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Setup Your Dev Environment

We setup this repository and course with a development container that has a Universal runtime that can support Python3, .NET, Node.js and Java development. The related configuration is defined in the devcontainer.json file located in the .devcontainer/ folder at the root of this repository.

To activate the dev container, launch it in GitHub Codespaces (for a cloud-hosted runtime) or in Docker Desktop (for a local device-hosted runtime). Read this documentation for more details on how dev containers work within VS Code.

Tip

We recommend using GitHub Codespaces for a quick start with minimal effort. It provides a generous free usage quota for personal accounts. Configure timeouts to stop or delete inactive codespaces to maximize your quota usage.

1. Executing Assignments

Each lesson will have optional assignments that may be provided in one or more programming languages including: Python, .NET/C#, Java and JavaScript/TypeScript. This section provides general guidance related to executing those assignments.

1.1 Python Assignments

Python assignments are provided either as applications (.py files) or Jupyter notebooks (.ipynb files).

  • To run the notebook, open it in Visual Studio Code then click Select Kernel (at top right) and select the default Python 3 option shown. You can now Run All to execute the notebook.
  • To run Python applications from command-line, follow assignment-specific instructions to ensure you select the right files and provide required arguments

2. Configuring Providers

Assignments may also be setup to work against one or more Large Language Model (LLM) deployments through a supported service provider like OpenAI, Azure or Hugging Face. These provide a hosted endpoint (API) that we can access programmatically with the right credentials (API key or token). In this course, we discuss these providers:

  • OpenAI with diverse models including the core GPT series.
  • Azure OpenAI for OpenAI models with enterprise readiness in focus
  • Hugging Face for open-source models and inference server

You will need to use your own accounts for these exercises. Assignments are optional so you can choose to setup one, all - or none - of the providers based on your interests. Some guidance for signup:

Signup Cost API Key Playground Comments
OpenAI Pricing Project-based No-Code, Web Multiple Models Available
Azure Pricing SDK Quickstart Studio Quickstart Must Apply Ahead For Access
Hugging Face Pricing Access Tokens Hugging Chat Hugging Chat has limited models

Follow the directions below to configure this repository for use with different providers. Assignments that require a specific provider will contain one of these tags in their filename:

  • aoai - requires Azure OpenAI endpoint, key
  • oai - requires OpenAI endpoint, key
  • hf - requires Hugging Face token

You can configure one, none, or all providers. Related assignments will simply error out on missing credentials.

2.1. Create .env file

We assume that you have already read the guidance above and signed up with the relevant provider, and obtained the required authentication credentials (API_KEY or token). In the case of Azure OpenAI, we assume you also have a valid deployment of an Azure OpenAI service (endpoint) with at least one GPT model deployed for chat completion.

The next step is to configure your local environment variables as follows:

  1. Look in the root folder for a .env.copy file that should have contents like this:

    # OpenAI Provider
OPENAI_API_KEY='<add your OpenAI API key here>'

## Azure OpenAI
AZURE_OPENAI_API_VERSION='2024-02-01' # Default is set!
AZURE_OPENAI_API_KEY='<add your AOAI key here>'
AZURE_OPENAI_ENDPOINT='<add your AOIA service endpoint here>'
AZURE_OPENAI_DEPLOYMENT='<add your chat completion model name here>' 
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT='<add your embeddings model name here>'

## Hugging Face
HUGGING_FACE_API_KEY='<add your HuggingFace API or token here>'

  2. Copy that file to .env using the command below. This file is gitignore-d, keeping secrets safe.

    cp .env.copy .env

  3. Fill in the values (replace placeholders on right side of =) as described in the next section.

  4. (Option) If you use GitHub Codespaces, you have the option to save environment variables as Codespaces secrets associated with this repository. In that case, you won't need to setup a local .env file. However, note that this option works only if you use GitHub Codespaces. You will still need to setup the .env file if you use Docker Desktop instead.

2.2. Populate .env file

Let's take a quick look at the variable names to understand what they represent:

Variable Description
HUGGING_FACE_API_KEY This is the user access token you setup in your profile
OPENAI_API_KEY This is the authorization key for using the service for non-Azure OpenAI endpoints
AZURE_OPENAI_KEY This is the authorization key for using that service
AZURE_OPENAI_ENDPOINT This is the deployed endpoint for an Azure OpenAI resource
AZURE_OPENAI_DEPLOYMENT This is the text generation model deployment endpoint
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT This is the text embeddings model deployment endpoint

Note: The last two Azure OpenAI variables reflect a default model for chat completion (text generation) and vector search (embeddings) respectively. Instructions for setting them will be defined in relevant assignments.

2.3 Configure Azure: From Portal

The Azure OpenAI endpoint and key values will be found in the Azure Portal so let's start there.

  1. Go to the Azure Portal
  2. Click the Keys and Endpoint option in the sidebar (menu at left).
  3. Click Show Keys - you should see the following: KEY 1, KEY 2 and Endpoint.
  4. Use the KEY 1 value for AZURE_OPENAI_KEY
  5. Use the Endpoint value for AZURE_OPENAI_ENDPOINT

Next, we need the endpoints for the specific models we've deployed.

  1. Click the Model deployments option in the sidebar (left menu) for Azure OpenAI resource.
  2. In the destination page, click Manage Deployments

This will take you to the Azure OpenAI Studio website, where we'll find the other values as described below.

2.4 Configure Azure: From Studio

  1. Navigate to Azure OpenAI Studio from your resource as described above.
  2. Click the Deployments tab (sidebar, left) to view currently deployed models.
  3. If your desired model is not deployed, use Create new deployment to deploy it.
  4. You will need a text-generation model - we recommend: gpt-35-turbo
  5. You will need a text-embedding model - we recommend text-embedding-ada-002

Now update the environment variables to reflect the Deployment name used. This will typically be the same as the model name unless you changed it explcitly. So, as an example, you might have:

AZURE_OPENAI_DEPLOYMENT='gpt-35-turbo'
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT='text-embedding-ada-002'

Don't forget to save the .env file when done. You can now exit the file and return to the instructions for running the notebook.

2.5 Configure OpenAI: From Profile

Your OpenAI API key can be found in your OpenAI account. If you don't have one, you can sign up for an account and create an API key. Once you have the key, you can use it to populate the OPENAI_API_KEY variable in the .env file.

2.6 Configure Hugging Face: From Profile

Your Hugging Face token can be found in your profile under Access Tokens. Don't post or share these publicly. Instead, create a new token for this project usage and copy that into the .env file under the HUGGING_FACE_API_KEY variable. Note: This is technically not an API key but is used for authentication so we are keeping that naming convention for consistency.