[Hack Update] 066-AI fundamentals 0126

066-OpenAIFundamentals/.wordlist.txt

@@ -42,3 +42,4 @@ multimodal
 Agentic
 MCP
 Leaderboards
+Gally

066-OpenAIFundamentals/Coach/Solution-00.md

@@ -6,17 +6,17 @@
 
 Challenge-00 is all about helping the student set up the prerequisites for this hack. This includes necessary installations, environment options, and other libraries needed. 
 
-They will be creating all needed Azure resources through the Azure AI Foundry. Once they create a hub, they will have an AOAI, AI Search, Azure Document Intelligence, and Azure Storage Account deployed. They will get the credentials for AOAI, AI Search, and Document Intelligence through the AI Foundry. For Azure Storage, they will need to navigate to the Azure Portal.
+They will be creating all needed Azure resources through Microsoft Foundry. If they use the deployment script, all of the required Azure resources will be created for them. Otherwise, they will need to create the required resources in the Azure Portal and the Foundry portal. Once they create a project, they will have an AOAI, AI Search, Azure Document Intelligence, and Azure Storage Account deployed. They will get the credentials for AOAI, AI Search, and Document Intelligence through  Microsoft Foundry. For Azure Storage, they will need to navigate to the Azure Portal if they are doing it manually.
 
 **NOTE:** Target | Endpoint | Base can be used interchangeably. 
 
-**NOTE:** For all of the challenges, if a student changes any variables in their .env file, they will need to re-run those cells that load the .env file and set the variables in Python. They can check the values of their Jupyter variables by clicking the Jupyter tab in Visual Studio Code. 
+**NOTE:** For all of the challenges, if a student changes any variables in their .env file, they will need to re-run those cells that load the .env file and set the variables in Python. They can check the values of their Jupyter variables by clicking the Jupyter tab in Visual Studio Code. If Visual Studio Code hangs during cell execution, they will need to restart the kernel which means they will need to re-run any cells that load the .env file and set the variables in Python again. 
 
 ### Model Deployment
 
 Challenge 0 has the students deploy multiple models that will be used for the following:
 - One model will be used by the Jupyter notebooks for Challenges 1, 3, 4, & 5. The notebooks expect to find the name of this model in the `CHAT_MODEL_NAME` value of the `.env` file.
-- A second model will be used in Challenge 2 for model comparison with the Leaderboards in Azure AI Foundry.
+- A second model will be used in Challenge 2 for model comparison with the Leaderboards in Microsoft Foundry.
 - A text embedding model will be used in the Jupyter notebooks for Challenges 3 & 4. The notebooks expect to find the name of this model in the `EMBEDDING_MODEL_NAME` value of the `.env` file.
 
 Students can use different/newer models than the ones listed in the student guide when this hack was published. Most models should work fine. Just ensure the values set in the `.env` file to match the names of the models deployed.

066-OpenAIFundamentals/Coach/Solution-02.md

@@ -28,7 +28,7 @@ Some possible model choices include:
 1. GPT-4o and GPT-4o Mini
 2. GPT-4o and GPT-5 Mini
 
-For Model Router, students will be deploying an instance of model router in AI Foundry and prompting it with different questions in the chat playground to see how the queries are automatically sent to the different LLMs in depending on their complexity.
+For Model Router, students will be deploying an instance of model router in Microsoft Foundry and prompting it with different questions in the chat playground to see how the queries are automatically sent to the different LLMs in depending on their complexity.
 
 The router may choose the model **`gpt-5-nano-2025-08-07`** consistently for the given prompts. This model is known for its ultra low latency and fast responses for simple tasks. Encourage students to try longer, multi-step reasoning prompts to trigger a different model.
 

066-OpenAIFundamentals/Coach/Solution-04.md

@@ -16,4 +16,4 @@ Known Bugs
 - **CH4 EXTRACTING FILES: operation returned an invalid status `inefficient storage`** \
   **Solution:** If some of the files are extracted, students can continue working with those and ignore the error message.
 - Rate Limits \
-  **Solution:** Have students go into their model deployments in the AI Studio and adjust the TPM allocation for that model to a higher value using the toggle.
+  **Solution:** Have students go into their model deployments in the Microsoft Foundry and adjust the TPM allocation for that model to a higher value using the toggle.

066-OpenAIFundamentals/Student/Challenge-00.md

@@ -12,7 +12,7 @@ In this challenge, you will set up the necessary prerequisites and environment t
 - [Setup Jupyter Notebook Environment](#setup-jupyter-notebook-environment)
   - [GitHub Codespaces](#setup-github-codespace)
   - [Local Workstation](#setup-local-workstation)
-- [Deploy Azure AI Foundry Resources](#deploy-azure-ai-foundry-resources)
+- [Deploy Microsoft Foundry Resources](#deploy-azure-ai-foundry-resources)
 
 ### Azure Subscription
 
@@ -60,7 +60,7 @@ Your Codespace environment should load in a new browser tab. It will take approx
 
 - When the codespace completes loading, you should find an instance of Visual Studio Code running in your browser with the files needed for this hackathon.
 
-You are ready to run the Jupyter Notebook files, hooray! Skip to section: [Setup Azure AI Foundry Project and Hub](#Setup-Azure-AI-Foundry-Project-and-Hub)
+You are ready to run the Jupyter Notebook files, hooray! Skip to section: [Setup Microsoft Foundry Project](#Setup-Microsoft-Foundry-Project)
 
 **NOTE:** If you close your Codespace window, or need to return to it later, you can go to [GitHub Codespaces](https://github.com/codespaces) and you should find your existing Codespaces listed with a link to re-launch it.
 
@@ -134,20 +134,20 @@ Once you have an Azure Machine Learning Studio Workspace set up, you can upload
 </details>
 <br/>
 
-### Deploy Azure AI Foundry Resources
+### Deploy Microsoft Foundry Resources
 
 Now that you have a Jupyter notebook environment setup, you need to:
-- Deploy AI models and resources in Azure AI Foundry.  
+- Deploy AI models and resources in Microsoft Foundry.  
 - Setup Jupyter Notebooks Configuration File
 
-We have provided an automation script that will perform these tasks for you. However, you may wish to complete these tasks manually to become more familiar with Azure AI Foundry.
+We have provided an automation script that will perform these tasks for you. However, you may wish to complete these tasks manually to become more familiar with Microsoft Foundry.
 
-- [Automate Azure AI Foundry Deployment](#automate-azure-ai-foundry-deployment)
-- [Manual Azure AI Foundry Deployment](#manual-azure-ai-foundry-deployment)
+- [Automate Microsoft Foundry Deployment](#automate-microsoft-foundry-deployment)
+- [Manual Microsoft Foundry Deployment](#manual-microsoft-foundry-deployment)
 
 **NOTE:** If you are limited on time, we recommend using the automation script option.
 
-#### Automate Azure AI Foundry Deployment
+#### Automate Microsoft Foundry Deployment
 
 We have provided a deployment script and a set of Bicep templates which will deploy and configure the Azure AI resources which you will use for this hackathon. You can find these files in the `/infra` folder of your Codespace or the student `Resources.zip` package.
 
@@ -174,42 +174,38 @@ cd infra
 chmod +x deploy.sh
 ./deploy.sh  
 ```
-**NOTE:** By default, the script will create an Azure resource group for you named `rg-ai-foundry-secure`. You may optionally specify a `resourceGroupName` and/or `location` parameters if you need the resources deployed to a specific resource group or region.  The default location is "`eastus`" if you don't specify one. 
+**NOTE:** By default, the script will create an Azure resource group for you named `rg-microsoft-foundry-secure`. You may optionally specify a `resourceGroupName` and/or `location` parameters if you need the resources deployed to a specific resource group or region.  The default location is "`eastus`" if you don't specify one. 
 
 ```
 ./deploy.sh --resourceGroupName "[resource-group-name]" --location "[location]"
 ```
 
-#### Manual Azure AI Foundry Deployment
+#### Manual Microsoft Foundry Deployment
 
-**NOTE:** You can skip this section if you chose to automate the deployment.
+**NOTE:** You can skip this section if you chose to automate the deployment. It is strongly recommended that you use the automated approach. If you'd like to understand more what the automated approach is doing, you can use GitHub Copilot to explain what the deployment script and associated Bicep files are doing. 
 
-If you want to deploy the Azure AI Foundry resources, expand the section below and follow instructions there.
+If you want to deploy the Microsoft Foundry resources, expand the section below and follow instructions there.
 
 <details markdown=1>
 <summary markdown="span"><strong>Click to expand/collapse Manual Deployment Instructions</strong></summary>
 
-#### Setup Azure AI Foundry Project and Hub
+#### Setup Azure Microsoft Foundry Project 
 
-Navigate to [AI Foundry](https://ai.azure.com) to create your Azure AI project and the needed resources. A project is used to organize your work and allows you to collaborate with others. A hub provides the hosting environment for your projects. An Azure AI hub can be used across multiple projects.
+Navigate to [Microsoft Foundry](https://ai.azure.com) to create your Microsoft Foundry project. 
+
+- Click on the **+ Create New** button.
+- Choose Microsoft Foundry resource for the resource type. Click the **Next** button
+  - Fill out a name for your project. **Note:** You should not need to specify Advanced Options unless you need or want to change the region because of capacity constraints. Click the **Create** button
+- From the Azure portal (or you can use an Infrastructure as Code approach if you prefer using Bicep/Terraform/ARM/CLI)
+  - Create an Azure AI Search service
+  - Specify a service name for your Azure AI Search. You can use the same resource group and location as the Microsoft Foundry resource. **Note:** Make sure you set the Pricing Tier to Standard (Basic/Free is not supported)
 
-- Click on the **+ Create Project** button.
-- Give your project a name and click **Create a new hub**.
-  - Fill out a name for your hub. 
-  - Click the **Next** button
-  - Click the **Customize** button
-  - Click **Create new AI Search**.
-  - Fill out a name for your Azure AI Search
-  - Click the **Next** button to finish setting up your Azure AI Search
-  - Click the **Next** button on the screen where it says **Create a hub for your projects**
-  - On the Review and Finish page, click the **Create** button
-- The hub will create an Azure Open AI, Azure Blob, and an AI Service resource for you once it is finished. Resources are different Azure services you will use within the challenges.
 
 #### Deploy Azure OpenAI Models
 
 Now we will deploy the needed large language models from Azure OpenAI. 
 
-- Navigate to the [AI Foundry](https://ai.azure.com) 
+- Navigate to the [Microsoft Foundry](https://ai.azure.com) 
 - On the left navigation bar, under My Assets, click on Models + endpoints. Click the Deploy Model button and select Deploy base model
 - Deploy the following 3 models in your Azure OpenAI resource. 
   - `gpt-4o`
@@ -226,25 +222,13 @@ You will find the `.env.sample` file in the root of the codespace. If you are wo
 
 - Rename the file from `.env.sample` to `.env`.
 - Add all the required Azure resource credentials in the `.env` file. This includes: Azure OpenAI, model deployments, AI Search, Azure Document Intelligence, and Azure Blob
-    - For **Azure OpenAI and Model Deployments**, you can find these credentials in Azure AI Foundry:
-      - Navigate to the [AI Foundry](https://ai.azure.com)
-      - Navigate to your project. In the lower left corner, click on the link to Management Center. It is also under Project details.
-      - Click on Connected resources under your project
-      - Click the name of your Azure OpenAI Service to see its details. Copy the Target URL and API Key for `OPENAI_API_BASE` and `OPEN_API_KEY`, respectively into the `.env` file
-      - From the **`Manage connect resources in this project`** screen, click the Name with the type **`AIServices`**. The AI Services deployment is a multi-service resource that allows you to access multiple Azure AI services like Document Intelligence with a single key and endpoint. Copy the Target URL and the API Key for `AZURE_DOC_INTELLIGENCE_ENDPOINT` and `AZURE_DOC_INTELLIGENCE_KEY`, respectively into the `.env` file
-      - In the [Azure Portal](portal.azure.com), navigate to the resource group you made when creating your hub within the AI Foundry.
-      - Locate your **AI Search** service that you created earlier
-      - From the **Overview**, copy the URL for `AZURE_AI_SEARCH_ENDPOINT` in the .env file
-      - Under **`Settings`** go to Keys, copy the admin key into `AZURE_AI_SEARCH_KEY` in the `.env` file      
-      - Model deployment names should be the same as the ones populated in the `.env.sample` file especially if you have deployed a different model due to quota issues.
-    - For **Azure Blob**, you can find these credentials in the [Azure Portal](portal.azure.com).
-      - In the Azure Portal, navigate to the resource group you made when creating your hub within the AI Foundry.
-      - Click on your **`Storage account`** resource
-      - Click on **`Security + networking`** and find **`Access keys`**. You should be able to see the **`Storage account name`**, **`key`**, and **`Connection string`**.
+    - For **Azure OpenAI and Model Deployments**, you can find these credentials in Azure Microsoft Foundry:
+      - Navigate to the [Microsoft Foundry](https://ai.azure.com)
+      - You will need the values for `OPENAI_API_BASE`, `AZURE_DOC_INTELLIGENCE_ENDPOINT`, `AZURE_AI_SEARCH_ENDPOINT`, `AZURE_AI_PROJECT_ENDPOINT`, and `AZURE_BLOB_STORAGE_ACCOUNT_NAME` to put in your `.env` file. Use your favorite search tool or Github Copilot to figure out where to retrieve these values either in the Foundry Portal, Azure Portal, or using the Azure CLI. 
 
   **TIP:** Learn more about using `.env` files [here](https://dev.to/edgar_montano/how-to-setup-env-in-python-4a83#:~:text=How%20to%20setup%20a%20.env%20file%201%201.To,file%20using%20the%20following%20format%3A%20...%20More%20items).
 
-**NOTE:** We have also provided a `.gitignore` file that should prevent you from accidentally committing your renamed `.env` file to a Git repo during this hack.
+**NOTE:** We have also provided a `.gitignore` file that should prevent you from accidentally committing your own `.env` file to a Git repo during this hack.
 
 **NOTE:** On MacOS, files that start with a `.` are hidden files and are not viewable in Finder when browsing the file system. They will be visible in both VS Code or GitHub Codespaces.
 
@@ -260,10 +244,10 @@ If using GitHub Codespaces:
 - Verify you have the following files & folders available in the Codespace:
     - `/data`
     - `/notebooks`
-    - `.env` <= Renamed from `.env.sample`
+    - `.env` <= Copied from `.env.sample`
     - `.gitignore`
     - `requirements.txt`
-- Verify that you have created the Project and Hub in your AI Foundry.
+- Verify that you have created the Project in Microsoft Foundry.
     - Verify that you have the following resources: Azure OpenAI, deployed the necessary models, AI Search, Document Intelligence, Azure Blob.
 
 If working on a local workstation: 
@@ -276,7 +260,7 @@ If working on a local workstation:
     - `.env` <= Renamed from `.env.sample`
     - `.gitignore`
     - `requirements.txt`
-- Verify that you have created the Project and Hub in your AI Foundry.
+- Verify that you have created the Project in your Microsoft Foundry.
     - Verify that you have the following resources: Azure OpenAI, deployed the necessary models, AI Search, Document Intelligence, Azure Blob.
 
 ## Learning Resources

066-OpenAIFundamentals/Student/Challenge-01.md

@@ -4,8 +4,8 @@
 
 ## Prerequisites
 
-* Ensure you have the needed resources from the previous challenge in [AI Foundry](https://ai.azure.com/)
-* Update the `.env.sample` file (and save as `.env`) with your respective resource credentials if you haven't already
+* Ensure you have the needed resources from the previous challenge in [Microsoft Foundry](https://ai.azure.com/)
+* Update the `.env.sample` file (and save as `.env`) with your respective resource credentials if you haven't already done so. 
 
 ## Introduction
 

066-OpenAIFundamentals/Student/Challenge-02-Old.md

@@ -17,7 +17,7 @@ Questions you should be able to answer by the end of this challenge:
 - What model would you select to perform complex problem solving？
 - What model would you select to generate new names?
 
-You will work in the Azure AI Foundry for this challenge. We recommend keeping the student guide and the Azure AI Foundry in two windows side by side as you work. This will also help to validate you have met the [success criteria](#success-criteria) below for this challenge.
+You will work in the Azure Microsoft Foundry for this challenge. We recommend keeping the student guide and the Azure Microsoft Foundry in two windows side by side as you work. This will also help to validate you have met the [success criteria](#success-criteria) below for this challenge.
 
 This challenge is divided into the following sections:
 
@@ -33,7 +33,7 @@ This challenge is divided into the following sections:
 Scenario: You are part of a research team working on getting information from biotech news articles. Your goal is to explore the Model Catalog and identify some suitable models for accurate question answering. There is no right or wrong answer here.
 
 #### Student Task 2.1
-- Go into the [Azure AI Foundry](https://ai.azure.com).
+- Go into the [Microsoft Foundry](https://ai.azure.com).
 - Navigate to the Model Catalog and explore different models using the correct filters. 
 - Identify which models can potentially improve the accuracy of the task at hand.
 
@@ -53,7 +53,7 @@ Scenario: You are part of a research team working on getting information from bi
 ### 2.4 Prompt Flow
 Scenario: You are a product manager at a multinational tech company, and your team is developing an advanced AI-powered virtual assistant to provide real-time customer support. The company is deciding between GPT-3.5 Turbo and GPT-4 to power the virtual assistant. Your task is to evaluate both models to determine which one best meets the company's needs for handling diverse customer inquiries efficiently and effectively.
 
-Navigate to the AI Foundry and click on your project. You should be able to see **Prompt flow** under Tools in the navigation bar. Create a new **standard flow** to solve the tasks below and compare the responses from different models. For each task, you will see the provided prompts that you can test against the deployed models.
+Navigate to the Microsoft Foundry and click on your project. You should be able to see **Prompt flow** under Tools in the navigation bar. Create a new **standard flow** to solve the tasks below and compare the responses from different models. For each task, you will see the provided prompts that you can test against the deployed models.
 
 **NOTE:** If you get this **User Error: This request is not authorized to perform this operation using this permission. Please grant workspace/registry read access to the source storage account.** when you create a new **standard** flow using the default name, then please append some random characters to the name or create a unique name for your flow.