work that way anymore and this will fail to install on modern poetry 2. You list pyyaml as a dependency but don't use it anywhere 3. The healthcheck endpoint is interesting, but expensive and a security risk. I like the value this provides, but I don't know if exposing it this way or using it as a healthcheck is a good idea 1. You typically don't want to touch external systems (mongo) as part of a healthcheck as this can cause cascading failure chains that get out of hand quickly 2. You typically don't want to touch the underlying system itself 1. which means you can / should get rid of psutil as a dependency 4. You don't need and shouldn't use pytest-asyncio for a FastAPI project. It comes built-in with its own async test handlers that you should be using 5. Having python-dotenv installed in production has burned me many times. I recommend removing this complete, otherwise just moving it to a dev dep 6. Using the src layout prevents a lot of weird import time problems from cropping up in production, I recommend checking it out 7. The entrypoint for the Docker container should be using 1 worker, as containers really prefer if you have only a single root PID chain and nothing else. Deploying this into k8s would cause a lot of issues 8. Native python logging really isn't great for modern production applications. Structlog or Loguru are great alternatives and much easier to use (which should remove your only dependency on pyyaml) 9. The configuration management may not work the way you want since it is weakly typed. Since FastAPI uses Pydantic, you have access to BaseSettings which is a far superior product for configuration management, especially with environment variables 10. The app and API folder structure is an anti-pattern that doesn't scale past projects the size of a tutorial on how to laern FastAPI. I strongly recommend changing this to move of a vertical slice or folder per feature layout such as is used in https://github.com/Netflix/dispatch/tree/master/src/dispatch 11. FastAPI routes don't need `response_model=` anymore in favor of adding the return type to your function signature such as `async def create_thing() -> Thing:` 12. The uuid_masker function is interesting, but exposing UUIDs in logs usually doesn't pose a security risk and only makes debugging more difficult 13. You have some type lies in your code that could burn you such as https://github.com/alexk1919/fastapi-motor-mongo-template/blob/main/app/db/db.py#L10 . This pattern for the global DB handle has also burned me in the past and I had to go back and refactor out all of them to instead to purely use the FastAPI dependency injection chaining 14. datetime.datetime isn't safe to use as it is in sample_resource_common.py, you need a timezone aware implementation 15. Your test suite is stateful, require a running database, leak a lot of implementation details of the underlying models. This is every anti-pattern in the book for unit testing. And if you are going to do integration tests, then you would be better off with tooling designed for it such as playwright. Again, these are all just my opinions and may alone not be enough to warrant changing anything you have here.
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Python projects with best practices on Github?
Two random examples I found from 30 seconds of googling: Here’s Netflix using it in their crisis management tool, and here’s Uber using it in their deep learning framework.
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Open Source Projects based on FastAPI
netflix dispatch
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As a long time programmer what are some important coding styles ?
As someone who uses FastAPI, I find the https://github.com/Netflix/dispatch code to be a great reference.
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CEO faces backlash after quoting Martin Luther King Jr. in announcing layoffs
Besides that paying $21 to $41 per user for this nuts. Set up a VPS with Dispatch (opensourced by Netflix) and save your company some money.
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Total beginner, use FastAPI?
For production ready code examples I use: https://github.com/Netflix/dispatch
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
fastapi-best-practices - FastAPI Best Practices and Conventions we used at our startup
langchain - 🦜🔗 Build context-aware reasoning applications
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
fastapi-router-controller - A FastAPI utility to allow Controller Class usage
BentoML - The easiest way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Multi-model Inference Graph/Pipelines, LLM/RAG apps, and more!
opal - Fork of https://github.com/permitio/opal
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
databases - Async database support for Python. 🗄
jina - ☁️ Build multimodal AI applications with cloud-native stack