full-stack-fastapi-template
haystack
full-stack-fastapi-template | haystack | |
---|---|---|
28 | 55 | |
23,239 | 13,711 | |
- | 3.1% | |
9.5 | 9.9 | |
3 days ago | 7 days ago | |
TypeScript | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
full-stack-fastapi-template
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Building a Secure API with FastAPI, PostgreSQL, and Hanko Authentication
This project is a modification of the authentication flow of the awesome repository made by tiangolo at full-stack-fastapi-postgresql
- Do you know any quality FastAPI starter projects?
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What is a sensible way to go about designing an authentication microservice?
FastAPI with a PostgreSQL database: https://github.com/tiangolo/full-stack-fastapi-postgresql/tree/master
- Faster way to kickstart and develop backend REST apis?
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Is a Framework like Django possible in Rust
Ha! I do write SQL since that's where I cut my teeth many years ago. But I mostly use stored procedures where possible. I prefer not to use ORMs - sorry I don't find that side work... I am not saying Django's was bad or inferior, just do not prefer it. For FastAPI - maybe you missed the various repos with everything you mentioned was missing (there are great ones directly from the maintainer as well as others). No more glue than what you find in all the modules in a large Django project, just maybe in different forms and flavors. Besides, we're here to talk about Rust, making me wonder why we're debating two Python projects. Yes, I fell in love with Django, the romance faded in 2018, and I moved on. Feel free to enjoy using it - I'm not trying to sway you away from it!
- Is there any open source project that uses FasAPI?
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How to build a scalable project file structure for a beginner.
I've just recently switched to a structure that follows Netflix's Dispatch application after starting with https://github.com/tiangolo/full-stack-fastapi-postgresql and it feels way better and organized.
- ORM for FastAPI+PostgreSQL, Tortoise or Sqlalchemy? what would you choose and why?
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Creating a webpage for data entry
Honestly your easiest option for data gathering would be to create google spreadsheets/forms and give each municipality access. For a custom data entry platform I suggest looking for templates like this one and learning how to add custom logic to the boilerplate: https://github.com/tiangolo/full-stack-fastapi-postgresql
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FastAPI Best Practices
I would encourage you to take a look at this repo: https://github.com/tiangolo/full-stack-fastapi-postgresql This is a boilerplate of an application made with fastapi, prepared by the creator of the fastapi himself. You can even set it up yourself locally and have a look how it’s organised. I know it has a lot of different services included, but I find the fastapi part itself to be well thought. Inside the api directory you can notice another folder named api_v1, so you can have multiple versions of your API routes when needed, with the general code in other places that is more generic and can be reused in all your different API versions. The schemas are separated from the models and models itself have different classes depending on what you would actually like to do with the data. The migrations are managed with alembic based on schemas rather than models itself. The settings are a python class that implicitly reads the .env file in your project’s directory. And many, many other interesting patterns to explore. Too much to write in one comment to be honest.
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-starter - A FastAPI based low code starter/boilerplate: SQLAlchemy 2.0 (async), Postgres, React-Admin, pytest and cypress
langchain - 🦜🔗 Build context-aware reasoning applications
fastapi-react - 🚀 Cookiecutter Template for FastAPI + React Projects. Using PostgreSQL, SQLAlchemy, and Docker
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
uvicorn-gunicorn-fastapi-docker - Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python with performance auto-tuning.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
cookiecutter-djangopackage - A cookiecutter template for creating reusable Django packages quickly.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
fastapi-users - Ready-to-use and customizable users management for FastAPI
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
docker-celery-flower - Minimum docker/fastapi/celery/flower setup
jina - ☁️ Build multimodal AI applications with cloud-native stack