Popular GitHub Repositories for Swagger

?Vite+Vue3+Gin拥有AI辅助的基础开发平台，支持TS和JS混用。它集成了JWT鉴权、权限管理、动态路由、显隐可控组件、分页封装、多点登录拦截、资源权限、上传下载、代码生成器、表单生成器和可配置的导入导出等开发必备功能。

NestJS Helper + AI Chatbot Development

? Helix is a private GenAI stack for building AI applications with declarative pipelines, knowledge (RAG), API bindings, and first-class testing.

TypeScript Agentic AI Library specialized for LLM Function Calling enhanced by compiler skills.

An efficient API documentation management tool that fully adheres to the OpenAPI specification and incorporates advanced LLM technology. This tool can automate the generation of API documentation, data models, and test cases, greatly enhancing development efficiency and documentation quality.

Gopher Signal uses smart technology to quickly summarize important points from HackerNews.com articles. https://gophersignal.com

This is an OpenAI Whisper automatic speech recognition microservice

Generate Things from API Descriptions

Thesis project: topic categorization and sentiment analysis on twitter with Apache Spark

Data Engineering: Chapter 5 aws chapter for pragmatic ai. Creates an "real world" Data Engineering API using Flask,Click, Pandas and Swagger docs

In this project we use Microsoft Azure to configure a cloud based Machine Learning model.

This is an e-bookstore with an integrated Rasa chatbot and NLU pipeline built with SpaCy

This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.

? Harness AI advanced capabilities in a Pythonic way.

This is an Express service that provides authorization functionality and includes gen-AI features using RAG and Redis.

provides accurate and scalable national currency forecasts by integrating advanced techniques, ensuring reliable predictions for informed decisions.

Microsoft Azure is used to configure a cloud-based machine learning production model, deploy it, and consume it. Also, a Machine Learning Pipeline has been created, published, and consumed using Python SDK.

Testing the Ollama AI on Spring Boot, with Kotlin and Swagger configured.

Cloud based models built using Azure AutoML and Logistic Regression with its hyperparameters tuned using HyperDrive. The best model was deployed using ACI with Swagger Documentation.

Using Azure to configure a cloud-based machine learning production model, deploy it, and consume it.