FULL STACK DEVELOPER

A React + AWS Serverless full stack implementation of the example applications found in the official OpenAI API documentation. See this system architectural diagram for details. This is an instructional tool for the YouTube channel "Full Stack With Lawrence" and for University of British Columbia course, "Artificial Intelligence Cloud Technology Implementation".

Powered by Cookiecutter <https://github.com/cookiecutter/cookiecutter>, Cookiecutter Open edX Devops Tools is a community maintained project template for jumpstarting production-ready, Kubernetes <https://kubernetes.io/>-based installations of the Open edX <https://openedx.org/>_ online learning management system running on AWS <https://aws.amazon.com/>_ cloud infrastructure that is built and managed with fully parameterized Terraform <https://www.terraform.io/>_ and Github Actions <https://docs.github.com/en/actions>_ automation scripts. The template offers a number of generation options, we invite you to check the dedicated docs <https://github.com/cookiecutter-openedx/cookiecutter-openedx-devops/tree/main/doc>_ pages to learn more about each of them.

Smarter is used as an instructional tool at University of British Columbia for teaching AI. The Smarter Project is an open source, cloud-native platform and developer framework for building sophisticated AI applications without writing a single line of Python. An application that searches the web, queries your databases, calls your APIs, uses the tools of any MCP server, and defends itself against prompt injection and data leaks is described in one short YAML file, and deployed with one command:

smarter apply -f my-ai-application.yaml

Demonstrate basic usage of Azure Machine Learning's Automated ML service. Implements the following:

• AzureAIMLWorkspace: Azure AI ML Studio workspace helper class. generates an authenticated instance of a workspace.
• AzureAIMLAssetsDataset: Azure AI ML Studio data set. Provides helpers for managing ML Studio data sets, and for porting to/from kaggle data sets and local csv and Excel files.
• AzureAIMLStudioComputeCluster: Azure AI ML Studio Compute - compute cluster object with helpers for instantitation.
• AzureAIMLStudioAssetsBatchEndpoint: Azure AI ML Studio Assets - batch end point. Providers helpers for managing end points.
• AzureAIMLStudioAuthoringAutomatedML: Azure AI ML Studio Authoring - Automated ML. Helper class for managing life cycle of 'automated ml' jobs. There is also an example production deployment of a model created using AutomatedML. See important deployment details, here. Note the following:

1. Model training is a computationally intensive task and it is not free. You'll need a paid Azure Subscription. Expect to spend in the neighborhood of $0.10/per hour ($USD) when training models.
2. This repo is currently based on Python3.9 even though this is several versions behind the latest stable version of Python. DO NOT ARBITRARILY UPGRADE TO LATER VERSIONS OF PYTHON. It will break. This repo is actively maintained. We monitor this. You have been warned.

An Open edX plugin showcasing a curated collection of code samples for extending and modifying the stock functionality of an Open edX installation using the built-in plugin architecture. This plugin can be installed in any Open edX installation version Juniper or later, independent of the installation method. This plugin currently runs in native builds, tutor installations, and Cookiecutter-openedx Kubernetes, amongst other proprietary installation methodologies. Open edX official documentation recommends that you leverage its plugin architecture rather than fork its code repositories. This project provides more than two dozen code examples to help you accomplish this. Usage:

pip install openedx-plugin-example

Technical features and coding techniques that are showcased in this repo include:

• Iterate and introspect structured course content
• Custom oauth backend that seamlessly integrates with Open edX third party authentication
• Custom url to auto-enroll students in a course
• Automatically detect and adapt to the browser OS locale setting
• Usage examples of Open edX's extensive library of Python decorators
• Open edX Django configuration settings
• Open edX Django urls
• Open edX Django logging
• Open edX Django signals
• Open edX Django RestFramework custom api
• Django app setup
• Django models
• Django and Mako templating
• Django static assets
• Django Admin
• Django middleware
• Django manage.py custom commands
• Django Waffle flags
• Python environment variables
• How to bundle multiple plugins in a single pip package
• How to redirect Open edX urls in lms and cms to endpoints created in this plugin
• How to automatically initialize Django model data during app startup
• Adding unit tests to plugin code
• Semantic version control
• Pre-commit with linting by flake8 and black, both of which are configured to match the opinionated styling that you'll find in Open edX repositories
• pip configuration, requirements, constraints, setup.py, pyproject.toml
• Publishing to PyPi

🤖 Retrieval Augmented Generation and Hybrid Search 🤖 A Hybrid Search and Augmented Generation prompting solution using Python OpenAI API Embeddings persisted to a Pinecone vector database index and managed by LangChain. Demonstrates the following:

• System Prompting. How do use the system prompt to modify LLM text completion behavior.
• Templates. How to create templates in order keep your prompts DRY.
• LangChain. How to setup a project using LangChain as an alternative to vendor specific LLM PyPi packages.
• PDF Loader. a command-line pdf loader program that extracts text, vectorizes, and loads into a Pinecone dot product vector database that is dimensioned to match OpenAI embeddings.
• Pinecone. How to create, load, and query a Pinecone vector database.
• Retrieval Augmented Generation (RAG). A chatGPT prompt based on a hybrid search retriever that locates relevant documents from the vector database and includes these in OpenAI prompts. Secondarily, I also use this repo for demonstrating how to setup Pydantic to manage your project settings and how to safely work with sensitive credentials data inside your project.

This is the source code for my Blog Article and FullStackWithLawrence Youtube Video. Terraform scaffolding to create a production-ready Kubernetes cluster running inside its own VPC. Sets up spot pricing for EC2 instances, and installs and configures commonly needed packages including:

• nginx-ingress-controller
• cert-manager
• metrics-server
• prometheus
• vertical pod autoscaler (vpa) Consists of the following source code:

License: MIT This is a fully functional reference project that demonstrates a hybrid Django + ReactJS setup. After building this environment locally you can view this example page with ReactJS running inside of Django. The React app in this project was scaffolded with Create React App. It is completely scalable and is wrapped inside of a Django app located in django_with_react/djangoapps/frontend. You build the React app normally, using npm run build or yarn run build. This single view within the frontend app enables the Django templating system to automatically find and seamlessly integrate the React build's optimized js and css bundles. You have the option of using both Django urls.py as well as React Router. Additionally, you can pass data to the React app using both a traditional REST api or using the Django template context. The React build is served entirely by Django's templating system and native static asset functionality. There are some anciliary objectives as well that bear mentioning. The local development stack consists of the following fundamental building blocks:

1. Python Virtual Environment This project uses Python3.9 with what I consider to be the ideal set of PyPi packages for a garden variety Django project as of this writing.
2. Django project that was scaffolded with cookiecutter-django and includes a fully configured implementation of Django REST Framework that is ideally suited for serving a REST api for the ReactJS frontend.
3. The ReactJS app.
4. Sphinx documentation server that was also scaffolded with CookieCutter
5. Docker which provides all backend services (see the list below, in this section)
6. HTTPS. This project also includes documentation and an example of how to implement https in your local development environment, which is helpful for things like testing oauth.
7. Opinionated Code Styling Enforcement. This project includes an example implementation of pre-commit with black and flake8 code formatters. The Docker backend services consist of the following:

• Django WSGI app server:
• http://localhost:8000
• https://django_with_react.stepwisemath.local/
• Nginx server
• Postgres database server: port 5432
• Sphinx Documentation server: http://localhost:7000
• Flower server: http://localhost:5555
• Celery worker
• CeleryBeat server

A Python "Hello World" application that demonstrates the OpenAI API and usage of Docker Compose to containerize your project. This is a command line utility that returns the value "Hello World" in the written language of your choice. Python code is located here

A facial recognition microservice built with AWS Rekognition, DynamoDB, S3, IAM, CloudWatch, API Gateway and Lambda. See this json dump for configuration options.