bootcamp
fastapi
bootcamp | fastapi | |
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25 | 473 | |
1,682 | 72,383 | |
3.7% | - | |
9.4 | 9.8 | |
7 days ago | 5 days ago | |
HTML | Python | |
Apache License 2.0 | 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.
bootcamp
- FLaNK-AIM: 20 May 2024 Weekly
- FLaNK AI - 01 April 2024
- FLaNK Stack Weekly 22 January 2024
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Milvus Adventures Jan 5, 2023
Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.
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Build a search engine, not a vector DB
Partially agree.
Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.
As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.
The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.
No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.
With this idea I built a Retrieval-as-a-service solution, and here is its demo:
https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...
Curious to learn your thoughts.
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Vector Database in a Jupyter Notebook
Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.
For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp
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Beginner-ish resources for choosing a vector database?
Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search
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Semantic Similarity Search
I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query
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[D] Looking for open source projects to contribute
For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.
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I built an image similarity search system... Suggestions needed: what are some fun image datasets or scenarios I can use with this? :)
Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search
fastapi
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Documenting my pin collection with Segment Anything: Part 3
FastAPI: A web framework for building APIs (and web pages). It is used as the backbone of the application to handle web requests, routing, and server logic, and orchestrates the overall API structure. Although not used here, FastAPI provides robust features such as data validation, serialisation, and asynchronous request handling.
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Setting up Fast API in IIS and run APIs in Python
This article will show you how to setup an API written in Python using an amazing framework called FastAPI. This article is an introduction on how to use the framework, I blog later on more advanced use cases.
- Python FastAPI: Integrating OAuth2 Security with the Application's Own Authentication Process
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Setup REST-API service of AI by using Local LLMs with Ollama
FASTAPI
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Github Sponsor Sebastián Ramírez Python programmer
He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.
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Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.
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FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
google-research - Google Research
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
docarray - Represent, send, store and search multimodal data
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
Flask - The Python micro framework for building web applications.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.