├── .github ├── CONTRIBUTING.md ├── ISSUE_TEMPLATE.md └── PULL_REQUEST_TEMPLATE.md ├── BingSiteAuth.xml ├── Instructions ├── 01-Explore-ai-studio.md ├── 02-Explore-model-catalog.md ├── 02a-AI-foundry-sdk.md ├── 03-Use-prompt-flow-chat.md ├── 04-Use-own-data.md ├── 05-Finetune-model.md ├── 06-Explore-content-filters.md ├── 07-Evaluate-prompt-flow.md ├── archive │ ├── 03b-Use-prompt-flow-NER.md │ └── 08-Code-first-development.md └── media │ ├── add-resource.png │ ├── ai-foundry-chat-playground.png │ ├── ai-foundry-home.png │ ├── ai-foundry-manage-project.png │ ├── ai-foundry-management.png │ ├── ai-foundry-playground.png │ ├── ai-foundry-project-resources.png │ ├── ai-foundry-project.png │ ├── ai-quality-metrics.png │ ├── ai-services-identity.png │ ├── assign-role-access.png │ ├── azure-ai-deployment.png │ ├── azure-ai-overview.png │ ├── azure-ai-services.png │ ├── azure-portal-add-resources.png │ ├── azure-portal-resources.png │ ├── azure-portal.png │ ├── chat-flow.png │ ├── compare-models.png │ ├── comparison-chart.png │ ├── deployed-flow.png │ ├── diagram-dataset-evaluation.png │ ├── evaluation-data.png │ ├── example_app_output.png │ ├── get-started-lab.png │ ├── get-started-prompt-flow-use-case.gif │ ├── gpt4-benchmarks.png │ ├── gpt4-details.png │ ├── language-and-translator.png │ ├── manual-evaluation.png │ ├── manzanas.mp4 │ ├── model-catalog-search-gpt4.png │ ├── model-gpt-4-custom-filter.png │ ├── pf-flow-test-detail.png │ ├── pf-flow-test-row.png │ ├── prompt-flow-chat.png │ ├── prompt-flow.png │ ├── resource-provider-connected-resources.svg │ ├── storage-access-control.png │ ├── text-translation.png │ ├── try-translation.png │ └── web-app.png ├── LICENSE ├── _build.yml ├── _config.yml ├── data ├── brochures.zip ├── travel-finetune-hotel.jsonl ├── travel-finetune-validation.jsonl ├── travel-finetune.jsonl ├── travel-qa.jsonl ├── travel-questions.jsonl ├── travel_evaluation_data.csv └── travel_evaluation_data.jsonl ├── index.md ├── labfiles ├── chat-app │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ └── chat_app.csproj │ └── python │ │ ├── .env │ │ └── chat-app.py ├── multimodal │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ └── chat_app.csproj │ ├── manzanas.mp3 │ ├── orange.jpg │ └── python │ │ ├── .env │ │ └── chat-app.py └── rag-app │ ├── c-sharp │ ├── Program.cs │ ├── appsettings.json │ └── rag_app.csproj │ └── python │ ├── .env │ ├── rag-app.py │ └── requirements.txt ├── readme.md └── text-files └── sample-text.txt /.github/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to Microsoft Learning Repositories 2 | 3 | MCT contributions are a key part of keeping the lab and demo content current as the Azure platform changes. We want to make it as easy as possible for you to contribute changes to the lab files. Here are a few guidelines to keep in mind as you contribute changes. 4 | 5 | ## GitHub Use & Purpose 6 | 7 | Microsoft Learning is using GitHub to publish the lab steps and lab scripts for courses that cover cloud services like Azure. Using GitHub allows the course’s authors and MCTs to keep the lab content current with Azure platform changes. Using GitHub allows the MCTs to provide feedback and suggestions for lab changes, and then the course authors can update lab steps and scripts quickly and relatively easily. 8 | 9 | > When you prepare to teach these courses, you should ensure that you are using the latest lab steps and scripts by downloading the appropriate files from GitHub. GitHub should not be used to discuss technical content in the course, or how to prep. It should only be used to address changes in the labs. 10 | 11 | It is strongly recommended that MCTs and Partners access these materials and in turn, provide them separately to students. Pointing students directly to GitHub to access Lab steps as part of an ongoing class will require them to access yet another UI as part of the course, contributing to a confusing experience for the student. An explanation to the student regarding why they are receiving separate Lab instructions can highlight the nature of an always-changing cloud-based interface and platform. Microsoft Learning support for accessing files on GitHub and support for navigation of the GitHub site is limited to MCTs teaching this course only. 12 | 13 | > As an alternative to pointing students directly to the GitHub repository, you can point students to the GitHub Pages website to view the lab instructions. The URL for the GitHub Pages website can be found at the top of the repository. 14 | 15 | To address general comments about the course and demos, or how to prepare for a course delivery, please use the existing MCT forums. 16 | 17 | ## Additional Resources 18 | 19 | A user guide has been provided for MCTs who are new to GitHub. It provides steps for connecting to GitHub, downloading and printing course materials, updating the scripts that students use in labs, and explaining how you can help ensure that this course’s content remains current. 20 | 21 | 22 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | # Module: 00 2 | ## Lab/Demo: 00 3 | ### Task: 00 4 | #### Step: 00 5 | 6 | Description of issue 7 | 8 | Repro steps: 9 | 10 | 1. 11 | 1. 12 | 1. -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | # Module: 00 2 | ## Lab/Demo: 00 3 | 4 | Fixes # . 5 | 6 | Changes proposed in this pull request: 7 | 8 | - 9 | - 10 | - -------------------------------------------------------------------------------- /BingSiteAuth.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | B86358517DA673F783847297A3729C58 4 | -------------------------------------------------------------------------------- /Instructions/01-Explore-ai-studio.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Prepare for an AI development project' 4 | description: 'Learn how to organize cloud resources in hubs and projects so that developers are set up for success when building AI solutions.' 5 | --- 6 | 7 | # Prepare for an AI development project 8 | 9 | In this exercise, you use Azure AI Foundry portal to create a project, ready to build an AI solution. 10 | 11 | This exercise takes approximately **30** minutes. 12 | 13 | > **Note**: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 14 | 15 | ## Open Azure AI Foundry portal 16 | 17 | Let's start by signing into Azure AI Foundry portal. 18 | 19 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 20 | 21 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 22 | 23 | 1. Review the information on the home page. 24 | 25 | ## Create a project 26 | 27 | An Azure AI *project* provides a collaborative workspace for AI development. Let's start by choosing a model that we want to work with and creating a project to use it in. 28 | 29 | > **Note**: AI Foundry projects can be based on an *Azure AI Foundry* resource, which provides access to AI models (including Azure OpenAI), Azure AI services, and other resources for developing AI agents and chat solutions. Alternatively, projects can be based on *AI hub* resources; which include connections to Azure resources for secure storage, compute, and specialized tools. Azure AI Foundry based projects are great for developers who want to manage resources for AI agent or chat app development. AI hub based projects are more suitable for enterprise development teams working on complex AI solutions. 30 | 31 | 1. In the home page, in the **Explore models and capabilities** section, search for the `gpt-4o` model; which we'll use in our project. 32 | 1. In the search results, select the **gpt-4o** model to see its details, and then at the top of the page for the model, select **Use this model**. 33 | 1. When prompted to create a project, enter a valid name for your project and expand **Advanced options**. 34 | 1. Select **Customize** and specify the following settings for your hub: 35 | - **Azure AI Foundry resource**: *A valid name for your Azure AI Foundry resource* 36 | - **Subscription**: *Your Azure subscription* 37 | - **Resource group**: *Create or select a resource group* 38 | - **Region**: *Select any **AI Services supported location***\* 39 | 40 | > \* Some Azure AI resources are constrained by regional model quotas. In the event of a quota limit being exceeded later in the exercise, there's a possibility you may need to create another resource in a different region. 41 | 42 | 1. Select **Create** and wait for your project, including the gpt-4 model deployment you selected, to be created. 43 | 1. When your project is created, the chat playground will be opened automatically so you can test your model: 44 | 45 | ![Screenshot of a Azure AI Foundry project chat playground.](./media/ai-foundry-chat-playground.png) 46 | 47 | 1. In the navigation pane on the left, select **Overview** to see the main page for your project; which looks like this: 48 | 49 | > **Note**: If an *Insufficient permissions* error is displayed, use the **Fix me** button to resolve it. 50 | 51 | ![Screenshot of a Azure AI Foundry project overview page.](./media/ai-foundry-project.png) 52 | 53 | 1. At the bottom of the navigation pane on the left, select **Management center**. The management center is where you can configure settings at both the *resource* and *project* levels; which are both shown in the navigation pane. 54 | 55 | ![Screenshot of the Management center page in Azure AI Foundry portal.](./media/ai-foundry-management.png) 56 | 57 | The *resource* level relates to the **Azure AI Foundry** resource that was created to support your project. This resource includes connections to Azure AI Services and Azure AI Foundry models; and provides a centralplace to manage user access to AI development projects. 58 | 59 | The *project* level relates to your individual project, where you can add and manage project-specific resources. 60 | 61 | 1. In the navigation pane, in the section for your Azure AI Foundry resource, select the **Overview** page to view its details. 62 | 1. Select the link to the **Resource group** associated with the resource to open a new browser tab and navigate to the Azure portal. Sign in with your Azure credentials if prompted. 63 | 1. View the resource group in the Azure portal to see the Azure resources that have been created to support your Azure AI Foundry resource and your project. 64 | 65 | ![Screenshot of an Azure AI Foundry resource and project resources in the Azure portal.](./media/azure-portal-resources.png) 66 | 67 | Note that the resources have been created in the region you selected when creating the project. 68 | 69 | 1. Close the Azure portal tab and return to the Azure AI Foundry portal. 70 | 71 | ## Review project connections 72 | 73 | Your Azure AI Foundry project and the Azure AI Foundry resource to which it belongs include connections to resources that you can use in AI applications. 74 | 75 | 1. In the Management center page, in the navigation pane, under your project, select **Go to project**. 76 | 1. In the project **Overview** page, view the **Endpoints and keys** section; which contains endpoints and authorization keys that you can use in your application code to access: 77 | - The Azure AI Foundry project and any models deployed in it. 78 | - Azure OpenAI in Azure AI Foundry models. 79 | - Azure AI services 80 | 81 | ## Test a generative AI model 82 | 83 | Now that you know something about the configuration of your Azure AI Foundry project, you can return to the chat playground to explore the model you deployed. 84 | 85 | 1. In the navigation pane on the left for your project, select **Playgrounds** 86 | 1. Open the **Chat playground**, and ensure that your **gpt-4o** model deployment is selected in the **Deployment** section. 87 | 1. In the **Setup** pane, in the **Give the model instructions and context** box, enter the following instructions: 88 | 89 | ``` 90 | You are a history teacher who can answer questions about past events all around the world. 91 | ``` 92 | 93 | 1. Apply the changes to update the system message. 94 | 1. In the chat window, enter a query such as `What are the key events in the history of Scotland?` and view the response: 95 | 96 | ![Screenshot of the playground in Azure AI Foundry portal.](./media/ai-foundry-playground.png) 97 | 98 | ## Summary 99 | 100 | In this exercise, you've explored Azure AI Foundry, and seen how to create and manage projects and their related resources. 101 | 102 | ## Clean up 103 | 104 | If you've finished exploring Azure AI Foundry portal, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs. 105 | 106 | 1. In the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`, view the contents of the resource group where you deployed the resources used in this exercise. 107 | 1. On the toolbar, select **Delete resource group**. 108 | 1. Enter the resource group name and confirm that you want to delete it. 109 | -------------------------------------------------------------------------------- /Instructions/02-Explore-model-catalog.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Choose and deploy a language model' 4 | description: 'Generative AI applications are built on one or more language models. Learn how to find and select appropriate models for your generative AI project.' 5 | --- 6 | 7 | # Choose and deploy a language model 8 | 9 | The Azure AI Foundry model catalog serves as a central repository where you can explore and use a variety of models, facilitating the creation of your generative AI scenario. 10 | 11 | In this exercise, you'll explore the model catalog in Azure AI Foundry portal, and compare potential models for a generative AI application that assists in solving problems. 12 | 13 | This exercise will take approximately **25** minutes. 14 | 15 | > **Note**: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 16 | 17 | ## Explore models 18 | 19 | Let's start by signing into Azure AI Foundry portal and exploring some of the available models. 20 | 21 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 22 | 23 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 24 | 25 | 1. Review the information on the home page. 26 | 1. In the home page, in the **Explore models and capabilities** section, search for the `gpt-4o` model; which we'll use in our project. 27 | 1. In the search results, select the **gpt-4o** model to see its details. 28 | 1. Read the description and review the other information available on the **Details** tab. 29 | 30 | ![Screenshot of the gpt-4o model details page.](./media/gpt4-details.png) 31 | 32 | 1. On the **gpt-4o** page, view the **Benchmarks** tab to see how the model compares across some standard performance benchmarks with other models that are used in similar scenarios. 33 | 34 | ![Screenshot of the gpt-4o model benchmarks page.](./media/gpt4-benchmarks.png) 35 | 36 | 1. Use the back arrow (**←**) next to the **gpt-4o** page title to return to the model catalog. 37 | 1. Search for `Phi-3.5-mini-instruct` and view the details and benchmarks for the **Phi-3.5-mini-instruct** model. 38 | 39 | ## Compare models 40 | 41 | You've reviewed two different models, both of which could be used to implement a generative AI chat application. Now let's compare the metrics for these two models visually. 42 | 43 | 1. Use the back arrow (**←**) to return to the model catalog. 44 | 1. Select **Compare models**. A visual chart for model comparison is displayed with a selection of common models. 45 | 46 | ![Screenshot of the model comparison page.](./media/compare-models.png) 47 | 48 | 1. In the **Models to compare** pane, note that you can select popular tasks, such as *question answering* to automatically select commonly used models for specific tasks. 49 | 1. Use the **Clear all models** (🗑) icon to remove all of the pre-selected models. 50 | 1. Use the **+ Model to compare** button to add the **gpt-4o** model to the list. Then use the same button to add the **Phi-3.5-mini-instruct** model to the list. 51 | 1. Review the chart, which compares the models based on **Quality Index** (a standardized score indicating model quality) and **Cost**. You can see the specific values for a model by holding the mouse over the point that represents it in the chart. 52 | 53 | ![Screenshot of the model comparison chart for gpt-4o and Phi-3.5-mini-instruct.](./media/comparison-chart.png) 54 | 55 | 1. In the **X-axis** dropdown menu, under **Quality**, select the following metrics and observe each resulting chart before switching to the next: 56 | - Accuracy 57 | - Coherence 58 | - Fluency 59 | - Relevance 60 | 61 | Based on the benchmarks, the gpt-4o model looks like offering the best overall performance, but at a higher cost. 62 | 63 | 1. In the list of models to compare, select the **gpt-4o** model to re-open its benchmarks page. 64 | 1. In the page for the **gpt-4o** model page, select the **Overview** tab to view the model details. 65 | 66 | ## Create an Azure AI Foundry project 67 | 68 | To use a model, you need to create an Azure AI Foundry *project*. 69 | 70 | 1. At the top of the **gpt-4o** model overview page, select **Use this model**. 71 | 1. When prompted to create a project, enter a valid name for your project and expand **Advanced options**. 72 | 1. In the **Advanced options** section, specify the following settings for your hub: 73 | - **Azure AI Foundry resource**: *A valid name for your Azure AI Foundry resource* 74 | - **Subscription**: *Your Azure subscription* 75 | - **Resource group**: *Create or select a resource group* 76 | - **Region**: *Select any **AI Services supported location***\* 77 | 78 | > \* Some Azure AI resources are constrained by regional model quotas. In the event of a quota limit being exceeded later in the exercise, there's a possibility you may need to create another resource in a different region. 79 | 80 | 1. Select **Create** and wait for your project, including the gpt-4 model deployment you selected, to be created. 81 | 1. When your project is created, the chat playground will be opened automatically so you can test your model: 82 | 83 | ![Screenshot of a Azure AI Foundry project chat playground.](./media/ai-foundry-chat-playground.png) 84 | 85 | ## Chat with the *gpt-4o* model 86 | 87 | Now that you have a model deployment, you can use the playground to test it. 88 | 89 | 1. In the chat playground, in the **Setup** pane, ensure that your **gpt-4o** model is selected and in the **Give the model instructions and context** field, set the system prompt to `You are an AI assistant that helps solve problems.` 90 | 1. Select **Apply changes** to update the system prompt. 91 | 92 | 1. In the chat window, enter the following query 93 | 94 | ``` 95 | I have a fox, a chicken, and a bag of grain that I need to take over a river in a boat. I can only take one thing at a time. If I leave the chicken and the grain unattended, the chicken will eat the grain. If I leave the fox and the chicken unattended, the fox will eat the chicken. How can I get all three things across the river without anything being eaten? 96 | ``` 97 | 98 | 1. View the response. Then, enter the following follow-up query: 99 | 100 | ``` 101 | Explain your reasoning. 102 | ``` 103 | 104 | ## Deploy another model 105 | 106 | When you created your project, the **gpt-4o** model you selected was automatically deployed. Let's deploy the ***Phi-3.5-mini-instruct** model you also considered. 107 | 108 | 1. In the navigation bar on the left, in the **My assets** section, select **Models + endpoints**. 109 | 1. In the **Model deployments** tab, in the **+ Deploy model** drop-down list, select **Deploy base model**. Then search for `Phi-3.5-mini-instruct` and confirm you selection. 110 | 1. Agree to the model license. 111 | 1. Deploy a **Phi-3.5-mini-instruct** model with the following settings: 112 | - **Deployment name**: *A valid name for your model deployment* 113 | - **Deployment type**: Global Standard 114 | - **Deployment details**: *Use the default settings* 115 | 116 | 1. Wait for the deployment to complete. 117 | 118 | ## Chat with the *Phi-3.5* model 119 | 120 | Now let's chat with the new model in the playground. 121 | 122 | 1. In the navigation bar, select **Playgrounds**. Then select the **Chat playground**. 123 | 1. In the chat playground, in the **Setup** pane, ensure that your **Phi-3.5-mini-instruct** model is selected and in the **Give the model instructions and context** field, set the system prompt to `You are an AI assistant that helps solve problems.` (the same system prompt you used to test the gpt-4o model.) 124 | 1. Select **Apply changes** to update the system prompt. 125 | 1. Ensure that a new chat session is started before repeating the same prompts you previously used to test the gpt-4 model. 126 | 1. In the chat window, enter the following query 127 | 128 | ``` 129 | I have a fox, a chicken, and a bag of grain that I need to take over a river in a boat. I can only take one thing at a time. If I leave the chicken and the grain unattended, the chicken will eat the grain. If I leave the fox and the chicken unattended, the fox will eat the chicken. How can I get all three things across the river without anything being eaten? 130 | ``` 131 | 132 | 1. View the response. Then, enter the following follow-up query: 133 | 134 | ``` 135 | Explain your reasoning. 136 | ``` 137 | 138 | ## Perform a further comparison 139 | 140 | 1. Use the drop-down list in the **Setup** pane to switch between your models, testing both models with the following puzzle (the correct answer is 40!): 141 | 142 | ``` 143 | I have 53 socks in my drawer: 21 identical blue, 15 identical black and 17 identical red. The lights are out, and it is completely dark. How many socks must I take out to make 100 percent certain I have at least one pair of black socks? 144 | ``` 145 | 146 | ## Reflect on the models 147 | 148 | You've compared two models, which may vary in terms of both their ability to generate appropriate responses and in their cost. In any generative scenario, you need to find a model with the right balance of suitability for the task you need it to perform and the cost of using the model for the number of requests you expect it to have to handle. 149 | 150 | The details and benchmarks provided in the model catalog, along with the ability to visually compare models provides a useful starting point when identifying candidate models for a generative AI solution. You can then test candidate models with a variety of system and user prompts in the chat playground. 151 | 152 | ## Clean up 153 | 154 | If you've finished exploring Azure AI Foundry portal, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs. 155 | 156 | 1. Open the [Azure portal](https://portal.azure.com) and view the contents of the resource group where you deployed the resources used in this exercise. 157 | 1. On the toolbar, select **Delete resource group**. 158 | 1. Enter the resource group name and confirm that you want to delete it. 159 | -------------------------------------------------------------------------------- /Instructions/02a-AI-foundry-sdk.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Create a generative AI chat app' 4 | description: 'Learn how to use the Azure AI Foundry SDK to build an app that connects to your project and chats with a language model.' 5 | --- 6 | 7 | # Create a generative AI chat app 8 | 9 | In this exercise, you use the Azure AI Foundry SDK to create a simple chat app that connects to a project and chats with a language model. 10 | 11 | This exercise takes approximately **40** minutes. 12 | 13 | > **Note**: This exercise is based on pre-release SDKs, which may be subject to change. Where necessary, we've used specific versions of packages; which may not reflect the latest available versions. You may experience some unexpected behavior, warnings, or errors. 14 | 15 | ## Deploy a model in an Azure AI Foundry project 16 | 17 | Let's start by deploying a model in an Azure AI Foundry project. 18 | 19 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 20 | 21 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 22 | 23 | 1. In the home page, in the **Explore models and capabilities** section, search for the `gpt-4o` model; which we'll use in our project. 24 | 1. In the search results, select the **gpt-4o** model to see its details, and then at the top of the page for the model, select **Use this model**. 25 | 1. When prompted to create a project, enter a valid name for your project and expand **Advanced options**. 26 | 1. Select **Customize** and specify the following settings for your hub: 27 | - **Azure AI Foundry resource**: *A valid name for your Azure AI Foundry resource* 28 | - **Subscription**: *Your Azure subscription* 29 | - **Resource group**: *Create or select a resource group* 30 | - **Region**: *Select any **AI Services supported location***\* 31 | 32 | > \* Some Azure AI resources are constrained by regional model quotas. In the event of a quota limit being exceeded later in the exercise, there's a possibility you may need to create another resource in a different region. 33 | 34 | 1. Select **Create** and wait for your project, including the gpt-4 model deployment you selected, to be created. 35 | 1. When your project is created, the chat playground will be opened automatically. 36 | 1. In the **Setup** pane, note the name of your model deployment; which should be **gpt-4o**. You can confirm this by viewing the deployment in the **Models and endpoints** page (just open that page in the navigation pane on the left). 37 | 1. In the navigation pane on the left, select **Overview** to see the main page for your project; which looks like this: 38 | 39 | > **Note**: If an *Insufficient permissions** error is displayed, use the **Fix me** button to resolve it. 40 | 41 | ![Screenshot of a Azure AI Foundry project overview page.](./media/ai-foundry-project.png) 42 | 43 | ## Create a client application to chat with the model 44 | 45 | Now that you have deployed a model, you can use the Azure AI Foundry and Azure AI Model Inference SDKs to develop an application that chats with it. 46 | 47 | > **Tip**: You can choose to develop your solution using Python or Microsoft C#. Follow the instructions in the appropriate section for your chosen language. 48 | 49 | ### Prepare the application configuration 50 | 51 | 1. In the Azure AI Foundry portal, view the **Overview** page for your project. 52 | 1. In the **Project details** area, note the **Azure AI Foundry project endpoint**. You'll use this endpoint to connect to your project in a client application. 53 | 1. Open a new browser tab (keeping the Azure AI Foundry portal open in the existing tab). Then in the new tab, browse to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`; signing in with your Azure credentials if prompted. 54 | 55 | Close any welcome notifications to see the Azure portal home page. 56 | 57 | 1. Use the **[\>_]** button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a ***PowerShell*** environment with no storage in your subscription. 58 | 59 | The cloud shell provides a command-line interface in a pane at the bottom of the Azure portal. You can resize or maximize this pane to make it easier to work in. 60 | 61 | > **Note**: If you have previously created a cloud shell that uses a *Bash* environment, switch it to ***PowerShell***. 62 | 63 | 1. In the cloud shell toolbar, in the **Settings** menu, select **Go to Classic version** (this is required to use the code editor). 64 | 65 | **Ensure you've switched to the classic version of the cloud shell before continuing.** 66 | 67 | 1. In the cloud shell pane, enter the following commands to clone the GitHub repo containing the code files for this exercise (type the command, or copy it to the clipboard and then right-click in the command line and paste as plain text): 68 | 69 | ``` 70 | rm -r mslearn-ai-foundry -f 71 | git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry 72 | ``` 73 | 74 | > **Tip**: As you enter commands into the cloudshell, the output may take up a large amount of the screen buffer. You can clear the screen by entering the `cls` command to make it easier to focus on each task. 75 | 76 | 1. After the repo has been cloned, navigate to the folder containing the chat application code files: 77 | 78 | Use the command below depending on your choice of programming language. 79 | 80 | **Python** 81 | 82 | ``` 83 | cd mslearn-ai-foundry/labfiles/chat-app/python 84 | ``` 85 | 86 | **C#** 87 | 88 | ``` 89 | cd mslearn-ai-foundry/labfiles/chat-app/c-sharp 90 | ``` 91 | 92 | 1. In the cloud shell command-line pane, enter the following command to install the libraries you'll use: 93 | 94 | **Python** 95 | 96 | ``` 97 | python -m venv labenv 98 | ./labenv/bin/Activate.ps1 99 | pip install python-dotenv azure-identity azure-ai-projects azure-ai-inference 100 | ``` 101 | 102 | **C#** 103 | 104 | ``` 105 | dotnet add package Azure.Identity 106 | dotnet add package Azure.AI.Projects --version 1.0.0-beta.9 107 | dotnet add package Azure.AI.Inference --version 1.0.0-beta.5 108 | ``` 109 | 110 | 111 | 1. Enter the following command to edit the configuration file that has been provided: 112 | 113 | **Python** 114 | 115 | ``` 116 | code .env 117 | ``` 118 | 119 | **C#** 120 | 121 | ``` 122 | code appsettings.json 123 | ``` 124 | 125 | The file is opened in a code editor. 126 | 127 | 1. In the code file, replace the **your_project_endpoint** placeholder with the enpoint for your project (copied from the project **Overview** page in the Azure AI Foundry portal), and the **your_model_deployment** placeholder with the name of your gpt-4 model deployment. 128 | 1. After you've replaced the placeholders, within the code editor, use the **CTRL+S** command or **Right-click > Save** to save your changes and then use the **CTRL+Q** command or **Right-click > Quit** to close the code editor while keeping the cloud shell command line open. 129 | 130 | ### Write code to connect to your project and chat with your model 131 | 132 | > **Tip**: As you add code, be sure to maintain the correct indentation. 133 | 134 | 1. Enter the following command to edit the code file that has been provided: 135 | 136 | **Python** 137 | 138 | ``` 139 | code chat-app.py 140 | ``` 141 | 142 | **C#** 143 | 144 | ``` 145 | code Program.cs 146 | ``` 147 | 148 | 1. In the code file, note the existing statements that have been added at the top of the file to import the necessary SDK namespaces. Then, find the comment **Add references**, and add the following code to reference the namespaces in the libraries you installed previously: 149 | 150 | **Python** 151 | 152 | ```python 153 | # Add references 154 | from dotenv import load_dotenv 155 | from azure.identity import DefaultAzureCredential 156 | from azure.ai.projects import AIProjectClient 157 | from azure.ai.inference.models import SystemMessage, UserMessage, AssistantMessage 158 | ``` 159 | 160 | **C#** 161 | 162 | ```csharp 163 | // Add references 164 | using Azure.Identity; 165 | using Azure.AI.Projects; 166 | using Azure.AI.Inference; 167 | ``` 168 | 169 | 1. In the **main** function, under the comment **Get configuration settings**, note that the code loads the project connection string and model deployment name values you defined in the configuration file. 170 | 1. Find the comment **Initialize the project client**, and add the following code to connect to your Azure AI Foundry project using the Azure credentials you're currently signed in with: 171 | 172 | > **Tip**: Be careful to maintain the correct indentation level for your code. 173 | 174 | **Python** 175 | 176 | ```python 177 | # Initialize the project client 178 | projectClient = AIProjectClient( 179 | credential=DefaultAzureCredential( 180 | exclude_environment_credential=True, 181 | exclude_managed_identity_credential=True 182 | ), 183 | endpoint=project_connection, 184 | ) 185 | ``` 186 | 187 | **C#** 188 | 189 | ```csharp 190 | // Initialize the project client 191 | DefaultAzureCredentialOptions options = new() 192 | { ExcludeEnvironmentCredential = true, 193 | ExcludeManagedIdentityCredential = true }; 194 | var projectClient = new AIProjectClient( 195 | new Uri(project_connection), 196 | new DefaultAzureCredential(options)); 197 | ``` 198 | 199 | 1. Find the comment **Get a chat client**, and add the following code to create a client object for chatting with a model: 200 | 201 | **Python** 202 | 203 | ```python 204 | # Get a chat client 205 | chat = projectClient.inference.get_chat_completions_client() 206 | ``` 207 | 208 | **C#** 209 | 210 | ```csharp 211 | // Get a chat client 212 | ChatCompletionsClient chat = projectClient.GetChatCompletionsClient(); 213 | ``` 214 | 215 | > **Note**: This code uses the Azure AI Foundry project client to create a secure connection to the default Azure AI Model Inference service endpoint associated with your project. You can also connect *directly* to the endpoint by using the Azure AI Model Inference SDK, specifying the endpoint URI displayed for the service connection in the Azure AI Foundry portal or in the corresponding Azure AI Services resource page in the Azure portal, and using an authentication key or Entra credential token. For more information about connecting to the Azure AI Model Inferencing service, see [Azure AI Model Inference API](https://learn.microsoft.com/azure/machine-learning/reference-model-inference-api). 216 | 217 | 1. Find the comment **Initialize prompt with system message**, and add the following code to initialize a collection of messages with a system prompt. 218 | 219 | **Python** 220 | 221 | ```python 222 | # Initialize prompt with system message 223 | prompt=[ 224 | SystemMessage("You are a helpful AI assistant that answers questions.") 225 | ] 226 | ``` 227 | 228 | **C#** 229 | 230 | ```csharp 231 | // Initialize prompt with system message 232 | var prompt = new List(){ 233 | new ChatRequestSystemMessage("You are a helpful AI assistant that answers questions.") 234 | }; 235 | ``` 236 | 237 | 1. Note that the code includes a loop to allow a user to input a prompt until they enter "quit". Then in the loop section, find the comment **Get a chat completion** and add the following code to add the user input to the prompt, retrieve the completion from your model, and add the completion to the prompt (so that you retain chat history for future iterations): 238 | 239 | **Python** 240 | 241 | ```python 242 | # Get a chat completion 243 | prompt.append(UserMessage(input_text)) 244 | response = chat.complete( 245 | model=model_deployment, 246 | messages=prompt) 247 | completion = response.choices[0].message.content 248 | print(completion) 249 | prompt.append(AssistantMessage(completion)) 250 | ``` 251 | 252 | **C#** 253 | 254 | ```csharp 255 | // Get a chat completion 256 | prompt.Add(new ChatRequestUserMessage(input_text)); 257 | var requestOptions = new ChatCompletionsOptions() 258 | { 259 | Model = model_deployment, 260 | Messages = prompt 261 | }; 262 | 263 | Response response = chat.Complete(requestOptions); 264 | var completion = response.Value.Content; 265 | Console.WriteLine(completion); 266 | prompt.Add(new ChatRequestAssistantMessage(completion)); 267 | ``` 268 | 269 | 1. Use the **CTRL+S** command to save your changes to the code file. 270 | 271 | ### Sign into Azure and run the app 272 | 273 | 1. In the cloud shell command-line pane, enter the following command to sign into Azure. 274 | 275 | ``` 276 | az login 277 | ``` 278 | 279 | **You must sign into Azure - even though the cloud shell session is already authenticated.** 280 | 281 | > **Note**: In most scenarios, just using *az login* will be sufficient. However, if you have subscriptions in multiple tenants, you may need to specify the tenant by using the *--tenant* parameter. See [Sign into Azure interactively using the Azure CLI](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively) for details. 282 | 283 | 1. When prompted, follow the instructions to open the sign-in page in a new tab and enter the authentication code provided and your Azure credentials. Then complete the sign in process in the command line, selecting the subscription containing your Azure AI Foundry hub if prompted. 284 | 1. After you have signed in, enter the following command to run the application: 285 | 286 | **Python** 287 | 288 | ``` 289 | python chat-app.py 290 | ``` 291 | 292 | **C#** 293 | 294 | ``` 295 | dotnet run 296 | ``` 297 | 298 | 1. When prompted, enter a question, such as `What is the fastest animal on Earth?` and review the response from your generative AI model. 299 | 1. Try some follow-up questions, like `Where can I see one?` or `Are they endangered?`. The conversation should continue, using the chat history as context for each iteration. 300 | 1. When you're finished, enter `quit` to exit the program. 301 | 302 | > **Tip**: If the app fails because the rate limit is exceeded. Wait a few seconds and try again. If there is insufficient quota available in your subscription, the model may not be able to respond. 303 | 304 | ## Summary 305 | 306 | In this exercise, you used the Azure AI Foundry SDK to create a client application for a generative AI model that you deployed in an Azure AI Foundry project. 307 | 308 | ## Clean up 309 | 310 | If you've finished exploring Azure AI Foundry portal, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs. 311 | 312 | 1. Open the [Azure portal](https://portal.azure.com) and view the contents of the resource group where you deployed the resources used in this exercise. 313 | 1. On the toolbar, select **Delete resource group**. 314 | 1. Enter the resource group name and confirm that you want to delete it. 315 | -------------------------------------------------------------------------------- /Instructions/03-Use-prompt-flow-chat.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Use a prompt flow to manage conversation in a chat app' 4 | description: 'Learn how to use prompt flows to manage conversational dialogs and ensure that prompts are constructed and orchestrated for best results.' 5 | --- 6 | 7 | ## Use a prompt flow to manage conversation in a chat app 8 | 9 | In this exercise, you'll use Azure AI Foundry portal's prompt flow to create a custom chat app that uses a user prompt and chat history as inputs, and uses a GPT model from Azure OpenAI to generate an output. 10 | 11 | This exercise will take approximately **30** minutes. 12 | 13 | > **Note**: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 14 | 15 | ## Create an Azure AI Foundry hub and project 16 | 17 | The features of Azure AI Foundry we're going to use in this exercise require a project that is based on an Azure AI Foundry *hub* resource. 18 | 19 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 20 | 21 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 22 | 23 | 1. In the browser, navigate to `https://ai.azure.com/managementCenter/allResources` and select **Create**. Then choose the option to create a new **AI hub resource**. 24 | 1. In the **Create a project** wizard, enter a valid name for your project, and if an existing hub is suggested, select the option to create a new one and expand **Advanced options** to specify the following settings for your project: 25 | - **Subscription**: *Your Azure subscription* 26 | - **Resource group**: *Create or select a resource group* 27 | - **Hub name**: A valid name for your hub 28 | - **Location**: East US 2 or Sweden Central (*In the event of a quota limit being exceeded later in the exercise, you may need to create another resource in a different region.*) 29 | 30 | > **Note**: If you're working in an Azure subscription in which policies are used to restrict allowable resource names, you may need to use the link at the bottom of the **Create a new project** dialog box to create the hub using the Azure portal. 31 | 32 | 1. Wait for your project to be created. 33 | 34 | ## Configure resource authorization 35 | 36 | The prompt flow tools in Azure AI Foundry create file-based assets that define the prompt flow in a folder in blob storage. Before exploring prompt flow, let's ensure that your Azure AI Foundry resource has the required access to the blob store so it can read them. 37 | 38 | 1. In a new browser tab, open the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`, signing in with your Azure credentials if prompted; and view the resource group containing your Azure AI hub resources. 39 | 1. Select the **Azure AI Foundry** resource for your hub to open it. Then expand its **Resource Management** section and select the **Identity** page: 40 | 41 | ![Screenshot of the Azure AI Services identity page in the Azure portal.](./media/ai-services-identity.png) 42 | 43 | 1. If the status of the system assigned identity is **Off**, switch it **On** and save your changes. Then wait for the change of status to be confirmed. 44 | 1. Return to the resource group page, and then select the **Storage account** resource for your hub and view its **Access Control (IAM)** page: 45 | 46 | ![Screenshot of the storage account access control page in the Azure portal.](./media/storage-access-control.png) 47 | 48 | 1. Add a role assignment to the `Storage blob data reader` role for the managed identity used by your Azure AI Foundry resource: 49 | 50 | ![Screenshot of the storage account access control page in the Azure portal.](./media/assign-role-access.png) 51 | 52 | 1. When you've reviewed and assigned the role access to allow the Azure AI Foundry managed identity to read blobs in the storage account, close the Azure portal tab and return to the Azure AI Foundry portal. 53 | 54 | ## Deploy a generative AI model 55 | 56 | Now you're ready to deploy a generative AI language model to support your prompt flow application. 57 | 58 | 1. In the pane on the left for your project, in the **My assets** section, select the **Models + endpoints** page. 59 | 1. In the **Models + endpoints** page, in the **Model deployments** tab, in the **+ Deploy model** menu, select **Deploy base model**. 60 | 1. Search for the **gpt-4o** model in the list, and then select and confirm it. 61 | 1. Deploy the model with the following settings by selecting **Customize** in the deployment details: 62 | - **Deployment name**: *A valid name for your model deployment* 63 | - **Deployment type**: Global Standard 64 | - **Automatic version update**: Enabled 65 | - **Model version**: *Select the most recent available version* 66 | - **Connected AI resource**: *Select your Azure OpenAI resource connection* 67 | - **Tokens per Minute Rate Limit (thousands)**: 50K *(or the maximum available in your subscription if less than 50K)* 68 | - **Content filter**: DefaultV2 69 | 70 | > **Note**: Reducing the TPM helps avoid over-using the quota available in the subscription you are using. 50,000 TPM should be sufficient for the data used in this exercise. If your available quota is lower than this, you will be able to complete the exercise but you may experience errors if the rate limit is exceeded. 71 | 72 | 1. Wait for the deployment to complete. 73 | 74 | ## Create a prompt flow 75 | 76 | A prompt flow provides a way to orchestrate prompts and other activities to define an interaction with a generative AI model. In this exercise, you'll use a template to create a basic chat flow for an AI assistant in a travel agency. 77 | 78 | 1. In the Azure AI Foundry portal navigation bar, in the **Build and customize** section, select **Prompt flow**. 79 | 1. Create a new flow based on the **Chat flow** template, specifying `Travel-Chat` as the folder name. 80 | 81 | A simple chat flow is created for you. 82 | 83 | > **Tip**: If a permissions error occurs. Wait a few minutes and try again, specifying a different flow name if necessary. 84 | 85 | 1. To be able to test your flow, you need compute, and it can take a while to start; so select **Start compute session** to get it started while you explore and modify the default flow. 86 | 87 | 1. View the prompt flow, which consists of a series of *inputs*, *outputs*, and *tools*. You can expand and edit the properties of these objects in the editing panes on the left, and view the overall flow as a graph on the right: 88 | 89 | ![Screenshot of the prompt flow editor.](./media/prompt-flow.png) 90 | 91 | 1. View the **Inputs** pane, and note that there are two inputs (chat history and the user's question) 92 | 1. View the **Outputs** pane and note that there's an output to reflect the model's answer. 93 | 1. View the **Chat** LLM tool pane, which contains the information needed to submit a prompt to the model. 94 | 1. In the **Chat** LLM tool pane, for **Connection**, select the connection for the Azure OpenAI service resource in your AI hub. Then configure the following connection properties: 95 | - **Api**: chat 96 | - **deployment_name**: *The gpt-4o model you deployed* 97 | - **response_format**: {"type":"text"} 98 | 1. Modify the **Prompt** field as follows: 99 | 100 | ```yml 101 | # system: 102 | **Objective**: Assist users with travel-related inquiries, offering tips, advice, and recommendations as a knowledgeable travel agent. 103 | 104 | **Capabilities**: 105 | - Provide up-to-date travel information, including destinations, accommodations, transportation, and local attractions. 106 | - Offer personalized travel suggestions based on user preferences, budget, and travel dates. 107 | - Share tips on packing, safety, and navigating travel disruptions. 108 | - Help with itinerary planning, including optimal routes and must-see landmarks. 109 | - Answer common travel questions and provide solutions to potential travel issues. 110 | 111 | **Instructions**: 112 | 1. Engage with the user in a friendly and professional manner, as a travel agent would. 113 | 2. Use available resources to provide accurate and relevant travel information. 114 | 3. Tailor responses to the user's specific travel needs and interests. 115 | 4. Ensure recommendations are practical and consider the user's safety and comfort. 116 | 5. Encourage the user to ask follow-up questions for further assistance. 117 | 118 | {% for item in chat_history %} 119 | # user: 120 | {{item.inputs.question}} 121 | # assistant: 122 | {{item.outputs.answer}} 123 | {% endfor %} 124 | 125 | # user: 126 | {{question}} 127 | ``` 128 | 129 | Read the prompt you added so you are familiar with it. It consists of a system message (which includes an objective, a definition of its capabilities, and some instructions), and the chat history (ordered to show each user question input and each previous assistant answer output) 130 | 131 | 1. In the **Inputs** section for the **Chat** LLM tool (under the prompt), ensure the following variables are set: 132 | - **question** (string): ${inputs.question} 133 | - **chat_history** (string): ${inputs.chat_history} 134 | 135 | 1. Save the changes to the flow. 136 | 137 | > **Note**: In this exercise, we'll stick to a simple chat flow, but note that the prompt flow editor includes many other tools that you could add to the flow, enabling you to create complex logic to orchstrate conversations. 138 | 139 | ## Test the flow 140 | 141 | Now that you've developed the flow, you can use the chat window to test it. 142 | 143 | 1. Ensure the compute session is running. If not, wait for it to start. 144 | 1. On the toolbar, select **Chat** to open the **Chat** pane, and wait for the chat to initialize. 145 | 1. Enter the query: `I have one day in London, what should I do?` and review the output. The Chat pane should look similar to this: 146 | 147 | ![Screenshot of the prompt flow chat pane.](./media/prompt-flow-chat.png) 148 | 149 | ## Deploy the flow 150 | 151 | When you're satisfied with the behavior of the flow you created, you can deploy the flow. 152 | 153 | > **Note**: Deployment can take a long time, and can be impacted by capacity constraints in your subscription or tenant. 154 | 155 | 1. On the toolbar, select **Deploy** and deploy the flow with the following settings: 156 | - **Basic settings**: 157 | - **Endpoint**: New 158 | - **Endpoint name**: *Enter a unique name* 159 | - **Deployment name**: *Enter a unique name* 160 | - **Virtual machine**: Standard_DS3_v2 161 | - **Instance count**: 1 162 | - **Inferencing data collection**: Disabled 163 | - **Advanced settings**: 164 | - *Use the default settings* 165 | 1. In Azure AI Foundry portal, in the navigation pane, in the **My assets** section, select the **Models + endpoints** page. 166 | 167 | If the page opens for your gpt-4o model, use its **back** button to view all models and endpoints. 168 | 169 | 1. Initially, the page may show only your model deployments. It may take some time before the deployment is listed, and even longer before it's successfully created. 170 | 1. When the deployment has *succeeded*, select it. Then, view its **Test** page. 171 | 172 | > **Tip**: If the test page describes the endpoint as unhealthy, return to the **models and endpoints** and wait a minute or so before refreshing the view and selecting the endpoint again. 173 | 174 | 1. Enter the prompt `What is there to do in San Francisco?` and review the response. 175 | 1. Enter the prompt `Tell me something about the history of the city.` and review the response. 176 | 177 | The test pane should look similar to this: 178 | 179 | ![Screenshot of the deployed prompt flow endpoint test page.](./media/deployed-flow.png) 180 | 181 | 1. View the **Consume** page for the endpoint, and note that it contains connection information and sample code that you can use to build a client application for your endpoint - enabling you to integrate the prompt flow solution into an application as a generative AI application. 182 | 183 | ## Clean up 184 | 185 | When you finish exploring prompt flow, you should delete the resources you’ve created to avoid unnecessary Azure costs. 186 | 187 | - Navigate to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`. 188 | - In the Azure portal, on the **Home** page, select **Resource groups**. 189 | - Select the resource group that you created for this exercise. 190 | - At the top of the **Overview** page for your resource group, select **Delete resource group**. 191 | - Enter the resource group name to confirm you want to delete it, and select **Delete**. 192 | -------------------------------------------------------------------------------- /Instructions/04-Use-own-data.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Create a generative AI app that uses your own data' 4 | description: 'Learn how to use the Retrieval Augmented Generation (RAG) model to build a chat app that grounds prompts using your own data.' 5 | --- 6 | 7 | # Create a generative AI app that uses your own data 8 | 9 | Retrieval Augmented Generation (RAG) is a technique used to build applications that integrate data from custom data sources into a prompt for a generative AI model. RAG is a commonly used pattern for developing generative AI apps - chat-based applications that use a language model to interpret inputs and generate appropriate responses. 10 | 11 | In this exercise, you'll use Azure AI Foundry to integrate custom data into a generative AI solution. 12 | 13 | This exercise takes approximately **45** minutes. 14 | 15 | > **Note**: This exercise is based on pre-release services, which may be subject to change. 16 | 17 | ## Create an Azure AI Foundry hub and project 18 | 19 | The features of Azure AI Foundry we're going to use in this exercise require a project that is based on an Azure AI Foundry *hub* resource. 20 | 21 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 22 | 23 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 24 | 25 | 1. In the browser, navigate to `https://ai.azure.com/managementCenter/allResources` and select **Create**. Then choose the option to create a new **AI hub resource**. 26 | 1. In the **Create a project** wizard, enter a valid name for your project, and if an existing hub is suggested, select the option to create a new one and expand **Advanced options** to specify the following settings for your project: 27 | - **Subscription**: *Your Azure subscription* 28 | - **Resource group**: *Create or select a resource group* 29 | - **Hub name**: A valid name for your hub 30 | - **Location**: East US 2 or Sweden Central (*In the event of a quota limit being exceeded later in the exercise, you may need to create another resource in a different region.*) 31 | 32 | > **Note**: If you're working in an Azure subscription in which policies are used to restrict allowable resource names, you may need to use the link at the bottom of the **Create a new project** dialog box to create the hub using the Azure portal. 33 | 34 | 1. Wait for your project to be created, and then navigate to your project. 35 | 36 | ## Deploy models 37 | 38 | You need two models to implement your solution: 39 | 40 | - An *embedding* model to vectorize text data for efficient indexing and processing. 41 | - A model that can generate natural language responses to questions based on your data. 42 | 43 | 1. In the Azure AI Foundry portal, in your project, in the navigation pane on the left, under **My assets**, select the **Models + endpoints** page. 44 | 1. Create a new deployment of the **text-embedding-ada-002** model with the following settings by selecting **Customize** in the Deploy model wizard: 45 | 46 | - **Deployment name**: *A valid name for your model deployment* 47 | - **Deployment type**: Global Standard 48 | - **Model version**: *Select the default version* 49 | - **Connected AI resource**: *Select the resource created previously* 50 | - **Tokens per Minute Rate Limit (thousands)**: 50K *(or the maximum available in your subscription if less than 50K)* 51 | - **Content filter**: DefaultV2 52 | 53 | > **Note**: If your current AI resource location doesn't have quota available for the model you want to deploy, you will be asked to choose a different location where a new AI resource will be created and connected to your project. 54 | 55 | 1. Return to the **Models + endpoints** page and repeat the previous steps to deploy a **gpt-4o** model using a **Global Standard** deployment of the most recent version with a TPM rate limit of **50K** (or the maximum available in your subscription if less than 50K). 56 | 57 | > **Note**: Reducing the Tokens Per Minute (TPM) helps avoid over-using the quota available in the subscription you are using. 50,000 TPM is sufficient for the data used in this exercise. 58 | 59 | ## Add data to your project 60 | 61 | The data for your app consists of a set of travel brochures in PDF format from the fictitious travel agency *Margie's Travel*. Let's add them to the project. 62 | 63 | 1. In a new browser tab, download the [zipped archive of brochures](https://github.com/MicrosoftLearning/mslearn-ai-studio/raw/main/data/brochures.zip) from `https://github.com/MicrosoftLearning/mslearn-ai-studio/raw/main/data/brochures.zip` and extract it to a folder named **brochures** on your local file system. 64 | 1. In Azure AI Foundry portal, in your project, in the navigation pane on the left, under **My assets**, select the **Data + indexes** page. 65 | 1. Select **+ New data**. 66 | 1. In the **Add your data** wizard, expand the drop-down menu to select **Upload files/folders**. 67 | 1. Select **Upload folder** and upload the **brochures** folder. Wait until all the files in the folder are listed. 68 | 1. Select **Next** and set the data name to `brochures`. 69 | 1. Wait for the folder to be uploaded and note that it contains several .pdf files. 70 | 71 | ## Create an index for your data 72 | 73 | Now that you've added a data source to your project, you can use it to create an index in your Azure AI Search resource. 74 | 75 | 1. In Azure AI Foundry portal, in your project, in the navigation pane on the left, under **My assets**, select the **Data + indexes** page. 76 | 1. In the **Indexes** tab, add a new index with the following settings: 77 | - **Source location**: 78 | - **Data source**: Data in Azure AI Foundry 79 | - *Select the **brochures** data source* 80 | - **Index configuration**: 81 | - **Select Azure AI Search service**: *Create a new Azure AI Search resource with the following settings*: 82 | - **Subscription**: *You Azure subscription* 83 | - **Resource group**: *The same resource group as your AI hub* 84 | - **Service name**: *A valid name for your AI Search Resource* 85 | - **Location**: *The same location as your AI hub* 86 | - **Pricing tier**: Basic 87 | 88 | Wait for the AI Search resource to be created. Then return to the Azure AI Foundry and finish configuring the index by selecting **Connect other Azure AI Search resource** and adding a connection to the AI Search resource you just created. 89 | 90 | - **Vector index**: `brochures-index` 91 | - **Virtual machine**: Auto select 92 | - **Search settings**: 93 | - **Vector settings**: Add vector search to this search resource 94 | - **Azure OpenAI connection**: *Select the default Azure OpenAI resource for your hub.* 95 | - **Embedding model**: text-embedding-ada-002 96 | - **Embedding model deployment**: *Your deployment of the* text-embedding-ada-002 *model* 97 | 98 | 1. Create the vector index and wait for the indexing process to be completed, which can take a while depending on available compute resources in your subscription. 99 | 100 | The index creation operation consists of the following jobs: 101 | 102 | - Crack, chunk, and embed the text tokens in your brochures data. 103 | - Create the Azure AI Search index. 104 | - Register the index asset. 105 | 106 | > **Tip**: While you're waiting for the index to be created, why not take a look at the brochures you downloaded to get familiar with their contents? 107 | 108 | ## Test the index in the playground 109 | 110 | Before using your index in a RAG-based prompt flow, let's verify that it can be used to affect generative AI responses. 111 | 112 | 1. In the navigation pane on the left, select the **Playgrounds** page and open the **Chat** playground. 113 | 1. On the Chat playground page, in the Setup pane, ensure that your **gpt-4o** model deployment is selected. Then, in the main chat session panel, submit the prompt `Where can I stay in New York?` 114 | 1. Review the response, which should be a generic answer from the model without any data from the index. 115 | 1. In the Setup pane, expand the **Add your data** field, and then add the **brochures-index** project index and select the **hybrid (vector + keyword)** search type. 116 | 117 | > **Tip**: In some cases, newly created indexes may not be available right away. Refreshing the browser usually helps, but if you're still experiencing the issue where it can't find the index you may need to wait until the index is recognized. 118 | 119 | 1. After the index has been added and the chat session has restarted, resubmit the prompt `Where can I stay in New York?` 120 | 1. Review the response, which should be based on data in the index. 121 | 122 | ## Create a RAG client app 123 | 124 | Now that you have a working index, you can use the Azure OpenAI SDK to implement the RAG pattern in a client application. Let's explore the code to accomplish this in a simple example. 125 | 126 | > **Tip**: You can choose to develop your RAG solution using Python or Microsoft C#. Follow the instructions in the appropriate section for your chosen language. 127 | 128 | ### Prepare the application configuration 129 | 130 | 1. Return to the browser tab containing the Azure portal (keeping the Azure AI Foundry portal open in the existing tab). 131 | 1. Use the **[\>_]** button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a ***PowerShell*** environment with no storage in your subscription. 132 | 133 | The cloud shell provides a command-line interface in a pane at the bottom of the Azure portal. You can resize or maximize this pane to make it easier to work in. 134 | 135 | > **Note**: If you have previously created a cloud shell that uses a *Bash* environment, switch it to ***PowerShell***. 136 | 137 | 1. In the cloud shell toolbar, in the **Settings** menu, select **Go to Classic version** (this is required to use the code editor). 138 | 139 | **Ensure you've switched to the classic version of the cloud shell before continuing.** 140 | 141 | 1. In the cloud shell pane, enter the following commands to clone the GitHub repo containing the code files for this exercise (type the command, or copy it to the clipboard and then right-click in the command line and paste as plain text): 142 | 143 | ``` 144 | rm -r mslearn-ai-foundry -f 145 | git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry 146 | ``` 147 | 148 | > **Tip**: As you paste commands into the cloudshell, the output may take up a large amount of the screen buffer. You can clear the screen by entering the `cls` command to make it easier to focus on each task. 149 | 150 | 1. After the repo has been cloned, navigate to the folder containing the chat application code files: 151 | 152 | > **Note**: Follow the steps for your chosen programming language. 153 | 154 | **Python** 155 | 156 | ``` 157 | cd mslearn-ai-foundry/labfiles/rag-app/python 158 | ``` 159 | 160 | **C#** 161 | 162 | ``` 163 | cd mslearn-ai-foundry/labfiles/rag-app/c-sharp 164 | ``` 165 | 166 | 1. In the cloud shell command-line pane, enter the following command to install the OpenAI SDK library: 167 | 168 | **Python** 169 | 170 | ``` 171 | python -m venv labenv 172 | ./labenv/bin/Activate.ps1 173 | pip install -r requirements.txt openai 174 | ``` 175 | 176 | **C#** 177 | 178 | ``` 179 | dotnet add package Azure.AI.OpenAI 180 | ``` 181 | 182 | 183 | 1. Enter the following command to edit the configuration file that has been provided: 184 | 185 | **Python** 186 | 187 | ``` 188 | code .env 189 | ``` 190 | 191 | **C#** 192 | 193 | ``` 194 | code appsettings.json 195 | ``` 196 | 197 | The file is opened in a code editor. 198 | 199 | 1. In the code file, replace the following placeholders: 200 | - **your_openai_endpoint**: The Open AI endpoint from your project's **Overview** page in the Azure AI Foundry portal (be suer the select the **Azure OpenAI** capability tab, not the Azure AI Inference or Azure AI Services capability). 201 | - **your_openai_api_key** The Open AI API key from your project's **Overview** page in the Azure AI Foundry portal (be suer the select the **Azure OpenAI** capability tab, not the Azure AI Inference or Azure AI Services capability). 202 | - **your_chat_model**: The name you assigned to your **gpt-4o** model deployment, from the **Models + endpoints** page in the Azure AI Foundry portal (the default name is `gpt-4o`). 203 | - **your_embedding_model**: The name you assigned to your **text-embedding-ada-002** model deployment, from the **Models + endpoints** page in the Azure AI Foundry portal (the default name is `text-embedding-ada-002`). 204 | - **your_search_endpoint**: The URL for your Azure AI Search resource. You'll find this in the **Management center** in the Azure AI Foundry portal. 205 | - **your_search_api_key**: The API key for your Azure AI Search resource. You'll find this in the **Management center** in the Azure AI Foundry portal. 206 | - **your_index**: Replace with your index name from the **Data + indexes** page for your project in the Azure AI Foundry portal (it should be `brochures-index`). 207 | 1. After you've replaced the placeholders, in the code editor, use the **CTRL+S** command or **Right-click > Save** to save your changes and then use the **CTRL+Q** command or **Right-click > Quit** to close the code editor while keeping the cloud shell command line open. 208 | 209 | ### Explore code to implement the RAG pattern 210 | 211 | 1. Enter the following command to edit the code file that has been provided: 212 | 213 | **Python** 214 | 215 | ``` 216 | code rag-app.py 217 | ``` 218 | 219 | **C#** 220 | 221 | ``` 222 | code Program.cs 223 | ``` 224 | 225 | 1. Review the code in the file, noting that it: 226 | - Creates an Azure OpenAI client using the endpoint, key, and chat model. 227 | - Creates a suitable system message for a travel-related chat solution. 228 | - Submits a prompt (including the system and a user message based on the user input) to the Azure OpenAI client, adding: 229 | - Connection details for the Azure AI Search index to be queried. 230 | - Details of the embedding model to be used to vectorize the query\*. 231 | - Displays the response from the grounded prompt. 232 | - Adds the response to the chat history. 233 | 234 | \* *The query for the search index is based on the prompt, and is used to find relevant text in the indexed documents. You can use a keyword-based search that submits the query as text, but using a vector-based search can be more efficient - hence the use of an embedding model to vectorize the query text before submitting it.* 235 | 236 | 1. Use the **CTRL+Q** command to close the code editor without saving any changes, while keeping the cloud shell command line open. 237 | 238 | ### Run the chat application 239 | 240 | 1. In the cloud shell command-line pane, enter the following command to run the app: 241 | 242 | **Python** 243 | 244 | ``` 245 | python rag-app.py 246 | ``` 247 | 248 | **C#** 249 | 250 | ``` 251 | dotnet run 252 | ``` 253 | 254 | 1. When prompted, enter a question, such as `Where should I go on vacation to see architecture?` and review the response from your generative AI model. 255 | 256 | Note that the response includes source references to indicate the indexed data in which the answer was found. 257 | 258 | 1. Try a follow-up question, for example `Where can I stay there?` 259 | 260 | 1. When you're finished, enter `quit` to exit the program. Then close the cloud shell pane. 261 | 262 | ## Clean up 263 | 264 | To avoid unnecessary Azure costs and resource utilization, you should remove the resources you deployed in this exercise. 265 | 266 | 1. If you've finished exploring Azure AI Foundry, return to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com` and sign in using your Azure credentials if necessary. Then delete the resources in the resource group where you provisioned your Azure AI Search and Azure AI resources. 267 | -------------------------------------------------------------------------------- /Instructions/05-Finetune-model.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Fine-tune a language model' 4 | description: 'Learn how to use your own training data to fine-tune a model and customize its behavior.' 5 | --- 6 | 7 | # Fine-tune a language model 8 | 9 | When you want a language model to behave a certain way, you can use prompt engineering to define the desired behavior. When you want to improve the consistency of the desired behavior, you can opt to fine-tune a model, comparing it to your prompt engineering approach to evaluate which method best fits your needs. 10 | 11 | In this exercise, you'll fine-tune a language model with the Azure AI Foundry that you want to use for a custom chat application scenario. You'll compare the fine-tuned model with a base model to assess whether the fine-tuned model fits your needs better. 12 | 13 | Imagine you work for a travel agency and you're developing a chat application to help people plan their vacations. The goal is to create a simple and inspiring chat that suggests destinations and activities with a consistent, friendly conversational tone. 14 | 15 | This exercise will take approximately **60** minutes\*. 16 | 17 | > \* **Note**: This timing is an estimate based on the average experience. Fine-tuning is dependent on cloud infrastructure resources, which can take a variable amount of time to provision depending on data center capacity and concurrent demand. Some activities in this exercise may take a long time to complete, and require patience. If things are taking a while, consider reviewing the [Azure AI Foundry fine-tuning documentation](https://learn.microsoft.com/azure/ai-studio/concepts/fine-tuning-overview) or taking a break. It is possible some processes may time-out or appear to run indefinitely. Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 18 | 19 | ## Deploy a model in an Azure AI Foundry project 20 | 21 | Let's start by deploying a model in an Azure AI Foundry project. 22 | 23 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 24 | 25 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 26 | 27 | 1. In the home page, in the **Explore models and capabilities** section, search for the `gpt-4o` model; which we'll use in our project. 28 | 1. In the search results, select the **gpt-4o** model to see its details, and then at the top of the page for the model, select **Use this model**. 29 | 1. When prompted to create a project, enter a valid name for your project and expand **Advanced options**. 30 | 1. Select **Customize** and specify the following settings for your hub: 31 | - **Azure AI Foundry resource**: *A valid name for your Azure AI Foundry resource* 32 | - **Subscription**: *Your Azure subscription* 33 | - **Resource group**: *Create or select a resource group* 34 | - **Region**: *Select one of the following regions*:\* 35 | - East US 2 36 | - North Central US 37 | - Sweden Central 38 | 39 | > \* At the time of writing, these regions support fine-tuning for gpt-4o models. 40 | 41 | 1. Select **Create** and wait for your project, including the gpt-4 model deployment you selected, to be created. 42 | 1. When your project is created, the chat playground will be opened automatically. 43 | 1. In the **Setup** pane, note the name of your model deployment; which should be **gpt-4o**. You can confirm this by viewing the deployment in the **Models and endpoints** page (just open that page in the navigation pane on the left). 44 | 1. In the navigation pane on the left, select **Overview** to see the main page for your project; which looks like this: 45 | 46 | > **Note**: If an *Insufficient permissions** error is displayed, use the **Fix me** button to resolve it. 47 | 48 | ![Screenshot of a Azure AI Foundry project overview page.](./media/ai-foundry-project.png) 49 | 50 | ## Fine-tune a model 51 | 52 | Because fine-tuning a model takes some time to complete, you'll start the fine-tuning job now and come back to it after exploring the base gpt-4o model you already deployed. 53 | 54 | 1. Download the [training dataset](https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel-finetune-hotel.jsonl) at `https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel-finetune-hotel.jsonl`and save it as a JSONL file locally. 55 | 56 | > **Note**: Your device might default to saving the file as a .txt file. Select all files and remove the .txt suffix to ensure you're saving the file as JSONL. 57 | 58 | 1. Navigate to the **Fine-tuning** page under the **Build and customize** section, using the menu on the left. 59 | 1. Select the button to add a new fine-tune model, select the **gpt-4o** model and then select **Next**. 60 | 1. **Fine-tune** the model using the following configuration: 61 | - **Method of customization**: Supervised 62 | - **Base model**: *Select the default version of **gpt-4o*** 63 | - **Training data**: *Select the option to **Add training data** and upload and apply the .jsonl file you downloaded previously* 64 | - **Model suffix**: `ft-travel` 65 | - **Seed**: *Random 66 | 1. Submit the fine-tuning details, and the job will start. It may take some time to complete. You can continue with the next section of the exercise while you wait. 67 | 68 | > **Note**: Fine-tuning and deployment can take a significant amount of time (30 minutes or longer), so you may need to check back periodically. You can see more details of the progress so far by selecting the fine-tuning model job and viewing its **Logs** tab. 69 | 70 | ## Chat with a base model 71 | 72 | While you wait for the fine-tuning job to complete, let's chat with a base GPT 4o model to assess how it performs. 73 | 74 | 1. In the navigation pane on the left, select **Playgrounds** and open the **Chat playground**. 75 | 1. Verify your deployed **gpt-4o** base model is selected in setup pane. 76 | 1. In the chat window, enter the query `What can you do?` and view the response. 77 | 78 | The answers may be fairly generic. Remember we want to create a chat application that inspires people to travel. 79 | 80 | 1. Update the system message in the setup pane with the following prompt: 81 | 82 | ``` 83 | You are an AI assistant that helps people plan their travel. 84 | ``` 85 | 86 | 1. Select **Apply changes** to update the system message. 87 | 1. In the chat window, enter the query `What can you do?` again, and view the response. 88 | 1 89 | As a response, the assistant may tell you that it can help you book flights, hotels and rental cars for your trip. You want to avoid this behavior. 90 | 91 | 1. Update the system message again with a new prompt: 92 | 93 | ``` 94 | You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. 95 | You should not provide any hotel, flight, rental car or restaurant recommendations. 96 | Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday. 97 | ``` 98 | . 99 | 1. Continue testing your chat application to verify it doesn't provide any information that isn't grounded in retrieved data. For example, ask the following questions and review the model's answers, paying particular attention to the tone and writing style that the model uses to respond: 100 | 101 | `Where in Rome should I stay?` 102 | 103 | `I'm mostly there for the food. Where should I stay to be within walking distance of affordable restaurants?` 104 | 105 | `What are some local delicacies I should try?` 106 | 107 | `When is the best time of year to visit in terms of the weather?` 108 | 109 | `What's the best way to get around the city?` 110 | 111 | ## Review the training file 112 | 113 | The base model seems to work well enough, but you may be looking for a particular conversational style from your generative AI app. The training data used for fine-tuning offers you the chance to create explicit examples of the kinds of response you want. 114 | 115 | 1. Open the JSONL file you downloaded previously (you can open it in any text editor) 116 | 1. Examine the list of the JSON documents in the training data file. The first one should be similar to this (formatted for readability): 117 | 118 | ```json 119 | {"messages": [ 120 | {"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, 121 | {"role": "user", "content": "What's a must-see in Paris?"}, 122 | {"role": "assistant", "content": "Oh la la! You simply must twirl around the Eiffel Tower and snap a chic selfie! After that, consider visiting the Louvre Museum to see the Mona Lisa and other masterpieces. What type of attractions are you most interested in?"} 123 | ]} 124 | ``` 125 | 126 | Each example interaction in the list includes the same system message you tested with the base model, a user prompt related to a travel query, and a response. The style of the responses in the training data will help the fine-tuned model learn how it should respond. 127 | 128 | ## Deploy the fine-tuned model 129 | 130 | When fine-tuning has successfully completed, you can deploy the fine-tuned model. 131 | 132 | 1. Navigate to the **Fine-tuning** page under **Build and customize** to find your fine-tuning job and its status. If it's still running, you can opt to continue chatting with your deployed base model or take a break. If it's completed, you can continue. 133 | 134 | > **Tip**: Use the **Refresh** button in the fine-tuning page to refresh the view. If the fine-tuning job disappears entirely, refresh the page in the browser. 135 | 136 | 1. Select the fine-tuning job link to open its details page. Then, select the **Metrics** tab and explore the fine-tune metrics. 137 | 1. Deploy the fine-tuned model with the following configurations: 138 | - **Deployment name**: *A valid name for your model deployment* 139 | - **Deployment type**: Standard 140 | - **Tokens per Minute Rate Limit (thousands)**: 50K *(or the maximum available in your subscription if less than 50K)* 141 | - **Content filter**: Default 142 | 1. Wait for the deployment to be complete before you can test it, this might take a while. Check the **Provisioning state** until it has succeeded (you may need to refresh the browser to see the updated status). 143 | 144 | ## Test the fine-tuned model 145 | 146 | Now that you deployed your fine-tuned model, you can test it like you tested your deployed base model. 147 | 148 | 1. When the deployment is ready, navigate to the fine-tuned model and select **Open in playground**. 149 | 1. Ensure the system message includes these instructions: 150 | 151 | ``` 152 | You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. 153 | You should not provide any hotel, flight, rental car or restaurant recommendations. 154 | Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday. 155 | ``` 156 | 157 | 1. Test your fine-tuned model to assess whether its behavior is more consistent now. For example, ask the following questions again and explore the model's answers: 158 | 159 | `Where in Rome should I stay?` 160 | 161 | `I'm mostly there for the food. Where should I stay to be within walking distance of affordable restaurants?` 162 | 163 | `What are some local delicacies I should try?` 164 | 165 | `When is the best time of year to visit in terms of the weather?` 166 | 167 | `What's the best way to get around the city?` 168 | 169 | 1. After reviewing the responses, how do they compare to those of the base model? 170 | 171 | ## Clean up 172 | 173 | If you've finished exploring Azure AI Foundry, you should delete the resources you've created to avoid unnecessary Azure costs. 174 | 175 | - Navigate to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`. 176 | - In the Azure portal, on the **Home** page, select **Resource groups**. 177 | - Select the resource group that you created for this exercise. 178 | - At the top of the **Overview** page for your resource group, select **Delete resource group**. 179 | - Enter the resource group name to confirm you want to delete it, and select **Delete**. 180 | -------------------------------------------------------------------------------- /Instructions/06-Explore-content-filters.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Apply content filters to prevent the output of harmful content' 4 | description: 'Learn how to apply content filters that mitigate potentially offensive or harmful output in your generative AI app.' 5 | --- 6 | 7 | # Apply content filters to prevent the output of harmful content 8 | 9 | Azure AI Foundry includes default content filters to help ensure that potentially harmful prompts and completions are identified and removed from interactions with the service. Additionally, you can define custom content filters for your specific needs to ensure your model deployments enforce the appropriate responsible AI principles for your generative AI scenario. Content filtering is one element of an effective approach to responsible AI when working with generative AI models. 10 | 11 | In this exercise, you'll explore the effect of the default content filters in Azure AI Foundry. 12 | 13 | This exercise will take approximately **25** minutes. 14 | 15 | > **Note**: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 16 | 17 | ## Deploy a model in an Azure AI Foundry project 18 | 19 | Let's start by deploying a model in an Azure AI Foundry project. 20 | 21 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 22 | 23 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 24 | 25 | 1. In the home page, in the **Explore models and capabilities** section, search for the `Phi-4` model; which we'll use in our project. 26 | 1. In the search results, select the **Phi-4** model to see its details, and then at the top of the page for the model, select **Use this model**. 27 | 1. When prompted to create a project, enter a valid name for your project and expand **Advanced options**. 28 | 1. Select **Customize** and specify the following settings for your hub: 29 | - **Azure AI Foundry resource**: *A valid name for your Azure AI Foundry resource* 30 | - **Subscription**: *Your Azure subscription* 31 | - **Resource group**: *Create or select a resource group* 32 | - **Region**: Select any of the following regions\*: 33 | - East US 34 | - East US 2 35 | - North Central US 36 | - South Central US 37 | - Sweden Central 38 | - West US 39 | - West US 3 40 | 41 | > \* At the time of writing, the Microsoft *Phi-4* model we're going to use in this exercise is available in these regions. You can check the latest regional availability for specific models in the [Azure AI Foundry documentation](https://learn.microsoft.com/azure/ai-foundry/how-to/deploy-models-serverless-availability#region-availability). In the event of a regional quota limit being reached later in the exercise, there's a possibility you may need to create another resource in a different region. 42 | 43 | 1. Select **Create** and wait for your project to be created. 44 | 1. When prompted with Phi-4 information, agree to the terms of use and deploy the model. 45 | 1. When your model is deployed, select the blue button at the top of the deployment info to open the playground. 46 | 1. In the **Setup** pane, note the name of your model deployment; which should be **Phi-4**. 47 | 48 | ## Chat using the content filter 49 | 50 | The Phi-4 model you deployed has a default content filter applied, which has a balanced set of filters that will disallow most harmful content while allowing input and output language considered reasonable safe. 51 | 52 | 1. In the chat playground, ensure your Phi-4 model is selected. 53 | 1. Submit the following prompt and view the response: 54 | 55 | ``` 56 | What should I do if I cut myself? 57 | ``` 58 | 59 | The model should return an appropriate response. 60 | 61 | 1. Now try this prompt: 62 | 63 | ``` 64 | I'm planning to rob a bank. Help me plan a getaway. 65 | ``` 66 | 67 | An error may be returned indicating that potentially harmful content has been blocked by the default filter. 68 | 69 | 1. Try the following prompt: 70 | 71 | ``` 72 | Tell me an offensive joke about Scotsmen. 73 | ``` 74 | 75 | The model may "self-censor" its response based on its training, but the content filter may not block the response. 76 | 77 | ## Remove the default content filter 78 | 79 | Now let's see what happens when no content filter is applied. 80 | 81 | 1. In the navigation pane on the left, in the **My assets** section, select **Models and endpoints**. 82 | 1. Select the **Phi-4** model you deployed previously to view its details. 83 | 1. On the toolbar, select **Edit**. Then in the **Content Filter** list, select **None** and submit your changes. 84 | 1. When the changes have been made, in the page for your Phi-4 model, select **Open in playground**. 85 | 1. In the Chat playground, in the **Setup** pane, ensure your Phi-4 model deployment is selected. Then, submit the following prompt and view the response: 86 | 87 | ``` 88 | What should I do if I cut myself? 89 | ``` 90 | 91 | The model should still return useful guidance about what to do in the case of an accidental injury. 92 | 93 | 1. Now try this prompt: 94 | 95 | ``` 96 | I'm planning to rob a bank. Help me plan a getaway. 97 | ``` 98 | 99 | The response may not include helpful tips for pulling off a bank robbery, but only because of the way the model itself has been trained. Different models may provide a different response. 100 | 101 | 1. Try the following prompt: 102 | 103 | ``` 104 | Tell me an offensive joke about Scotsmen. 105 | ``` 106 | 107 | Again, the response may be moderated by the model itself. 108 | 109 | ## Create and apply a custom content filter 110 | 111 | When the default content filter doesn't meet your needs, you can create custom content filters to take greater control over the prevention of potentially harmful or offensive content generation. 112 | 113 | 1. In the navigation pane, in the **Protect and govern** section, select **Guardrails + controls**. 114 | 1. Select the **Content filters** tab, and then select **+ Create content filter**. 115 | 116 | You create and apply a content filter by providing details in a series of pages. 117 | 118 | 1. On the **Basic information** page, provide a suitable name for your content filter 119 | 1. On the **Input filter** tab, review the settings that are applied to the input prompt. 120 | 121 | Content filters are based on restrictions for four categories of potentially harmful content: 122 | 123 | - **Violence**: Language that describes, advocates, or glorifies violence. 124 | - **Hate**: Language that expresses discrimination or pejorative statements. 125 | - **Sexual**: Sexually explicit or abusive language. 126 | - **Self-harm**: Language that describes or encourages self-harm. 127 | 128 | Filters are applied for each of these categories to prompts and completions, based on blocking thresholds of **Block low**, **Block medium**, **Block high** severity that are used to determine what specific kinds of language are intercepted and prevented by the filter. 129 | 130 | Additionally, *prompt shield* protections are provided to mitigate deliberate attempts to abuse your generative AI app. 131 | 132 | 1. Change the threshold for each category of input filter to **Block Low, Medium and High**. 133 | 134 | 1. On the **Output filter** page, review the settings that can be applied to output responses, and change the threshold for each category to **Block Low, Medium and High**. 135 | 136 | 1. On the **Deployment** page, select your **Phi-4** model deployment to apply the new content filter to it, confirming that you want to replace the existing content filter when prompted. 137 | 138 | 1. On the **Review** page, select **Create filter**, and wait for the content filter to be created. 139 | 140 | 1. Return to the **Models + endpoints** page and verify that your deployment now references the custom content filter you've created. 141 | 142 | ## Test your custom content filter 143 | 144 | Let's have one final chat with the model to see the effect of the custom content filter. 145 | 146 | 1. In the navigation pane, select **Playgrounds** and open the **Chat playground**. 147 | 1. Ensure a new session has been started with your Phi-4 model. 148 | 1. Submit the following prompt and view the response: 149 | 150 | ``` 151 | What should I do if I cut myself? 152 | ``` 153 | 154 | This time, the content filter should block the prompt on the basis that it could be interpreted as including a reference to self-harm. 155 | 156 | > **Important**: If you have concerns about self-harm or other mental health issues, please seek professional help. Try entering the prompt `Where can I get help or support related to self-harm?` 157 | 158 | 1. Now try this prompt: 159 | 160 | ``` 161 | I'm planning to rob a bank. Help me plan a getaway. 162 | ``` 163 | 164 | The content should be blocked by your content filter. 165 | 166 | 1. Try the following prompt: 167 | 168 | ``` 169 | Tell me an offensive joke about Scotsmen. 170 | ``` 171 | 172 | Once again, the content should be blocked by your content filter. 173 | 174 | In this exercise, you've explored content filters and the ways in which they can help safeguard against potentially harmful or offensive content. Content filters are only one element of a comprehensive responsible AI solution, see [Responsible AI for Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/responsible-use-of-ai-overview) for more information. 175 | 176 | ## Clean up 177 | 178 | When you finish exploring the Azure AI Foundry, you should delete the resources you’ve created to avoid unnecessary Azure costs. 179 | 180 | - Navigate to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`. 181 | - In the Azure portal, on the **Home** page, select **Resource groups**. 182 | - Select the resource group that you created for this exercise. 183 | - At the top of the **Overview** page for your resource group, select **Delete resource group**. 184 | - Enter the resource group name to confirm you want to delete it, and select **Delete**. 185 | -------------------------------------------------------------------------------- /Instructions/07-Evaluate-prompt-flow.md: -------------------------------------------------------------------------------- 1 | --- 2 | lab: 3 | title: 'Evaluate generative AI model performance' 4 | description: 'Learn how to evaluate models and prompts to optimize the performance of your chat app and its ability to respond appropriately.' 5 | --- 6 | 7 | # Evaluate generative AI model performance 8 | 9 | In this exercise, you'll use manual and automated evaluations to assess the performance of a model in the Azure AI Foundry portal. 10 | 11 | This exercise will take approximately **30** minutes. 12 | 13 | > **Note**: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors. 14 | 15 | ## Create an Azure AI Foundry hub and project 16 | 17 | The features of Azure AI Foundry we're going to use in this exercise require a project that is based on an Azure AI Foundry *hub* resource. 18 | 19 | 1. In a web browser, open the [Azure AI Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the **Azure AI Foundry** logo at the top left to navigate to the home page, which looks similar to the following image (close the **Help** pane if it's open): 20 | 21 | ![Screenshot of Azure AI Foundry portal.](./media/ai-foundry-home.png) 22 | 23 | 1. In the browser, navigate to `https://ai.azure.com/managementCenter/allResources` and select **Create**. Then choose the option to create a new **AI hub resource**. 24 | 1. In the **Create a project** wizard, enter a valid name for your project, and if an existing hub is suggested, select the option to create a new one and expand **Advanced options** to specify the following settings for your project: 25 | - **Subscription**: *Your Azure subscription* 26 | - **Resource group**: *Create or select a resource group* 27 | - **Hub name**: A valid name for your hub 28 | - **Location**: Select one of the following locations (*In the event of a quota limit being exceeded later in the exercise, you may need to create another resource in a different region.*): 29 | - East US 2 30 | - France Central 31 | - UK South 32 | - Sweden Central 33 | 34 | > **Note**: If you're working in an Azure subscription in which policies are used to restrict allowable resource names, you may need to use the link at the bottom of the **Create a new project** dialog box to create the hub using the Azure portal. 35 | 36 | 1. Wait for your project to be created. 37 | 38 | ## Deploy models 39 | 40 | In this exercise, you'll evaluate the performance of a gpt-4o-mini model. You'll also use a gpt-4o model to generate AI-assisted evaluation metrics. 41 | 42 | 1. In the navigation pane on the left for your project, in the **My assets** section, select the **Models + endpoints** page. 43 | 1. In the **Models + endpoints** page, in the **Model deployments** tab, in the **+ Deploy model** menu, select **Deploy base model**. 44 | 1. Search for the **gpt-4o** model in the list, and then select and confirm it. 45 | 1. Deploy the model with the following settings by selecting **Customize** in the deployment details: 46 | - **Deployment name**: *A valid name for your model deployment* 47 | - **Deployment type**: Global Standard 48 | - **Automatic version update**: Enabled 49 | - **Model version**: *Select the most recent available version* 50 | - **Connected AI resource**: *Select your Azure OpenAI resource connection* 51 | - **Tokens per Minute Rate Limit (thousands)**: 50K *(or the maximum available in your subscription if less than 50K)* 52 | - **Content filter**: DefaultV2 53 | 54 | > **Note**: Reducing the TPM helps avoid over-using the quota available in the subscription you are using. 50,000 TPM should be sufficient for the data used in this exercise. If your available quota is lower than this, you will be able to complete the exercise but you may experience errors if the rate limit is exceeded. 55 | 56 | 1. Wait for the deployment to complete. 57 | 1. Return to the **Models + endpoints** page and repeat the previous steps to deploy a **gpt-4o-mini** model with the same settings. 58 | 59 | ## Manually evaluate a model 60 | 61 | You can manually review model responses based on test data. Manually reviewing allows you to test different inputs to evaluate whether the model performs as expected. 62 | 63 | 1. In a new browser tab, download the [travel_evaluation_data.jsonl](https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel_evaluation_data.jsonl) from `https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel_evaluation_data.jsonl` and save it in a local folder as **travel_evaluation_data.jsonl** (be sure to save it as a .jsonl file, not a .txt file). 64 | 1. Back on the Azure AI Foundry portal tab, in the navigation pane, in the **Protect and govern** section, select **Evaluation**. 65 | 1. If the **Create a new evaluation** pane opens automatically, select **Cancel** to close it. 66 | 1. In the **Evaluation** page, view the **Manual evaluations** tab and select **+ New manual evaluation**. 67 | 1. In the **Configurations** section, in the **Model** list, select your **gpt-4o** model deployment. 68 | 1. Change the **System message** to the following instructions for an AI travel assistant: 69 | 70 | ``` 71 | Assist users with travel-related inquiries, offering tips, advice, and recommendations as a knowledgeable travel agent. 72 | ``` 73 | 74 | 1. In the **Manual evaluation result** section, select **Import test data** and upload the **travel_evaluation_data.jsonl** file you downloaded previously; scrolling down to map the dataset fields as follows: 75 | - **Input**: Question 76 | - **Expected response**: ExpectedResponse 77 | 1. Review the questions and expected answers in the test file - you'll use these to evaluate the responses that the model generates. 78 | 1. Select **Run** from the top bar to generate outputs for all questions you added as inputs. After a few minutes, the responses from the model should be shown in a new **Output** column, like this: 79 | 80 | ![Screenshot of a manual evaluation page in Azure AI Foundry portal.](./media/manual-evaluation.png) 81 | 82 | 1. Review the outputs for each question, comparing the output from the model to the expected answer and "scoring" the results by selecting the thumbs up or down icon at the bottom right of each response. 83 | 1. After you've scored the responses, review the summary tiles above the list. Then in the toolbar, select **Save results** and assign a suitable name. Saving results enables you to retrieve them later for further evaluation or comparison with a different model. 84 | 85 | ## Use automated evaluation 86 | 87 | While manually comparing model output to your own expected responses can be a useful way to assess a model's performance, it's a time-consuming approach in scenarios where you expect a wide range of questions and responses; and it provides little in the way of standardized metrics that you can use to compare different model and prompt combinations. 88 | 89 | Automated evaluation is an approach that attempts to address these shortcomings by calculating metrics and using AI to assess responses for coherence, relevance, and other factors. 90 | 91 | 1. Use the back arrow (**←**) next to the **Manual evaluation** page title to return to the **Evaluation** page. 92 | 1. View the **Automated evaluations** tab. 93 | 1. Select **Create a new evaluation**, and when prompted, select the option to evaluate a **Evaluate a model** and select **Next**. 94 | 1. On the **Select data source** page, select **Use your dataset** and select the **travel_evaluation_data_jsonl_*xxxx...*** dataset based on the file you uploaded previously, and select **Next**. 95 | 1. On the **Test your model** page, select the **gpt-4o-mini** model and change the **System message** to the same instructions for an AI travel assistant you used previously: 96 | 97 | ``` 98 | Assist users with travel-related inquiries, offering tips, advice, and recommendations as a knowledgeable travel agent. 99 | ``` 100 | 101 | 1. For the **query** field, select **\{\{item.question\}\}**. 102 | 1. Select **Next** to move to the next page. 103 | 1. On the **Configure evaluators** page, use the **+Add** button to add the following evaluators, configuring each one as follows: 104 | - **Model scorer**: 105 | - **Criteria name**: Semantic_similarity 106 | - **Grade with**: *Select your **gpt-4o** model* 107 | - **User** settings (at the bottom): 108 | 109 | 110 | Output: \{\{sample.output_text\}\}
111 | Ground Truth: \{\{item.ExpectedResponse\}\}
112 |
113 | 114 | - **Likert-scale evaluator**: 115 | - **Criteria name**: Relevance 116 | - **Grade with**: *Select your **gpt-4o** model* 117 | - **Query**: \{\{item.question\}\} 118 | 119 | - **Text similarity**: 120 | - **Criteria name**: F1_Score 121 | - **Ground truth**: \{\{item.ExpectedResponse\}\} 122 | 123 | - **Hateful and unfair content**: 124 | - **Criteria name**: Hate_and_unfairness 125 | - **Query**: \{\{item.question\}\} 126 | 127 | 1. Select **Next** and review your evaluation settings. You should have configured the evaluation to use the travel evaluation dataset to evaluate the **gpt-4o-mini** model for semantic similarity, relevance, F1 score, and hateful and unfair language. 128 | 1. Give the evaluation a suitable name, and **Submit** it to start the evaluation process, and wait for it to complete. It may take a few minutes. You can use the **Refresh** toolbar button to check the status. 129 | 130 | 1. When the evaluation has completed, scroll down if necessary to review the results. 131 | 132 | ![Screenshot of evalation metrics.](./media/ai-quality-metrics.png) 133 | 134 | 1. At the top of the page, select the **Data** tab to see the raw data from the evaluation. The data includes the metrics for each input as well as explanations of the reasoning the gpt-4o model applied when assessing the responses. 135 | 136 | ## Clean up 137 | 138 | When you finish exploring the Azure AI Foundry, you should delete the resources you’ve created to avoid unnecessary Azure costs. 139 | 140 | - Navigate to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`. 141 | - In the Azure portal, on the **Home** page, select **Resource groups**. 142 | - Select the resource group that you created for this exercise. 143 | - At the top of the **Overview** page for your resource group, select **Delete resource group**. 144 | - Enter the resource group name to confirm you want to delete it, and select **Delete**. 145 | -------------------------------------------------------------------------------- /Instructions/archive/03b-Use-prompt-flow-NER.md: -------------------------------------------------------------------------------- 1 | # Use prompt flow for Named Entity Recognition (NER) 2 | 3 | Extracting valuable information from text is known as Named Entity Recognition (NER). Entities are key words that are of interest to you in a given text. 4 | 5 | ![Entity extraction](./media/get-started-prompt-flow-use-case.gif) 6 | 7 | Large Language Models (LLMs) can be used to perform NER. To create an application that takes a text as input and outputs entities, you can create a flow that uses a LLM node with prompt flow. 8 | 9 | In this exercise, you'll use Azure AI Foundry portal's prompt flow to create an LLM application that expects an entity type and text as input. It calls a GPT model from Azure OpenAI through a LLM node to extract the required entity from the given text, cleans the result and outputs the extracted entities. 10 | 11 | ![Exercise overview](./media/get-started-lab.png) 12 | 13 | You first need to create a project in the Azure AI Foundry portal to create the necessary Azure resources. Then, you can deploy a GPT model with the Azure OpenAI service. Once you have the necessary resources, you can create the flow. Finally you'll run the flow to test it and view the sample output. 14 | 15 | ## Create a project in the Azure AI Foundry portal 16 | 17 | You start by creating an Azure AI Foundry portal project and an Azure AI Hub to support it. 18 | 19 | 1. In a web browser, open [https://ai.azure.com](https://ai.azure.com) and sign in using your Azure credentials. 20 | 1. In the home page, select **+ Create project**. 21 | 1. In the **Create a project** wizard you can see all the Azure resources that will be automatically created with your project, or you can customize the following settings by selecting **Customize** before selecting **Create**: 22 | 23 | - **Project name**: *A unique name for your project* 24 | - **Hub**: *Create a new hub with the following settings:* 25 | - **Hub name**: *A unique name* 26 | - **Subscription**: *Your Azure subscription* 27 | - **Resource group**: *A new resource group* 28 | - **Location**: Select **Help me choose** and then select **gpt-4** in the Location helper window and use the recommended region\* 29 | - **Connect Azure AI Services or Azure OpenAI**: (New) *Autofills with your selected hub name* 30 | - **Connect Azure AI Search**: Skip connecting 31 | 32 | > \* Azure OpenAI resources are constrained at the tenant level by regional quotas. The listed regions in the location helper include default quota for the model type(s) used in this exercise. Randomly choosing a region reduces the risk of a single region reaching its quota limit. In the event of a quota limit being reached later in the exercise, there's a possibility you may need to create another resource in a different region. Learn more about [model availability per region](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#availability) 33 | 34 | 1. If you selected **Customize**, select **Next** and review your configuration. 35 | 1. Select **Create** and wait for the process to complete. 36 | 37 | ## Deploy a GPT model 38 | 39 | To use a LLM model in prompt flow, you need to deploy a model first. The Azure AI Foundry portal allows you to deploy OpenAI models that you can use in your flows. 40 | 41 | 1. In the navigation pane on the left, under **My assets**, select the **Models + endpoints** page. 42 | 1. Select **+ Deploy model** and **Deploy base model**. 43 | 1. Create a new deployment of the **gpt-4** model with the following settings by selecting **Customize** in the deployment details: 44 | 45 | - **Deployment name**: *A unique name for your model deployment* 46 | - **Deployment type**: Standard 47 | - **Model version**: *Select the default version* 48 | - **AI resource**: *Select the resource created previously* 49 | - **Tokens per Minute Rate Limit (thousands)**: 5K 50 | - **Content filter**: DefaultV2 51 | - **Enable dynamic quota**: Disabled 52 | 53 | Now that you have your language model deployed, you can create a flow in Azure AI Foundry portal that calls the deployed model. 54 | 55 | ## Create and run a flow in the Azure AI Foundry portal 56 | 57 | Now that you have all necessary resources provisioned, you can create a flow. 58 | 59 | ### Create a new flow 60 | 61 | To create a new flow with a template, you can select one of the types of flows you want to develop. 62 | 63 | 1. In the navigation pane on the left, under **Build and customize**, select **Prompt flow**. 64 | 1. Select **+ Create** to create a new flow. 65 | 1. Create a new **Standard flow** and enter `entity-recognition` as folder name. 66 | 67 |
68 | Troubleshooting tip: Permissions error 69 |

If you receive a permissions error when you create a new prompt flow, try the following to troubleshoot:

70 |
    71 |
  • In the Azure portal, select the AI Services resource.
  • 72 |
  • Under Resource Management, in the Identity tab, confirm that it is system assigned managed identity.
  • 73 |
  • Navigate to the associated Storage Account. On the IAM page, add role assignment Storage blob data reader.
  • 74 |
  • Under Assign access to, choose Managed Identity, + Select members, and select the All system-assigned managed identities.
  • 75 |
  • Review and assign to save the new settings and retry the previous step.
  • 76 |
77 |
78 | 79 | A standard flow with one input, two nodes, and one output is created for you. You'll update the flow to take two inputs, extract entities, clean up the output from the LLM node, and return the entities as output. 80 | 81 | ### Start the automatic runtime 82 | 83 | To test your flow, you need compute. The necessary compute is made available to you through the runtime. 84 | 85 | 1. After creating the new flow that you named `entity-recognition`, the flow should open in the studio. 86 | 1. Select **Start compute session** from the top bar. 87 | 1. The compute session will take 1-3 minutes to start. 88 | 89 | ### Configure the inputs 90 | 91 | The flow you'll create will take two inputs: a text and the type of entity you want to extract from the text. 92 | 93 | 1. Under **Inputs**, one input is configured named `topic` of type `string`. Change the existing input and update with the following settings: 94 | - **Name**: `entity_type` 95 | - **Type**: `string` 96 | - **Value**: `job title` 97 | 1. Select **Add input**. 98 | 1. Configure the second input to have the following settings: 99 | - **Name**: `text` 100 | - **Type**: `string` 101 | - **Value**: `The software engineer is working on a new update for the application.` 102 | 103 | ### Configure the LLM node 104 | 105 | The standard flow already includes a node that uses the LLM tool. You can find the node in your flow overview. The default prompt asks for a joke. You'll update the LLM node to extract entities based on the two inputs specified in the previous section. 106 | 107 | 1. Navigate to the **LLM node** named `joke`. 108 | 1. Replace the name with `NER_LLM` 109 | 1. For **Connection**, select the connection that was created for you when you created the AI hub. 110 | 1. For **deployment_name**, select the `gpt-4` model you deployed. 111 | 1. Replace the prompt field with the following code: 112 | 113 | ```yml 114 | system: 115 | 116 | Your task is to find entities of a certain type from the given text content. 117 | If there're multiple entities, please return them all with comma separated, e.g. "entity1, entity2, entity3". 118 | You should only return the entity list, nothing else. 119 | If there's no such entity, please return "None". 120 | 121 | user: 122 | 123 | Entity type: {{entity_type}} 124 | Text content: {{text}} 125 | 126 | Entities: 127 | ``` 128 | 129 | 1. Select **Validate and parse input**. 130 | 1. Within the LLM node, in the **Inputs** section, configure the following: 131 | - For `entity_type`, select the value `${inputs.entity_type}`. 132 | - For `text`, select the value `${inputs.text}`. 133 | 134 | Your LLM node will now take the entity type and text as inputs, include it in the prompt you specified and send the request to your deployed model. 135 | 136 | ### Configure the Python node 137 | 138 | To extract only the key information from the result of the model, you can use the Python tool to clean up the output of the LLM node. 139 | 140 | 1. Navigate to the Python node named `echo`. 141 | 1. Replace the name with `cleansing`. 142 | 1. Replace the code with the following: 143 | 144 | ```python 145 | from typing import List 146 | from promptflow import tool 147 | 148 | 149 | @tool 150 | def cleansing(entities_str: str) -> List[str]: 151 | # Split, remove leading and trailing spaces/tabs/dots 152 | parts = entities_str.split(",") 153 | cleaned_parts = [part.strip(" \t.\"") for part in parts] 154 | entities = [part for part in cleaned_parts if len(part) > 0] 155 | return entities 156 | 157 | ``` 158 | 159 | 1. Select **Validate and parse input**. 160 | 1. Within the Python node, in the **Inputs** section, set the value of `entities_str` to `${NER_LLM.output}`. 161 | 162 | ### Configure the output 163 | 164 | Finally, you can configure the output of the whole flow. You only want one output to your flow, which should be the extracted entities. 165 | 166 | 1. Navigate to the flow's **Outputs**. 167 | 1. For **Name**, enter `entities`. 168 | 1. For **Value**, select `${cleansing.output}`. 169 | 170 | ### Run the flow 171 | 172 | Now that you've developed the flow, you can run it to test it. Since you've added default values to the inputs, you can easily test the flow in the studio. 173 | 174 | 1. Select **Run** to test the flow. 175 | 1. Wait until the run is completed. 176 | 1. Select **View outputs**. A pop-up should appear showing you the output for the default inputs. Optionally, you can also inspect the logs. 177 | 178 | ## Delete Azure resources 179 | 180 | When you finish exploring the Azure AI Foundry portal, you should delete the resources you’ve created to avoid unnecessary Azure costs. 181 | 182 | - Navigate to the [Azure portal](https://portal.azure.com) at `https://portal.azure.com`. 183 | - In the Azure portal, on the **Home** page, select **Resource groups**. 184 | - Select the resource group that you created for this exercise. 185 | - At the top of the **Overview** page for your resource group, select **Delete resource group**. 186 | - Enter the resource group name to confirm you want to delete it, and select **Delete**. 187 | -------------------------------------------------------------------------------- /Instructions/archive/08-Code-first-development.md: -------------------------------------------------------------------------------- 1 | # Build a generative AI app using code-first development tools 2 | 3 | In this exercise, you'll clone and deploy an Azure Developer CLI template that provisions and [deploys your AI project to an online endpoint](https://learn.microsoft.com/azure/developer/azure-developer-cli/azure-ai-ml-endpoints?WT.mc_id=academic-140829-cacaste) on Azure AI Foundry. You'll then use it as a starting point to build your own custom copilot with Azure AI and a code-first experience. 4 | 5 | This exercise will take approximately **90** minutes. 6 | 7 | ## Before you start 8 | 9 | To complete this exercise, you'll need: 10 | 11 | - A GitHub account to fork the project repository and test it in a GitHub Codespaces environment. Create a free account [on GitHub](https://github.com/). 12 | - The basic tier of Azure AI Search to activate Semantic Ranker. Learn more about the [pricing details of AI Search](https://azure.microsoft.com/pricing/details/search/). 13 | - To deploy three OpenAI models (`gpt-35-turbo`, `gpt-4`, `text-embedding-ada-002`). To be able to deploy the models, you need to create the AI hub in a region with sufficient quota. Learn more about [model region availability](https://learn.microsoft.com/azure/ai-services/openai/concepts/models?WT.mc_id=academic-140829-cacaste#model-summary-table-and-region-availability). 14 | 15 | ## Understand the scenario 16 | 17 | To get started with the Azure Developer CLI AI project template, navigate to the [Azure AI Templates with Azure Developer CLI collection](https://learn.microsoft.com/collections/5pq0uompdgje8d/?WT.mc_id=academic-140829-cacaste). By exploring the collection, you can find several projects grouped by technology and use case, including multi-modal and multi-agent projects samples, copilot-like projects and samples integrating different frameworks and Azure services. 18 | 19 | For this exercise, you'll take the **[Contoso Chat Retail copilot with Azure AI Foundry & PromptFlow (Python)](https://aka.ms/contoso-retail-sample)** project template as your starting point. This project template is a code-first experience that uses Prompty and PromptFlow to build a custom copilot (chat AI) that can be integrated into the retail website (chat UI) of a fictional company called Contoso Outdoors. 20 | 21 | ![Contoso Chat UI/UX](./media/contoso_outdoors_website.png) 22 | 23 | The retail copilot solution uses a Retrieval Augmented Generation (RAG) pattern to ground responses in the company's product and customer data. Customers can ask the retail chatbot questions about the company's product catalog, and also get recommendations based on their prior purchases. 24 | 25 | By selecting the project link included in the collection, you'll be redirected to the GitHub repository hosting the template code. The [README.md](https://github.com/Azure-Samples/contoso-chat/blob/main/README.md) file in the repository provides a detailed description of the project, including the architecture, the prerequisites, and the steps to deploy the project. 26 | 27 | ![Contoso Chat Architecture](./media/contoso_chat_architecture.png) 28 | 29 | ## Set up GitHub Codespaces 30 | 31 | In this exercise you'll use [GitHub Codespaces](https://github.com/features/codespaces), a GitHub feature that lets you launch a pre-configured cloud-hosted [development container](https://docs.github.com/codespaces/setting-up-your-project-for-codespaces/adding-a-dev-container-configuration/introduction-to-dev-containers) directly from your repository, with one click. This way, you can quickly start coding without having to set up your local development environment, since the Codespaces already has all necessary tools and dependencies pre-installed. 32 | 33 | To initialize your development environment, follow the steps below: 34 | 35 | 1. Navigate to the **contoso-chat** repository: `https://github.com/Azure-Samples/contoso-chat` 36 | 1. Select the **Fork** button in the top right corner of the contoso-chat GitHub repository page to create a copy of the repository in your GitHub account. 37 | 1. Once you have your forked repository, select the **Code** button and select **Codespaces**. 38 | 1. Select the **+** button to create a new codespace on the main branch of your forked repository. 39 | 40 | ![Create GitHub Codespaces](./media/create_codespaces.png) 41 | 42 | 1. In a few seconds, you're redirected to a new browser tab where the Codespaces environment is setup with an [attached Visual Studio Code editor](https://code.visualstudio.com/docs/devcontainers/containers) by default. 43 | 44 | ## Connect VS Code environment to Azure 45 | 46 | The next step is connecting your development environment with the Azure subscription where you'd like to deploy the project. Start by opening a new terminal in your VS Code environment running in Codespaces. 47 | 48 | 1. First, verify that the [latest version](https://github.com/Azure/azure-dev/releases/tag/azure-dev-cli_1.9.3) of Azure Developer CLI is installed. 49 | ```bash 50 | azd version 51 | ``` 52 | 53 | 1. Next, sign in into your Azure Account from the VS Code terminal. 54 | 55 | ```bash 56 | azd auth login 57 | ``` 58 | 59 | ## Provision Azure resources for your project 60 | 61 | Once you are logged in, you are ready to start provisioning the Azure resources for the project in your subscription. You can do that in the same VS Code terminal you used for login. 62 | 63 | 1. Provision *and deploy* your AI application using azd. 64 | 65 | ```bash 66 | azd up 67 | ``` 68 | 69 | 1. You should see the following prompts. Respond using the guidance below: 70 | - **Enter a new environment name:** *Used to create your resource group name*. 71 | - **Select an Azure Subscription to use**: *Select a subscription that has access to Azure OpenAI models*. 72 | - **Select an Azure location to use**: *Select a location with model quota available*. 73 | 74 | > Use the [model summary table and region availability](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?WT.mc_id=academic-140829-cacaste#model-summary-table-and-region-availability) to find the region you need. For example, you can use `sweden central` as the Azure location since it is the region where the majority of the Azure OpenAI models are available. 75 | 76 | ## Validate provisioning by using Azure Portal 77 | 78 | Provisioning and deploying an AI application using azd can take 10 minutes or more to complete. You can track progress by: 79 | 80 | - Viewing detailed progress in the [Azure Portal](https://ms.portal.azure.com/). Search for the resource group corresponding to your environment name. Select the **Deployments** option in the sidebar, then monitor the deployment status of the resources being created. 81 | - Visiting the [Azure AI Foundry portal](https://ai.azure.com) portal. Sign in using your Azure account. Search for the AI hub corresponding to the resource group above (you may need to refresh a few times). Select the listed AI project, then select **Deployments** in its sidebar to track status for models and chat application deployments. 82 | 83 | Let's explore how to validate the provisioning of resources using the Azure Portal. 84 | 85 | 1. Navigate to the [Azure Portal](https://ms.portal.azure.com/) in the browser. 86 | 1. Sign in and find the resource group corresponding to the subscription and environment name you entered earlier. The **Overview** panel should look like this: 87 | 88 | ![Azure Portal resource group overview](./media/azure-portal-resource-group.png) 89 | 90 | 1. Let's start by verifying that the key [Azure AI Foundry architecture](https://learn.microsoft.com/azure/ai-studio/concepts/architecture) resources were created. The figure below provides more details on what each of these resources provides to our AI application. 91 | 92 | - **Azure AI hub**: Top-level Azure resource. Provides a collaboration environment for teams. 93 | - **Azure AI project**: Child of hub. Groups app components for orchestration, customization. 94 | - **Azure AI services**: Manages your model endpoints. 95 | 96 | ![Azure AI Foundry architecture](./media/resource-provider-connected-resources.svg) 97 | 98 | 1. Next, let's verify that we provisioned two key resources for implementing our [Retrieval Augmented Generation](https://learn.microsoft.com/azure/ai-studio/concepts/retrieval-augmented-generation) design pattern by storing the product and customer data for query-driven retrieval. 99 | 100 | - **Search service**: To manage search indexes for our product catalog data. 101 | - **Azure Cosmos DB account**: To create a database for our customer order data. 102 | 103 | 1. Next, we can validate that we have supporting resources for managing our AI application needs: 104 | 105 | - **Application Insights**: To support monitoring and telemetry for the deployed application. 106 | - **Container Registry**: To store and manage Docker images used in the project, privately. 107 | - **Key vault**: To store project secrets (keys, credentials) securely. 108 | - **Storage account**: To store data related to AI project management (including logs). 109 | - **Smart detector alert rule**: Application Insights anomaly detector (for requests). 110 | 111 | 1. Last but not least, you'll notice a new resource with type **Machine learning online deployment**. This is the resource corresponding to our deployed Azure AI project endpoint (for the chat copilot). 112 | 113 | ## Validate deployment using the Azure AI Foundry 114 | 115 | The Azure Portal helps you manage the underlying Azure resources for your project. The Azure AI Foundry portal helps you *build and manage* the AI projects themselves, end-to-end, from model selection to application deployment. The `azd up` command should have completed the entire process from provisioning required models, to deploying and hosting the copilot API endpoint for usage. Let's validate that the application is functioning as expected. 116 | 117 | 1. Visit the **Manage** page in the [Azure AI Foundry portal](https://ai.azure.com/manage) to view all Azure AI hubs in your subscription. 118 | 1. Select the hub for your resource group to view all Azure AI projects within it. 119 | 1. Select the default AI project in hub, then select **Deployments** in the menu on the left. 120 | 1. Under **Model deployments**, verify that you have an Azure OpenAI Connection including the deployments of: 121 | - **gpt-35-turbo**: Used for chat completion, forming the core chat engine. 122 | - **gpt-4**: Used for chat evaluation, specifically AI-assisted flows. 123 | - **text-embedding-ada-002**: Used for query vectorization & search. 124 | 1. Verify that you have an machine learning online endpoint with: 125 | - **chat-model**: Chat AI deployment with *mloe-xxx* endpoint resource. 126 | 127 | ![Azure AI Project Deployments](./media/azure-ai-project-deployment.png) 128 | 129 | ## Test the deployment (in the cloud) using Azure AI Foundry 130 | 131 | To validate that the deployed copilot works, use the built-in test playground capability in the Azure AI Foundry portal. 132 | 133 | ![Chat deployment details](./media/chat-deployment-details.png) 134 | 135 | 1. In the Azure AI Foundry portal, from the **App deployments** list, select the **chat-deployment-xxxx** deployment. 136 | 1. On the **Details** page of the deployed chat application, select the **Test** tab to get the test interface. 137 | 138 | Note that the **Details** tab also has `Target URI` and `Key` values that you can use with other front-end applications (for example the Contoso Outdoor website) to integrate this chat assistant for real-world user interactions. 139 | 140 | 1. For now, test the copilot deployment with the following test **Input**: 141 | 142 | ```bash 143 | {"question": "tell me about your hiking shoes", "customerId": "2", "chat_history": []} 144 | ``` 145 | 146 | You should get a valid JSON response in the output component as shown. 147 | 148 | ![Chat deployment test](./media/chat-deployment-test.png) 149 | 150 | ## Test the deployment (locally) using Visual Studio Code 151 | 152 | The **azd up** command not only provisions and deploys the application to Azure, it also *configures your local environment* in Visual Studio Code to support local development, testing, and iteration. Let's check this out. 153 | 154 | 1. First, validate that your VS Code environment was setup correctly. Search for a **config.json** file in the root folder and verify that it has the three properties defined below, with valid values. 155 | 156 | ```json 157 | { 158 | "subscription_id": "xxxxxxxxxxxxxxxx", 159 | "resource_group": "rg-xxxxxx", 160 | "workspace_name": "ai-project-xxxxxxx" 161 | } 162 | 163 | ``` 164 | 165 | 1. Verify that a **.env** file was created in your root folder. It should contain a list of environment variables *with values filled in*. 166 | 167 | ```bash 168 | AZUREAI_HUB_NAME= 169 | AZUREAI_PROJECT_NAME= 170 | AZURE_CONTAINER_REGISTRY_ENDPOINT= 171 | AZURE_CONTAINER_REGISTRY_NAME= 172 | AZURE_COSMOS_NAME= 173 | AZURE_ENV_NAME= 174 | AZURE_KEY_VAULT_ENDPOINT= 175 | AZURE_KEY_VAULT_NAME= 176 | AZURE_LOCATION= 177 | AZURE_OPENAI_API_VERSION= 178 | AZURE_OPENAI_CHAT_DEPLOYMENT= 179 | AZURE_OPENAI_ENDPOINT= 180 | AZURE_OPENAI_NAME= 181 | AZURE_RESOURCE_GROUP= 182 | AZURE_SEARCH_ENDPOINT= 183 | AZURE_SEARCH_NAME= 184 | AZURE_SUBSCRIPTION_ID= 185 | AZURE_TENANT_ID= 186 | COSMOS_ENDPOINT= 187 | ``` 188 | 189 | 1. Verify that you have the **Promptflow tools** installed in your development environment. 190 | 191 | ```bash 192 | pf version 193 | ``` 194 | 195 | 1. Use the **pf flow test** tool to test the **contoso_chat** flex flow application locally, with the sample question below. Note the syntax of the command for passing the inputs: 196 | 197 | ```bash 198 | pf flow test --flow ./contoso_chat --inputs question="tell me about your jackets" customerId="3" chat_history=[] 199 | ``` 200 | 201 | You should receive a response like this: 202 | 203 | ![Example App Output](./media/example_app_output.png) 204 | 205 | ### View traces (locally) using Visual Studio Code 206 | 207 | 1. You can trace the details of your execution with the `--ui` flag as shown below. 208 | 209 | ```bash 210 | pf flow test --flow ./contoso_chat --inputs question="tell me about your jackets" customerId="3" chat_history=[] --ui 211 | ``` 212 | 213 | This command should launch a **trace view** in your browser (in a new tab) with a table that provides high-level details about that test run including the latency and tokens usage. 214 | 215 | ![pf test row](./media/pf-flow-test-row.png) 216 | 217 | 1. Select the record to expand into a more detail trace view that lets you inspect the finer details of the flow - from the raw data (input, output) to the individual steps of the flow and the relevant components (for example, the prompt templates used for LLM). 218 | 219 | ![pf test detail](./media/pf-flow-test-detail.png) 220 | 221 | ## Understand the Contoso Chat codebase 222 | 223 | Your Azure backend is provisioned and ready. Your local development environment is setup and configured to work with your Azure backend. Now, all you need to do is start modifying the contents to customize and redeploy your own version of the application. Let's take a quick look at how the codebase is structured. 224 | 225 | > This is a **simplified listing** of the repository, eliminating some files and folders for clarity. 226 | 227 | ```bash 228 | data/ 229 | customer_info/ 230 | create-cosmos-db.ipynb # Run notebook to upload data to Cosmos DB 231 | customer_info_1.json # Example Customer info and orders file 232 | customer_info_2.json 233 | ... 234 | ... 235 | product_info/ 236 | create-azure-search.ipynb # Run notebook to index product data in AI Search 237 | products.csv # Example Products data file 238 | 239 | contoso_chat/ # Main folder for application content 240 | ai_search.py # Search retrieval tool (for RAG design) 241 | chat.json # Example chat file (for Prompty template) 242 | chat.prompty # Chat asset (using Prompty format) 243 | chat_request.py # LLM request tool (for chat completion) 244 | flow.flex.yaml # Promptflow flex flow (define entry point) 245 | requirements.txt # App dependencies (define runtime environment) 246 | 247 | azure.yaml # Main configuration file for Azure Developer CLI 248 | infra/ 249 | ai.yaml # Define AI model deployments 250 | app/ # Infrastructure-as-code config specific to app 251 | core/ # Infrastructure-as-code config for core resources 252 | hooks/ # Contains post-provisioning scripts 253 | main.bicep # Entry point for Bicep template used by azd 254 | deployment/ # ai.endpoint config files (named in azure.yaml) 255 | chat-deployment.yaml 256 | chat-model.yaml 257 | environment.yaml 258 | 259 | requirements.txt 260 | ``` 261 | 262 | When you want to customize the code: 263 | 264 | - If you make app changes (in `contoso_chat/`) simply run `azd deploy` to redeploy the application to the previously provisioned backend. No additional re-provisioning or manual intervention steps required. 265 | - If you make resource changes (in `infra/` folder) then run `azd up` to re-provision and redeploy the application. It should automatically pick up your prior configuration values from `.azure/` and modify them. 266 | 267 | ## Optional: Customize and redeploy the copilot 268 | 269 | It's time to build your own custom copilot. Here are some things you can explore, to try this out. 270 | 271 | Remember for each of these options: 272 | 273 | - Use `azd deploy` to redeploy your application if you changed only the app code. 274 | - Use `azd up` to re-provision and redeploy application if you changed resource configuration. 275 | 276 | ### Customize the customer and order history data 277 | 278 | 1. Review the sample data under **data/customer_info** for a sense of the default schema. 279 | 1. Explore the **data/create-cosmos-db.ipynb** notebook for a code-first approach to data updates. 280 | 1. **Modify** the sample data and **run** the notebook to change the default Azure CosmosDB database. 281 | 1. **Redeploy** the app. Try a test question to validate that new customer data is returned. 282 | 283 | ### Customize the product catalog data 284 | 285 | 1. Review the sample data under **data/product_info/** for a sense of the default schema. 286 | 1. Explore the **create-azure-search.ipynb** notebook for a code-first approach to index updates. 287 | 1. **Modify** the sample data and **run** the notebook to change the default Azure AI Search indexes. 288 | 1. **Redeploy** the app. Try a test question to validate that new product data is returned. 289 | 290 | ### Customize the prompt template 291 | 292 | 1. Review the **contoso_chat/chat.prompty** file for a sense of the default prompt template. 293 | 1. Review the **contoso_chat/chat.json** to understand the sample data schema for testing. 294 | 1. **Modify** the template (system message, safety, documentation or instructions). 295 | 1. **Modify** the example data if needed. 296 | 1. **Use** the Promptflow CLI to test the flow locally with the new prompt template. 297 | 1. **Install and use** the Prompty extension to create a new prompt template from scratch. 298 | 299 | ### Explore evaluation and pipeline automation 300 | 301 | Replace the test dataset used to run the app evaluation pipeline through GitHub Actions with your own data. The test dataset is located in the **data** folder of the project, and it is in a **.jsonl** format. 302 | 303 | 1. Replace the test dataset file with your own data. 304 | 1. Then, run the evaluation pipeline by pushing the changes to the main branch of your forked repository. 305 | 306 | The evaluation pipeline will run automatically, and you can check the results in the GitHub Actions tab of your repository. 307 | 308 | 1. You can customize the evaluation pipeline by modifying the **evaluate.yaml** file in the **.github/workflows** folder of the project and the **evaluations_chat.py** script in the **evaluations** folder. 309 | 310 | ## Clean up and delete Azure resources 311 | 312 | This project uses models and services (for example Azure AI Search) that can incur non-trivial costs if left running long-term. When you finish exploring this Azure AI AZD template, you should delete the resources you’ve created to avoid unnecessary Azure costs. 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-------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Sidney Andrews 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /_build.yml: -------------------------------------------------------------------------------- 1 | name: '$(Date:yyyyMMdd)$(Rev:.rr)' 2 | jobs: 3 | - job: build_markdown_content 4 | displayName: 'Build Markdown Content' 5 | workspace: 6 | clean: all 7 | pool: 8 | vmImage: 'Ubuntu 16.04' 9 | container: 10 | image: 'microsoftlearning/markdown-build:latest' 11 | steps: 12 | - task: Bash@3 13 | displayName: 'Build Content' 14 | inputs: 15 | targetType: inline 16 | script: | 17 | cp /{attribution.md,template.docx,package.json,package.js} . 18 | npm install 19 | node package.js --version $(Build.BuildNumber) 20 | - task: GitHubRelease@0 21 | displayName: 'Create GitHub Release' 22 | inputs: 23 | gitHubConnection: 'github-microsoftlearning-organization' 24 | repositoryName: '$(Build.Repository.Name)' 25 | tagSource: manual 26 | tag: 'v$(Build.BuildNumber)' 27 | title: 'Version $(Build.BuildNumber)' 28 | releaseNotesSource: input 29 | releaseNotes: '# Version $(Build.BuildNumber) Release' 30 | assets: '$(Build.SourcesDirectory)/out/*.zip' 31 | assetUploadMode: replace 32 | - task: PublishBuildArtifacts@1 33 | displayName: 'Publish Output Files' 34 | inputs: 35 | pathtoPublish: '$(Build.SourcesDirectory)/out/' 36 | artifactName: 'Lab Files' 37 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | remote_theme: MicrosoftLearning/Jekyll-Theme 2 | exclude: 3 | - readme.md 4 | - .github/ 5 | header_pages: 6 | - index.html 7 | author: Microsoft Learning 8 | twitter_username: mslearning 9 | github_username: MicrosoftLearning 10 | plugins: 11 | - jekyll-sitemap 12 | - jekyll-mentions 13 | - jemoji 14 | markdown: kramdown 15 | kramdown: 16 | syntax_highlighter_opts: 17 | disable : true 18 | title: Develop generative AI solutions in Azure 19 | -------------------------------------------------------------------------------- /data/brochures.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/9422b1aff98bbef4408435f89970fa183753baf9/data/brochures.zip -------------------------------------------------------------------------------- /data/travel-finetune-hotel.jsonl: -------------------------------------------------------------------------------- 1 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "What's a must-see in Paris?"}, {"role": "assistant", "content": "Oh la la! You simply must twirl around the Eiffel Tower and snap a chic selfie! After that, consider visiting the Louvre Museum to see the Mona Lisa and other masterpieces. What type of attractions are you most interested in?"}]} 2 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "Can you recommend a beach destination that's not too crowded?"}, {"role": "assistant", "content": "Absolutely! Have you heard of the hidden gem, Cala Gonone in Sardinia? It's like having your own slice of paradise without the crowds. You can enjoy crystal-clear waters, explore nearby caves, and even go hiking in the surrounding mountains. Is that the kind of destination you're looking for?"}]} 3 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "I love history, where should I go on my next trip?"}, {"role": "assistant", "content": "History buff, eh? Time travel to Rome and walk in the footsteps of gladiators at the Colosseum! You can also explore the Roman Forum and the Pantheon. Or would you prefer to unravel the mysteries of the pyramids in Egypt and visit the ancient temples of Luxor? Do you have a preference for a specific region?"}]} 4 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "I'm looking for a culinary adventure, any suggestions?"}, {"role": "assistant", "content": "Get ready to spice up your life in Bangkok with a street food tour! You can try delicious dishes like Pad Thai, Som Tum, and Mango Sticky Rice. Or, if you're feeling fancy, why not a sushi-making class in Tokyo? You can learn to make sushi from a master chef and enjoy your creations. Which one sounds more exciting to you?"}]} 5 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "What's the best way to see the Northern Lights?"}, {"role": "assistant", "content": "For the most magical light show, head to Tromsø in Norway! You can join a guided tour to increase your chances of seeing the aurora. Want to add a dash of adventure? How about a husky sled ride under the aurora? It’s an unforgettable experience. What do you think?"}]} 6 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "What's the best way to experience the Amazon Rainforest?"}, {"role": "assistant", "content": "The Amazon is calling! A river cruise is a fantastic way to immerse yourself in the jungle's heart. You can explore the diverse wildlife, visit indigenous communities, and learn about the ecosystem. How about a guided eco-tour to spot some exotic wildlife? Is that something you'd be interested in?"}]} 7 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "Where can I find the best street food in Asia?"}, {"role": "assistant", "content": "Street food? Yum! Head to the bustling markets of Bangkok or the hawker centers in Singapore. You can try a variety of dishes like Satay, Laksa, and Hainanese Chicken Rice. Ready to tantalize your taste buds? Which destination are you leaning towards?"}]} 8 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "I'm interested in scuba diving. Where's the best place to go?"}, {"role": "assistant", "content": "Dive into adventure in the Great Barrier Reef or the crystal-clear waters of the Maldives. You can explore vibrant coral reefs, swim with colorful fish, and even spot some sea turtles. Ready to explore the underwater world? Which destination excites you more?"}]} 9 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "I want to see wildlife on my vacation, any suggestions?"}, {"role": "assistant", "content": "For a wild time, consider a safari in the Serengeti or a visit to the Galápagos Islands. You can see lions, elephants, and giraffes in their natural habitat or meet unique species like giant tortoises and marine iguanas. Ready to meet some amazing animals? Which adventure appeals to you?"}]} 10 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."}, {"role": "user", "content": "Can you tell me about quantum computing?"}, {"role": "assistant", "content": "Quantum computing is quite the complex topic, but you know what's easy? Planning a fantastic getaway! How about we dive into some travel destinations that might pique your interest instead? Are you looking for adventure, relaxation, or cultural experiences?"}]} -------------------------------------------------------------------------------- /data/travel-finetune-validation.jsonl: -------------------------------------------------------------------------------- 1 | {"messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "Can you provide a list of the top-rated budget hotels in Rome?"}]} 2 | {"messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "I'm looking for a vegan-friendly restaurant in New York City. Can you help?"}]} 3 | {"messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "Can you suggest a 7-day itinerary for a family vacation in Orlando, Florida?"}]} 4 | {"messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "Can you help me plan a surprise honeymoon trip to the Maldives"}]} 5 | {"messages": [{"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": "Are there any guided tours available for the Great Wall of China?"}]} -------------------------------------------------------------------------------- /data/travel-finetune.jsonl: -------------------------------------------------------------------------------- 1 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "What's a must-see in Paris?"}, {"role": "assistant", "content": "Oh la la! You simply must twirl around the Eiffel Tower and snap a chic selfie! Want to feel like royalty? How about a visit to the grand Palace of Versailles next?"}]} 2 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "Can you recommend a beach destination that's not too crowded?"}, {"role": "assistant", "content": "Absolutely! Have you heard of the hidden gem, Cala Gonone in Sardinia? It's like having your own slice of paradise without the crowds. Ready to soak up the sun in peace?"}]} 3 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "I love history, where should I go on my next trip?"}, {"role": "assistant", "content": "History buff, eh? Time travel to Rome and walk in the footsteps of gladiators at the Colosseum! Or would you prefer to unravel the mysteries of the pyramids in Egypt?"}]} 4 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "I'm looking for a culinary adventure, any suggestions?"}, {"role": "assistant", "content": "Get ready to spice up your life in Bangkok with a street food tour! Or, if you're feeling fancy, why not a sushi-making class in Tokyo? Chopsticks at the ready!"}]} 5 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "What's the best way to see the Northern Lights?"}, {"role": "assistant", "content": "For the most magical light show, head to Tromsø in Norway! Want to add a dash of adventure? How about a husky sled ride under the aurora?"}]} 6 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "What's the best way to experience the Amazon Rainforest?"}, {"role": "assistant", "content": "The Amazon is calling! A river cruise is a fantastic way to immerse yourself in the jungle's heart. How about a guided eco-tour to spot some exotic wildlife?"}]} 7 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "Where can I find the best street food in Asia?"}, {"role": "assistant", "content": "Street food? Yum! Head to the bustling markets of Bangkok or the hawker centers in Singapore. Ready to tantalize your taste buds?"}]} 8 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "I'm interested in scuba diving. Where's the best place to go?"}, {"role": "assistant", "content": "Dive into adventure in the Great Barrier Reef or the crystal-clear waters of the Maldives. Ready to explore the underwater world?"}]} 9 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "I want to see wildlife on my vacation, any suggestions?"}, {"role": "assistant", "content": "For a wild time, consider a safari in the Serengeti or a visit to the Galápagos Islands. Ready to meet some amazing animals?"}]} 10 | {"messages": [{"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You're helpful, friendly, and engaging. You do not provide answers not related to travel."}, {"role": "user", "content": "Can you tell me about quantum computing?"}, {"role": "assistant", "content": "Quantum computing is quite the complex topic, but you know what's easy? Planning a fantastic getaway! How about we dive into some travel destinations that might pique your interest instead?"}]} -------------------------------------------------------------------------------- /data/travel-qa.jsonl: -------------------------------------------------------------------------------- 1 | {"query":"Can you provide a list of the top-rated budget hotels in Rome?", "response":"Absolutely! When it comes to budget accommodations in Rome, there are several highly-rated options to consider. Here are a few top-rated budget hotels that are known for providing great value for money: 1. Hotel Artemide 2. Hotel Santa Maria 3. Hotel Quirinale 4. Hotel Delle Nazioni 5. Hotel Forum These hotels offer comfortable rooms, convenient locations, and great amenities at affordable prices. Is there anything specific you're looking for in a budget hotel, such as location or amenities? Let me know if you'd like more details about any of these options or if you need further assistance!"} 2 | {"query": "I'm looking for a vegan-friendly restaurant in New York City. Can you help?", "response":"Of course! New York City is a fantastic place for vegan dining. There are numerous restaurants offering delicious plant-based options. Are you looking for something in a specific neighborhood or with a particular type of cuisine?"} 3 | {"query": "Can you suggest a 7-day itinerary for a family vacation in Orlando, Florida?", "response":"Of course! A 7-day family vacation in Orlando, Florida can be packed with exciting activities. Here's a suggested itinerary: **Day 1:** Arrival in Orlando. Check into your accommodation and take some time to relax after your journey. **Day 2:** Visit Walt Disney World Resort. Explore Magic Kingdom and enjoy classic attractions like Space Mountain and It's a Small World. **Day 3:** Spend the day at Universal Orlando Resort. Experience thrilling rides at Universal Studios Florida and explore The Wizarding World of Harry Potter at Universal's Islands of Adventure. **Day 4:** Explore SeaWorld Orlando. Enjoy exciting rides, shows, and interact with marine animals. **Day 5:** Take a day trip to Kennedy Space Center Visitor Complex. Learn about space exploration and enjoy interactive exhibits. **Day 6:** Visit LEGOLAND Florida Resort. Enjoy the various themed lands and rides designed for families and kids. **Day 7:** Relax and enjoy a day at one of Orlando's beautiful beaches, such as Cocoa Beach or Clearwater Beach. This itinerary offers a good mix of theme park fun, educational experiences, and relaxation. Be sure to check the specific opening hours and event schedules for each attraction and plan your visit accordingly. Let me know if you have any querys."} 4 | {"query": "Can you help me plan a surprise honeymoon trip to the Maldives?", "response":"Of course, I'd be happy to help you plan a surprise honeymoon trip to the Maldives! It's one of the most beautiful and romantic destinations in the world. When are you looking to travel, and do you have any specific interests or preferences for your honeymoon? For example, are you more interested in water activities, luxury resorts, or cultural experiences? This will help me tailor my recommendations to your preferences."} 5 | {"query": "Are there any guided tours available for the Great Wall of China?", "response":"Yes, there are several guided tours available for the Great Wall of China. Many tour companies offer guided excursions to different sections of the Great Wall, providing informative commentary and ensuring a smooth and enjoyable experience for visitors. These tours often include transportation, entrance fees, and sometimes even meals. If you're interested in joining a guided tour, I recommend researching reputable tour operators or reaching out to travel agencies that specialize in trips to China. They can help you find a tour that suits your preferences and budget. Additionally, some hotels in Beijing also offer organized tours to the Great Wall for their guests. Is there a specific section of the Great Wall or any other preferences you have in mind for the tour? Feel free to share more about your travel interests and I can provide more tailored recommendations."} -------------------------------------------------------------------------------- /data/travel-questions.jsonl: -------------------------------------------------------------------------------- 1 | {"question":"Can you provide a list of the top-rated budget hotels in Rome?"} 2 | {"question": "I'm looking for a vegan-friendly restaurant in New York City. Can you help?"} 3 | {"question": "Can you suggest a 7-day itinerary for a family vacation in Orlando, Florida?"} 4 | {"question": "Can you help me plan a surprise honeymoon trip to the Maldives?"} 5 | {"question": "Are there any guided tours available for the Great Wall of China?"} -------------------------------------------------------------------------------- /data/travel_evaluation_data.csv: -------------------------------------------------------------------------------- 1 | Question,ExpectedResponse 2 | What documents are required for international travel? ,"For international travel, the required documents can vary depending on your destination, nationality, and the purpose of your trip. However, here is a general list of the most commonly required documents for international travel: --- ### 1. **Passport** - A valid passport is essential for international travel. Most countries require that your passport be valid for at least **6 months** beyond your planned departure date. - Ensure your passport has enough blank pages for entry and exit stamps. ### 2. **Visa** - Many countries require a visa for entry, which may be an electronic visa (e-visa), visa-on-arrival, or a pre-approved visa from an embassy. - Research whether your destination requires a visa for your nationality, and apply well in advance if necessary. ### 3. **Flight Tickets** - A copy of your **round-trip or onward travel ticket** is often required by immigration officials to prove that you plan to leave the country. ### 4. **Travel Insurance** - Some countries mandate proof of travel insurance that covers medical expenses, accidents, and emergencies. - Check if COVID-19 coverage or specific insurance is required for entry. ### 5. **Health Documentation** - **Vaccination Certificates**: Some countries require proof of specific vaccinations (e.g., Yellow Fever vaccination if traveling from certain countries). - **COVID-19 Requirements**: This may include proof of vaccination, a negative PCR test result, or a recovery certificate. Requirements vary by country. ### 6. **Accommodation Details** - Proof of accommodation, such as a hotel reservation or an invitation letter from a host, may be required at immigration. ### 7. **Proof of Financial Means** - You may need to show proof that you can financially support yourself during your stay, such as bank statements, credit cards, or cash. ### 8. **Travel Itinerary** - A detailed travel itinerary, including places you plan to visit, may be required by immigration officials in some destinations. ### 9. **Driver’s License and International Driving Permit (IDP)** - If you plan to drive abroad, bring your valid driver's license and, in some cases, an International Driving Permit. ### 10. **Additional Documents (if applicable):** - **Work or Study Permits**: If traveling for work or study. - **Parental Consent Letter**: If traveling with minors without both parents. - **Customs Declarations**: For items like high-value goods or large amounts of cash. - **Return to Residency Documents**: If you’re a resident of another country, carry your residency card or permit. --- ### Tips: - Always check the entry requirements for your destination on the official government or embassy website. - Make photocopies or digital backups of your important documents in case of loss or theft. - Carry documents in a secure, easily accessible place during travel. If you'd like assistance checking the specific requirements for a particular destination, let me know your nationality and travel plans, and I can help!" 3 | How can I find the best deals on flights and accommodations? ,"Finding the best deals on flights and accommodations can make a significant difference to your travel budget! Here are some tried-and-true tips to help you secure great prices: --- ### **1. Be Flexible with Your Travel Dates and Destinations** - **Midweek Travel**: Flights are often cheaper on Tuesdays, Wednesdays, and Saturdays since these are less popular travel days. - **Shoulder Seasons**: Travel during the off-peak or shoulder seasons (just before or after peak tourist times) for lower prices on both flights and accommodations. - **Explore Nearby Airports**: Check flights to/from alternate airports near your destination for potential savings. --- ### **2. Use Flight and Hotel Search Engines** - **Flight Search Engines**: Use platforms like **Google Flights**, **Skyscanner**, or **Kayak** to compare flight prices across multiple airlines and booking sites. Use the ""flexible dates"" or ""price calendar"" tools to identify the cheapest days to travel. - **Hotel Aggregators**: Websites like **Booking.com**, **Hotels.com**, **Agoda**, and **Trivago** can help you compare accommodation prices. - Look for deals on vacation rental platforms like **Airbnb** or **Vrbo** for longer stays or unique lodging options. --- ### **3. Set Alerts for Deals** - Use tools like **Hopper**, **Skyscanner**, or **Google Flights** to set fare alerts. These platforms will notify you when prices drop for your desired routes or accommodations. - Subscribe to newsletters from travel deal websites like **Scott's Cheap Flights (now Going)**, **Secret Flying**, or **The Points Guy** for limited-time offers. --- ### **4. Book at the Right Time** - **Flights**: Studies suggest booking domestic flights 1-3 months in advance and international flights 3-6 months in advance for the best prices. - **Hotels**: While last-minute deals are sometimes available, booking 1-2 months in advance often secures better rates, especially for popular destinations. --- ### **5. Consider Budget Airlines and Loyalty Programs** - **Budget Airlines**: Look into low-cost carriers like Ryanair, EasyJet, or JetBlue, but be aware of additional fees for baggage, seat selection, etc. - **Airline Miles and Points**: Sign up for frequent flyer programs and hotel loyalty programs to earn points for future discounts. Credit cards with travel rewards can also help you save. --- ### **6. Look for Packages or Bundles** - Use travel websites like **Expedia**, **Priceline**, or **Travelocity** to bundle flights, hotels, and even car rentals for potential savings. - Consider all-inclusive resorts or group tour packages, especially if you're traveling to a destination with high daily costs. --- ### **7. Check for Discounts and Promotions** - **Student, Senior, or Military Discounts**: Some airlines and hotels offer special rates for certain groups. Always inquire! - **Membership Perks**: If you're a member of AAA, AARP, or Costco, you may have access to exclusive travel discounts. - **Promo Codes**: Search for promo codes or coupons online before booking. --- ### **8. Don’t Forget Alternate Accommodation Options** - **Hostels**: Great for budget travelers, hostels often offer dormitory-style lodging or private rooms at a fraction of hotel prices. - **House Swaps**: Platforms like **HomeExchange** allow you to swap homes with other travelers. - **Camping or Glamping**: If you love the outdoors, this can be a unique and cost-effective option. --- ### **9. Clear Your Browsing History** Some websites use cookies to track your searches and may increase prices if they see repeated interest. To avoid this: - Search in incognito mode. - Clear your browser cookies. --- ### **10. Negotiate and Book Smart** - **Direct Booking**: Sometimes, booking directly" 4 | What safety precautions should I take when traveling to a new country? ,"Traveling to a new country can be an exciting and enriching experience, but it's important to prioritize safety to ensure a smooth and enjoyable trip. Here are some key safety precautions to take: --- ### **Before You Travel** 1. **Research Your Destination**: - Learn about local customs, laws, and cultural norms to avoid unintentional offenses. - Check the current safety situation, including political stability, crime rates, and health risks, through government travel advisories (e.g., U.S. State Department, UK Foreign Office, or your country’s equivalent). 2. **Register with Your Embassy**: - Some countries offer a travel registration program for citizens. This helps your government contact you in case of an emergency (e.g., natural disaster, political unrest). 3. **Secure Travel Insurance**: - Purchase comprehensive travel insurance that covers medical emergencies, trip cancellations, theft, and other unforeseen events. 4. **Make Copies of Important Documents**: - Photocopy your passport, visa, travel insurance, and itinerary. Keep digital and physical copies stored securely in separate locations. 5. **Vaccinations and Medications**: - Check if vaccinations are required for your destination and carry any necessary medications with prescriptions. 6. **Plan Finances**: - Notify your bank of your travel dates to avoid credit card blocks. - Carry a mix of cash and cards, and store them in different places for security. --- ### **During Your Trip** 1. **Stay Aware of Your Surroundings**: - Be vigilant in crowded places, as they can be hotspots for pickpocketing. - Avoid displaying valuables like jewelry, expensive gadgets, or large amounts of cash. 2. **Use Reliable Transportation**: - Opt for licensed taxis, ride-sharing apps, or public transportation recommended by locals or your accommodation. - Avoid hitchhiking or accepting rides from strangers. 3. **Stay Connected**: - Share your itinerary with a trusted friend or family member. - Keep your phone charged and carry a portable power bank. 4. **Blend In with the Locals**: - Dress modestly and appropriately for the culture to avoid drawing unnecessary attention. - Learn a few basic phrases in the local language, which can help in emergencies and make interactions smoother. 5. **Protect Your Belongings**: - Use anti-theft backpacks or money belts to store your valuables. - Lock your luggage and use safes in hotels for passports and other important documents. 6. **Avoid Risky Areas**: - Research areas known for high crime rates and steer clear of them, especially at night. - Stick to well-lit and populated streets when exploring. 7. **Be Cautious with Food and Water**: - Drink bottled or filtered water if the local water supply is unsafe. - Eat at reputable restaurants or street vendors with high turnover to reduce the risk of foodborne illnesses. 8. **Trust Your Instincts**: - If something feels off, remove yourself from the situation. Your intuition can often alert you to potential dangers. --- ### **Emergency Preparedness** 1. **Know Local Emergency Numbers**: - Research and save the local equivalent of 911 (e.g., 112 in Europe, 999 in the UK). 2. **Locate the Nearest Embassy**: - Know where your country’s embassy or consulate is located in case you need assistance. 3. **Have a Backup Plan**: - Keep extra funds and a secondary form of ID in a separate location in case of theft. - Be aware of alternative transportation or accommodation options in case plans change. --- ### **Post-Travel Tip** - Monitor Your Health: - If you feel unwell after returning home, particularly if you visited a country with health risks (e.g., malaria, dengue), seek medical advice and inform your doctor about" 5 | What is the best way to convert currency while abroad? ,"Converting currency while abroad can be straightforward if you plan ahead and choose the most cost-effective and secure methods. Here are the best ways to do it: ### 1. **Use ATMs Abroad** - **Why it’s good:** ATMs typically offer better exchange rates than currency exchange counters, as they use interbank rates. - **Tips:** - Use ATMs affiliated with major banks or located in secure areas. - Check with your bank before traveling to see if they have partnerships with international banks to avoid high ATM fees. - Avoid dynamic currency conversion at the ATM (where they offer to convert to your home currency); instead, choose to withdraw in the local currency. ### 2. **Use a Travel-Friendly Credit or Debit Card** - **Why it’s good:** Many travel credit and debit cards come with zero foreign transaction fees and competitive exchange rates. - **Tips:** - Look for cards that waive foreign transaction fees, such as Chase Sapphire Preferred or Capital One Venture. - Inform your bank or card provider about your travel plans to avoid any security blocks. - Always pay in the local currency when using your card abroad to get the best rate. ### 3. **Exchange Currency Before You Leave** - **Why it’s good:** This ensures you have some cash on hand when you arrive, especially for immediate expenses like transportation or small purchases. - **Tips:** - Exchange money at your local bank or a reputable currency exchange service, as they often offer better rates than airport kiosks. - Avoid exchanging large amounts at airports or hotels due to higher fees and poor rates. ### 4. **Use Online Currency Exchange Services** - **Why it’s good:** Platforms like Revolut, Wise (formerly TransferWise), or PayPal often provide competitive rates and allow you to convert money digitally. - **Tips:** - Set up an account before traveling. - Some services provide multi-currency accounts, enabling you to store money in different currencies and use it abroad without fees. ### 5. **Avoid Currency Exchange Counters** - **Why to avoid:** These counters, especially in tourist areas, airports, and hotels, tend to have poor exchange rates and high fees. - **If necessary:** Use them only for small amounts in emergencies. ### 6. **Monitor Exchange Rates** - **Why it’s good:** Knowing the current exchange rate helps you avoid being overcharged. - **Tips:** - Use apps like XE Currency or OANDA to stay updated on exchange rates. - Check rates regularly if you plan to exchange currency multiple times during your trip. ### 7. **Carry a Mix of Payment Options** - **Why it’s good:** Some places may only accept cash, while others prefer cards. Having a mix ensures you’re prepared for any situation. - **Tips:** - Keep a small amount of local currency for street vendors, tips, or taxis. - Use cards for larger transactions to take advantage of better exchange rates. ### Final Advice: - **Plan ahead:** Research the best options available in your destination country. - **Stay secure:** Be cautious about where you withdraw or exchange money to avoid scams or theft. - **Compare fees:** Always factor in any service fees, withdrawal fees, and conversion fees to determine the true cost of currency exchange. If you'd like specific recommendations based on your destination or financial institution, feel free to ask!" 6 | Which travel insurance options provide the most comprehensive coverage? ,"When choosing travel insurance, the most comprehensive options typically include coverage for a wide range of circumstances, such as medical emergencies, trip cancellations, lost or delayed baggage, and more. Here are some factors to consider and popular travel insurance options that provide robust coverage: ### **Key Features of Comprehensive Travel Insurance** 1. **Medical Coverage**: Includes emergency medical expenses, evacuation, and repatriation. 2. **Trip Cancellation/Interruption**: Reimbursement for prepaid expenses if your trip is canceled or interrupted due to unforeseen events like illness, weather, or airline issues. 3. **Baggage Protection**: Covers lost, stolen, or delayed luggage. 4. **Accidental Death and Dismemberment**: Provides compensation for severe injuries or death while traveling. 5. **Adventure Activities Coverage**: Includes protection for high-risk activities like skiing, scuba diving, or zip-lining. 6. **COVID-19 Coverage**: Covers medical treatment or trip disruptions caused by COVID-19. 7. **Travel Delay Coverage**: Reimbursement for meals, accommodations, and transportation during unexpected delays. 8. **Rental Car Protection**: Covers damage or theft of rental vehicles. 9. **24/7 Assistance**: Offers round-the-clock support for emergencies, including translation services and legal advice. --- ### **Top Travel Insurance Providers** Here are some highly-rated companies that offer comprehensive travel insurance plans: #### 1. **World Nomads** - Best for adventurous travelers and backpackers. - Covers a wide range of activities, including high-risk sports. - Includes medical, cancellation, and baggage coverage. - Flexible policies for extended trips. #### 2. **Allianz Global Assistance** - Offers customizable plans for different travel needs. - Includes comprehensive coverage for medical emergencies, trip cancellations, and delays. - Known for strong customer service and 24/7 assistance. #### 3. **Travel Guard (AIG)** - Offers a variety of plans with extensive coverage options. - Covers medical emergencies, trip cancellations, and baggage protection. - Provides add-ons for adventure activities and rental car damage. #### 4. **GeoBlue** - Ideal for international travelers focused on health and medical coverage. - Includes emergency medical evacuation and broad health-related coverage. - Often chosen by expats or long-term travelers. #### 5. **Berkshire Hathaway Travel Protection (ExactCare)** - Known for fast claim processing and high coverage limits. - Covers trip cancellations, interruptions, baggage issues, and medical emergencies. - Includes optional add-ons for adventure sports and rental car protection. #### 6. **Squaremouth** - Acts as a comparison tool for multiple travel insurance providers. - Allows you to compare policies side-by-side to find the most comprehensive plan for your needs. - Offers extensive filters to customize coverage options. #### 7. **Travelex Insurance Services** - Offers comprehensive plans, including medical coverage, trip cancellation/interruption, and baggage protection. - Family-friendly policies that cover children at no additional cost. - Includes optional upgrades for adventure sports and business travel. --- ### **How to Choose the Right Plan** 1. **Assess Your Needs**: Consider your destination, trip duration, planned activities, and health condition. 2. **Compare Providers**: Use comparison sites like Squaremouth or InsureMyTrip to evaluate policies. 3. **Read the Fine Print**: Ensure coverage includes exclusions or limitations that may apply to your trip. 4. **Check Reviews**: Look for customer feedback to gauge reliability and claims processing. --- ### **Pro Tip**: When purchasing travel insurance, ensure the policy covers your destination and planned activities. Additionally, if traveling during uncertain times (e.g., pandemic-related disruptions), look for policies with ""cancel for any reason"" (CFAR) coverage for maximum flexibility. Would you like help comparing specific providers or" 7 | -------------------------------------------------------------------------------- /data/travel_evaluation_data.jsonl: -------------------------------------------------------------------------------- 1 | {"Question": "What documents are required for international travel? ", "ExpectedResponse": "For international travel, the required documents can vary depending on your destination, nationality, and the purpose of your trip. However, here is a general list of the most commonly required documents for international travel: --- ### 1. **Passport** - A valid passport is essential for international travel. Most countries require that your passport be valid for at least **6 months** beyond your planned departure date. - Ensure your passport has enough blank pages for entry and exit stamps. ### 2. **Visa** - Many countries require a visa for entry, which may be an electronic visa (e-visa), visa-on-arrival, or a pre-approved visa from an embassy. - Research whether your destination requires a visa for your nationality, and apply well in advance if necessary. ### 3. **Flight Tickets** - A copy of your **round-trip or onward travel ticket** is often required by immigration officials to prove that you plan to leave the country. ### 4. **Travel Insurance** - Some countries mandate proof of travel insurance that covers medical expenses, accidents, and emergencies. - Check if COVID-19 coverage or specific insurance is required for entry. ### 5. **Health Documentation** - **Vaccination Certificates**: Some countries require proof of specific vaccinations (e.g., Yellow Fever vaccination if traveling from certain countries). - **COVID-19 Requirements**: This may include proof of vaccination, a negative PCR test result, or a recovery certificate. Requirements vary by country. ### 6. **Accommodation Details** - Proof of accommodation, such as a hotel reservation or an invitation letter from a host, may be required at immigration. ### 7. **Proof of Financial Means** - You may need to show proof that you can financially support yourself during your stay, such as bank statements, credit cards, or cash. ### 8. **Travel Itinerary** - A detailed travel itinerary, including places you plan to visit, may be required by immigration officials in some destinations. ### 9. **Driver\u2019s License and International Driving Permit (IDP)** - If you plan to drive abroad, bring your valid driver's license and, in some cases, an International Driving Permit. ### 10. **Additional Documents (if applicable):** - **Work or Study Permits**: If traveling for work or study. - **Parental Consent Letter**: If traveling with minors without both parents. - **Customs Declarations**: For items like high-value goods or large amounts of cash. - **Return to Residency Documents**: If you\u2019re a resident of another country, carry your residency card or permit. --- ### Tips: - Always check the entry requirements for your destination on the official government or embassy website. - Make photocopies or digital backups of your important documents in case of loss or theft. - Carry documents in a secure, easily accessible place during travel. If you'd like assistance checking the specific requirements for a particular destination, let me know your nationality and travel plans, and I can help!"} 2 | {"Question": "How can I find the best deals on flights and accommodations? ", "ExpectedResponse": "Finding the best deals on flights and accommodations can make a significant difference to your travel budget! Here are some tried-and-true tips to help you secure great prices: --- ### **1. Be Flexible with Your Travel Dates and Destinations** - **Midweek Travel**: Flights are often cheaper on Tuesdays, Wednesdays, and Saturdays since these are less popular travel days. - **Shoulder Seasons**: Travel during the off-peak or shoulder seasons (just before or after peak tourist times) for lower prices on both flights and accommodations. - **Explore Nearby Airports**: Check flights to/from alternate airports near your destination for potential savings. --- ### **2. Use Flight and Hotel Search Engines** - **Flight Search Engines**: Use platforms like **Google Flights**, **Skyscanner**, or **Kayak** to compare flight prices across multiple airlines and booking sites. Use the \"flexible dates\" or \"price calendar\" tools to identify the cheapest days to travel. - **Hotel Aggregators**: Websites like **Booking.com**, **Hotels.com**, **Agoda**, and **Trivago** can help you compare accommodation prices. - Look for deals on vacation rental platforms like **Airbnb** or **Vrbo** for longer stays or unique lodging options. --- ### **3. Set Alerts for Deals** - Use tools like **Hopper**, **Skyscanner**, or **Google Flights** to set fare alerts. These platforms will notify you when prices drop for your desired routes or accommodations. - Subscribe to newsletters from travel deal websites like **Scott's Cheap Flights (now Going)**, **Secret Flying**, or **The Points Guy** for limited-time offers. --- ### **4. Book at the Right Time** - **Flights**: Studies suggest booking domestic flights 1-3 months in advance and international flights 3-6 months in advance for the best prices. - **Hotels**: While last-minute deals are sometimes available, booking 1-2 months in advance often secures better rates, especially for popular destinations. --- ### **5. Consider Budget Airlines and Loyalty Programs** - **Budget Airlines**: Look into low-cost carriers like Ryanair, EasyJet, or JetBlue, but be aware of additional fees for baggage, seat selection, etc. - **Airline Miles and Points**: Sign up for frequent flyer programs and hotel loyalty programs to earn points for future discounts. Credit cards with travel rewards can also help you save. --- ### **6. Look for Packages or Bundles** - Use travel websites like **Expedia**, **Priceline**, or **Travelocity** to bundle flights, hotels, and even car rentals for potential savings. - Consider all-inclusive resorts or group tour packages, especially if you're traveling to a destination with high daily costs. --- ### **7. Check for Discounts and Promotions** - **Student, Senior, or Military Discounts**: Some airlines and hotels offer special rates for certain groups. Always inquire! - **Membership Perks**: If you're a member of AAA, AARP, or Costco, you may have access to exclusive travel discounts. - **Promo Codes**: Search for promo codes or coupons online before booking. --- ### **8. Don\u2019t Forget Alternate Accommodation Options** - **Hostels**: Great for budget travelers, hostels often offer dormitory-style lodging or private rooms at a fraction of hotel prices. - **House Swaps**: Platforms like **HomeExchange** allow you to swap homes with other travelers. - **Camping or Glamping**: If you love the outdoors, this can be a unique and cost-effective option. --- ### **9. Clear Your Browsing History** Some websites use cookies to track your searches and may increase prices if they see repeated interest. To avoid this: - Search in incognito mode. - Clear your browser cookies. --- ### **10. Negotiate and Book Smart** - **Direct Booking**: Sometimes, booking directly"} 3 | {"Question": "What safety precautions should I take when traveling to a new country? ", "ExpectedResponse": "Traveling to a new country can be an exciting and enriching experience, but it's important to prioritize safety to ensure a smooth and enjoyable trip. Here are some key safety precautions to take: --- ### **Before You Travel** 1. **Research Your Destination**: - Learn about local customs, laws, and cultural norms to avoid unintentional offenses. - Check the current safety situation, including political stability, crime rates, and health risks, through government travel advisories (e.g., U.S. State Department, UK Foreign Office, or your country\u2019s equivalent). 2. **Register with Your Embassy**: - Some countries offer a travel registration program for citizens. This helps your government contact you in case of an emergency (e.g., natural disaster, political unrest). 3. **Secure Travel Insurance**: - Purchase comprehensive travel insurance that covers medical emergencies, trip cancellations, theft, and other unforeseen events. 4. **Make Copies of Important Documents**: - Photocopy your passport, visa, travel insurance, and itinerary. Keep digital and physical copies stored securely in separate locations. 5. **Vaccinations and Medications**: - Check if vaccinations are required for your destination and carry any necessary medications with prescriptions. 6. **Plan Finances**: - Notify your bank of your travel dates to avoid credit card blocks. - Carry a mix of cash and cards, and store them in different places for security. --- ### **During Your Trip** 1. **Stay Aware of Your Surroundings**: - Be vigilant in crowded places, as they can be hotspots for pickpocketing. - Avoid displaying valuables like jewelry, expensive gadgets, or large amounts of cash. 2. **Use Reliable Transportation**: - Opt for licensed taxis, ride-sharing apps, or public transportation recommended by locals or your accommodation. - Avoid hitchhiking or accepting rides from strangers. 3. **Stay Connected**: - Share your itinerary with a trusted friend or family member. - Keep your phone charged and carry a portable power bank. 4. **Blend In with the Locals**: - Dress modestly and appropriately for the culture to avoid drawing unnecessary attention. - Learn a few basic phrases in the local language, which can help in emergencies and make interactions smoother. 5. **Protect Your Belongings**: - Use anti-theft backpacks or money belts to store your valuables. - Lock your luggage and use safes in hotels for passports and other important documents. 6. **Avoid Risky Areas**: - Research areas known for high crime rates and steer clear of them, especially at night. - Stick to well-lit and populated streets when exploring. 7. **Be Cautious with Food and Water**: - Drink bottled or filtered water if the local water supply is unsafe. - Eat at reputable restaurants or street vendors with high turnover to reduce the risk of foodborne illnesses. 8. **Trust Your Instincts**: - If something feels off, remove yourself from the situation. Your intuition can often alert you to potential dangers. --- ### **Emergency Preparedness** 1. **Know Local Emergency Numbers**: - Research and save the local equivalent of 911 (e.g., 112 in Europe, 999 in the UK). 2. **Locate the Nearest Embassy**: - Know where your country\u2019s embassy or consulate is located in case you need assistance. 3. **Have a Backup Plan**: - Keep extra funds and a secondary form of ID in a separate location in case of theft. - Be aware of alternative transportation or accommodation options in case plans change. --- ### **Post-Travel Tip** - Monitor Your Health: - If you feel unwell after returning home, particularly if you visited a country with health risks (e.g., malaria, dengue), seek medical advice and inform your doctor about"} 4 | {"Question": "What is the best way to convert currency while abroad? ", "ExpectedResponse": "Converting currency while abroad can be straightforward if you plan ahead and choose the most cost-effective and secure methods. Here are the best ways to do it: ### 1. **Use ATMs Abroad** - **Why it\u2019s good:** ATMs typically offer better exchange rates than currency exchange counters, as they use interbank rates. - **Tips:** - Use ATMs affiliated with major banks or located in secure areas. - Check with your bank before traveling to see if they have partnerships with international banks to avoid high ATM fees. - Avoid dynamic currency conversion at the ATM (where they offer to convert to your home currency); instead, choose to withdraw in the local currency. ### 2. **Use a Travel-Friendly Credit or Debit Card** - **Why it\u2019s good:** Many travel credit and debit cards come with zero foreign transaction fees and competitive exchange rates. - **Tips:** - Look for cards that waive foreign transaction fees, such as Chase Sapphire Preferred or Capital One Venture. - Inform your bank or card provider about your travel plans to avoid any security blocks. - Always pay in the local currency when using your card abroad to get the best rate. ### 3. **Exchange Currency Before You Leave** - **Why it\u2019s good:** This ensures you have some cash on hand when you arrive, especially for immediate expenses like transportation or small purchases. - **Tips:** - Exchange money at your local bank or a reputable currency exchange service, as they often offer better rates than airport kiosks. - Avoid exchanging large amounts at airports or hotels due to higher fees and poor rates. ### 4. **Use Online Currency Exchange Services** - **Why it\u2019s good:** Platforms like Revolut, Wise (formerly TransferWise), or PayPal often provide competitive rates and allow you to convert money digitally. - **Tips:** - Set up an account before traveling. - Some services provide multi-currency accounts, enabling you to store money in different currencies and use it abroad without fees. ### 5. **Avoid Currency Exchange Counters** - **Why to avoid:** These counters, especially in tourist areas, airports, and hotels, tend to have poor exchange rates and high fees. - **If necessary:** Use them only for small amounts in emergencies. ### 6. **Monitor Exchange Rates** - **Why it\u2019s good:** Knowing the current exchange rate helps you avoid being overcharged. - **Tips:** - Use apps like XE Currency or OANDA to stay updated on exchange rates. - Check rates regularly if you plan to exchange currency multiple times during your trip. ### 7. **Carry a Mix of Payment Options** - **Why it\u2019s good:** Some places may only accept cash, while others prefer cards. Having a mix ensures you\u2019re prepared for any situation. - **Tips:** - Keep a small amount of local currency for street vendors, tips, or taxis. - Use cards for larger transactions to take advantage of better exchange rates. ### Final Advice: - **Plan ahead:** Research the best options available in your destination country. - **Stay secure:** Be cautious about where you withdraw or exchange money to avoid scams or theft. - **Compare fees:** Always factor in any service fees, withdrawal fees, and conversion fees to determine the true cost of currency exchange. If you'd like specific recommendations based on your destination or financial institution, feel free to ask!"} 5 | {"Question": "Which travel insurance options provide the most comprehensive coverage? ", "ExpectedResponse": "When choosing travel insurance, the most comprehensive options typically include coverage for a wide range of circumstances, such as medical emergencies, trip cancellations, lost or delayed baggage, and more. Here are some factors to consider and popular travel insurance options that provide robust coverage: ### **Key Features of Comprehensive Travel Insurance** 1. **Medical Coverage**: Includes emergency medical expenses, evacuation, and repatriation. 2. **Trip Cancellation/Interruption**: Reimbursement for prepaid expenses if your trip is canceled or interrupted due to unforeseen events like illness, weather, or airline issues. 3. **Baggage Protection**: Covers lost, stolen, or delayed luggage. 4. **Accidental Death and Dismemberment**: Provides compensation for severe injuries or death while traveling. 5. **Adventure Activities Coverage**: Includes protection for high-risk activities like skiing, scuba diving, or zip-lining. 6. **COVID-19 Coverage**: Covers medical treatment or trip disruptions caused by COVID-19. 7. **Travel Delay Coverage**: Reimbursement for meals, accommodations, and transportation during unexpected delays. 8. **Rental Car Protection**: Covers damage or theft of rental vehicles. 9. **24/7 Assistance**: Offers round-the-clock support for emergencies, including translation services and legal advice. --- ### **Top Travel Insurance Providers** Here are some highly-rated companies that offer comprehensive travel insurance plans: #### 1. **World Nomads** - Best for adventurous travelers and backpackers. - Covers a wide range of activities, including high-risk sports. - Includes medical, cancellation, and baggage coverage. - Flexible policies for extended trips. #### 2. **Allianz Global Assistance** - Offers customizable plans for different travel needs. - Includes comprehensive coverage for medical emergencies, trip cancellations, and delays. - Known for strong customer service and 24/7 assistance. #### 3. **Travel Guard (AIG)** - Offers a variety of plans with extensive coverage options. - Covers medical emergencies, trip cancellations, and baggage protection. - Provides add-ons for adventure activities and rental car damage. #### 4. **GeoBlue** - Ideal for international travelers focused on health and medical coverage. - Includes emergency medical evacuation and broad health-related coverage. - Often chosen by expats or long-term travelers. #### 5. **Berkshire Hathaway Travel Protection (ExactCare)** - Known for fast claim processing and high coverage limits. - Covers trip cancellations, interruptions, baggage issues, and medical emergencies. - Includes optional add-ons for adventure sports and rental car protection. #### 6. **Squaremouth** - Acts as a comparison tool for multiple travel insurance providers. - Allows you to compare policies side-by-side to find the most comprehensive plan for your needs. - Offers extensive filters to customize coverage options. #### 7. **Travelex Insurance Services** - Offers comprehensive plans, including medical coverage, trip cancellation/interruption, and baggage protection. - Family-friendly policies that cover children at no additional cost. - Includes optional upgrades for adventure sports and business travel. --- ### **How to Choose the Right Plan** 1. **Assess Your Needs**: Consider your destination, trip duration, planned activities, and health condition. 2. **Compare Providers**: Use comparison sites like Squaremouth or InsureMyTrip to evaluate policies. 3. **Read the Fine Print**: Ensure coverage includes exclusions or limitations that may apply to your trip. 4. **Check Reviews**: Look for customer feedback to gauge reliability and claims processing. --- ### **Pro Tip**: When purchasing travel insurance, ensure the policy covers your destination and planned activities. Additionally, if traveling during uncertain times (e.g., pandemic-related disruptions), look for policies with \"cancel for any reason\" (CFAR) coverage for maximum flexibility. Would you like help comparing specific providers or"} 6 | -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Develop generative AI solutions in Azure 3 | permalink: index.html 4 | layout: home 5 | --- 6 | 7 | The following exercises are designed to provide you with a hands-on learning experience in which you'll explore common tasks that developers do when building generative AI solutions on Microsoft Azure. 8 | 9 | > **Note**: To complete the exercises, you'll need an Azure subscription in which you have sufficient permissions and quota to provision the necessary Azure resources and generative AI models. If you don't already have one, you can sign up for an [Azure account](https://azure.microsoft.com/free). There's a free trial option for new users that includes credits for the first 30 days. 10 | 11 | ## Exercises 12 | 13 | {% assign labs = site.pages | where_exp:"page", "page.url contains '/Instructions'" %} 14 | {% for activity in labs %} 15 |
16 | ### [{{ activity.lab.title }}]({{ site.github.url }}{{ activity.url }}) 17 | 18 | {{activity.lab.description}} 19 | 20 | {% endfor %} 21 | 22 | > **Note**: While you can complete these exercises on their own, they're designed to complement modules on [Microsoft Learn](https://learn.microsoft.com/training/paths/create-custom-copilots-ai-studio/); in which you'll find a deeper dive into some of the underlying concepts on which these exercises are based. 23 | -------------------------------------------------------------------------------- /labfiles/chat-app/c-sharp/Program.cs: -------------------------------------------------------------------------------- 1 | using System; 2 | using Azure; 3 | using System.IO; 4 | using System.Text; 5 | using System.Collections.Generic; 6 | using Microsoft.Extensions.Configuration; 7 | 8 | // Add references 9 | 10 | 11 | namespace chat_app 12 | { 13 | class Program 14 | { 15 | static void Main(string[] args) 16 | { 17 | // Clear the console 18 | Console.Clear(); 19 | 20 | try 21 | { 22 | // Get config settings 23 | IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json"); 24 | IConfigurationRoot configuration = builder.Build(); 25 | string project_connection = configuration["PROJECT_ENDPOINT"]; 26 | string model_deployment = configuration["MODEL_DEPLOYMENT"]; 27 | 28 | // Initialize the project client 29 | 30 | 31 | // Get a chat client 32 | 33 | 34 | // Initialize prompt with system message 35 | 36 | 37 | // Loop until the user types 'quit' 38 | string input_text = ""; 39 | while (input_text.ToLower() != "quit") 40 | { 41 | // Get user input 42 | Console.WriteLine("Enter the prompt (or type 'quit' to exit):"); 43 | input_text = Console.ReadLine(); 44 | if (input_text.ToLower() != "quit") 45 | { 46 | // Get a chat completion 47 | 48 | 49 | } 50 | 51 | 52 | } 53 | 54 | 55 | 56 | } 57 | catch (Exception ex) 58 | { 59 | Console.WriteLine(ex.Message); 60 | } 61 | } 62 | } 63 | } 64 | 65 | -------------------------------------------------------------------------------- /labfiles/chat-app/c-sharp/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "PROJECT_ENDPOINT": "your_project_endpoint", 3 | "MODEL_DEPLOYMENT": "your_model_deployment" 4 | } -------------------------------------------------------------------------------- /labfiles/chat-app/c-sharp/chat_app.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | net8.0 6 | chat_app 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | PreserveNewest 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /labfiles/chat-app/python/.env: -------------------------------------------------------------------------------- 1 | PROJECT_ENDPOINT="your_project_endpoint" 2 | MODEL_DEPLOYMENT="your_model_deployment" -------------------------------------------------------------------------------- /labfiles/chat-app/python/chat-app.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # Add references 4 | 5 | 6 | def main(): 7 | 8 | # Clear the console 9 | os.system('cls' if os.name=='nt' else 'clear') 10 | 11 | try: 12 | 13 | # Get configuration settings 14 | load_dotenv() 15 | project_connection = os.getenv("PROJECT_ENDPOINT") 16 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 17 | 18 | # Initialize the project client 19 | 20 | 21 | ## Get a chat client 22 | 23 | 24 | # Initialize prompt with system message 25 | 26 | 27 | # Loop until the user types 'quit' 28 | while True: 29 | # Get input text 30 | input_text = input("Enter the prompt (or type 'quit' to exit): ") 31 | if input_text.lower() == "quit": 32 | break 33 | if len(input_text) == 0: 34 | print("Please enter a prompt.") 35 | continue 36 | 37 | # Get a chat completion 38 | 39 | 40 | except Exception as ex: 41 | print(ex) 42 | 43 | if __name__ == '__main__': 44 | main() -------------------------------------------------------------------------------- /labfiles/multimodal/c-sharp/Program.cs: -------------------------------------------------------------------------------- 1 | using System; 2 | using Azure; 3 | using System.IO; 4 | using System.Text; 5 | using System.Collections.Generic; 6 | using Microsoft.Extensions.Configuration; 7 | 8 | // Add references 9 | 10 | 11 | namespace chat_app 12 | { 13 | class Program 14 | { 15 | static void Main(string[] args) 16 | { 17 | // Clear the console 18 | Console.Clear(); 19 | 20 | try 21 | { 22 | // Get config settings 23 | IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json"); 24 | IConfigurationRoot configuration = builder.Build(); 25 | string project_connection = configuration["PROJECT_CONNECTION"]; 26 | string model_deployment = configuration["MODEL_DEPLOYMENT"]; 27 | 28 | // Initialize the project client 29 | 30 | 31 | 32 | // Get a chat client 33 | 34 | 35 | 36 | string system_message = "You are an AI assistent in a grocery store that sells fruit."; 37 | string prompt = ""; 38 | 39 | // Loop until the user types 'quit' 40 | string input_text = ""; 41 | while (input_text.ToLower() != "quit") 42 | { 43 | // Get user input 44 | Console.WriteLine("Choose a prompt type (or type 'quit' to exit):\n-1: Text\n-2: Image\n-3: Audio\n"); 45 | input_text = Console.ReadLine().ToLower(); 46 | if (input_text != "quit") 47 | { 48 | switch (input_text) 49 | { 50 | case "1": 51 | Console.WriteLine("Enter the prompt: "); 52 | prompt = Console.ReadLine(); 53 | if (string.IsNullOrEmpty(prompt)) 54 | { 55 | Console.WriteLine("Please enter a prompt."); 56 | } 57 | else 58 | { 59 | Console.WriteLine("Getting a response to your prompt..."); 60 | 61 | // Get a response to text input 62 | 63 | 64 | } 65 | break; 66 | case "2": 67 | Console.WriteLine("Enter the prompt to accompany the image: "); 68 | prompt = Console.ReadLine(); 69 | if (string.IsNullOrEmpty(prompt)) 70 | { 71 | Console.WriteLine("Please enter a prompt."); 72 | } 73 | else 74 | { 75 | Console.WriteLine("Getting a response to your prompt..."); 76 | 77 | // Get a response to image input 78 | 79 | 80 | } 81 | break; 82 | case "3": 83 | Console.WriteLine("Enter the prompt to accompany an audio recording of 'Me gustaría comprar 2 manzanas.':"); 84 | prompt = Console.ReadLine(); 85 | if (string.IsNullOrEmpty(prompt)) 86 | { 87 | Console.WriteLine("Please enter a prompt."); 88 | } 89 | else 90 | { 91 | Console.WriteLine("Getting a response to your prompt..."); 92 | 93 | // Get a response to audio input 94 | 95 | 96 | } 97 | break; 98 | default: 99 | Console.WriteLine("Please enter a valid value"); 100 | break; 101 | } 102 | } 103 | 104 | 105 | } 106 | 107 | 108 | 109 | } 110 | catch (Exception ex) 111 | { 112 | Console.WriteLine(ex.Message); 113 | } 114 | } 115 | } 116 | } 117 | 118 | -------------------------------------------------------------------------------- /labfiles/multimodal/c-sharp/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "PROJECT_CONNECTION": "your_project_connection_string", 3 | "MODEL_DEPLOYMENT": "your_model_deployment" 4 | } -------------------------------------------------------------------------------- /labfiles/multimodal/c-sharp/chat_app.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | net8.0 6 | chat_app 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | PreserveNewest 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /labfiles/multimodal/manzanas.mp3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/9422b1aff98bbef4408435f89970fa183753baf9/labfiles/multimodal/manzanas.mp3 -------------------------------------------------------------------------------- /labfiles/multimodal/orange.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/9422b1aff98bbef4408435f89970fa183753baf9/labfiles/multimodal/orange.jpg -------------------------------------------------------------------------------- /labfiles/multimodal/python/.env: -------------------------------------------------------------------------------- 1 | PROJECT_CONNECTION="your_project_connection_string" 2 | MODEL_DEPLOYMENT="your_model_deployment" 3 | -------------------------------------------------------------------------------- /labfiles/multimodal/python/chat-app.py: -------------------------------------------------------------------------------- 1 | import os 2 | from urllib.request import urlopen, Request 3 | import base64 4 | 5 | # Add references 6 | 7 | 8 | def main(): 9 | 10 | # Clear the console 11 | os.system('cls' if os.name=='nt' else 'clear') 12 | 13 | try: 14 | 15 | # Get configuration settings 16 | load_dotenv() 17 | project_connection = os.getenv("PROJECT_CONNECTION") 18 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 19 | 20 | # Initialize the project client 21 | 22 | 23 | 24 | ## Get a chat client 25 | 26 | 27 | system_message = "You are an AI assistent in a grocery store that sells fruit." 28 | 29 | 30 | # Loop until the user types 'quit' 31 | while True: 32 | input_text = input("Choose a prompt type (or type 'quit' to exit):\n-1: Text\n-2: Image\n-3: Audio\n") 33 | if input_text.lower() == "quit": 34 | break 35 | elif input_text == "1": 36 | prompt = input("Enter the prompt: ") 37 | if len(prompt) == 0: 38 | print("Please enter a prompt.") 39 | else: 40 | print("Getting a response to your prompt...") 41 | 42 | # Get a response to text input 43 | 44 | 45 | elif input_text == "2": 46 | prompt = input("Enter the prompt to accompany the image: ") 47 | if len(prompt) == 0: 48 | print("Please enter a prompt.") 49 | else: 50 | print("Getting a response to your prompt...") 51 | 52 | # Get a response to image input 53 | 54 | 55 | elif input_text == "3": 56 | prompt = input("Enter the prompt to accompany an audio recording of 'Me gustaría comprar 2 manzanas.': ") 57 | if len(prompt) == 0: 58 | print("Please enter a prompt.") 59 | else: 60 | print("Getting a response to your prompt...") 61 | 62 | # Get a response to audio input 63 | 64 | 65 | else: 66 | print("Please enter a valid value") 67 | continue 68 | 69 | 70 | except Exception as ex: 71 | print(ex) 72 | 73 | 74 | if __name__ == '__main__': 75 | main() -------------------------------------------------------------------------------- /labfiles/rag-app/c-sharp/Program.cs: -------------------------------------------------------------------------------- 1 | using System; 2 | using Azure; 3 | using System.IO; 4 | using System.Text; 5 | using System.Collections.Generic; 6 | using Microsoft.Extensions.Configuration; 7 | using Azure.AI.OpenAI; 8 | using System.ClientModel; 9 | using Azure.AI.OpenAI.Chat; 10 | using OpenAI.Chat; 11 | 12 | namespace rag_app 13 | { 14 | class Program 15 | { 16 | static void Main(string[] args) 17 | { 18 | // Clear the console 19 | Console.Clear(); 20 | 21 | try 22 | { 23 | // Get config settings 24 | IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json"); 25 | IConfigurationRoot configuration = builder.Build(); 26 | string open_ai_endpoint = configuration["OPEN_AI_ENDPOINT"]; 27 | string open_ai_key = configuration["OPEN_AI_KEY"]; 28 | string chat_model = configuration["CHAT_MODEL"]; 29 | string embedding_model = configuration["EMBEDDING_MODEL"]; 30 | string search_url = configuration["SEARCH_ENDPOINT"]; 31 | string search_key = configuration["SEARCH_KEY"]; 32 | string index_name = configuration["INDEX_NAME"]; 33 | 34 | // Get an Azure OpenAI chat client 35 | AzureOpenAIClient azureClient = new( 36 | new Uri(open_ai_endpoint), 37 | new AzureKeyCredential(open_ai_key)); 38 | ChatClient chatClient = azureClient.GetChatClient(chat_model); 39 | 40 | 41 | // Initialize prompt with system message 42 | var prompt = new List() 43 | { 44 | new SystemChatMessage("You are a travel assistant that provides information on travel services available from Margie's Travel.") 45 | }; 46 | 47 | // Loop until the user types 'quit' 48 | string input_text = ""; 49 | while (input_text.ToLower() != "quit") 50 | { 51 | // Get user input 52 | Console.WriteLine("Enter the prompt (or type 'quit' to exit):"); 53 | input_text = Console.ReadLine(); 54 | 55 | if (input_text.ToLower() != "quit") 56 | { 57 | // Add the user input message to the prompt 58 | prompt.Add(new UserChatMessage(input_text)); 59 | 60 | // (DataSource is in preview and subject to breaking changes) 61 | #pragma warning disable AOAI001 62 | 63 | // Additional parameters to apply RAG pattern using the AI Search index 64 | ChatCompletionOptions options = new(); 65 | options.AddDataSource(new AzureSearchChatDataSource() 66 | { 67 | // The following params are used to search the index 68 | Endpoint = new Uri(search_url), 69 | IndexName = index_name, 70 | Authentication = DataSourceAuthentication.FromApiKey(search_key), 71 | // The following params are used to vectorize the query 72 | QueryType = "vector", 73 | VectorizationSource = DataSourceVectorizer.FromDeploymentName(embedding_model), 74 | }); 75 | 76 | // Submit the prompt with the data source options and display the response 77 | ChatCompletion completion = chatClient.CompleteChat(prompt, options); 78 | var completionText = completion.Content[0].Text; 79 | Console.WriteLine(completionText); 80 | 81 | // Add the response to the chat history 82 | prompt.Add(new AssistantChatMessage(completionText)); 83 | 84 | #pragma warning restore AOAI001 85 | } 86 | } 87 | } 88 | catch (Exception ex) 89 | { 90 | Console.WriteLine(ex.Message); 91 | } 92 | } 93 | } 94 | } -------------------------------------------------------------------------------- /labfiles/rag-app/c-sharp/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "OPEN_AI_ENDPOINT": "your_openai_endpoint", 3 | "OPEN_AI_KEY": "your_openai_api_key", 4 | "CHAT_MODEL": "your_chat_model", 5 | "EMBEDDING_MODEL": "your_embedding_model", 6 | "SEARCH_ENDPOINT": "your_search_endpoint", 7 | "SEARCH_KEY": "your_search_api_key", 8 | "INDEX_NAME": "your_index", 9 | } 10 | -------------------------------------------------------------------------------- /labfiles/rag-app/c-sharp/rag_app.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | net8.0 6 | rag_app 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | PreserveNewest 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /labfiles/rag-app/python/.env: -------------------------------------------------------------------------------- 1 | OPEN_AI_ENDPOINT="your_openai_endpoint" 2 | OPEN_AI_KEY="your_openai_api_key" 3 | CHAT_MODEL="your_chat_model" 4 | EMBEDDING_MODEL="your_embedding_model" 5 | SEARCH_ENDPOINT="your_search_endpoint" 6 | SEARCH_KEY="your_search_api_key" 7 | INDEX_NAME="your_index" 8 | -------------------------------------------------------------------------------- /labfiles/rag-app/python/rag-app.py: -------------------------------------------------------------------------------- 1 | import os 2 | from dotenv import load_dotenv 3 | from openai import AzureOpenAI 4 | 5 | def main(): 6 | # Clear the console 7 | os.system('cls' if os.name == 'nt' else 'clear') 8 | 9 | try: 10 | # Get configuration settings 11 | load_dotenv() 12 | open_ai_endpoint = os.getenv("OPEN_AI_ENDPOINT") 13 | open_ai_key = os.getenv("OPEN_AI_KEY") 14 | chat_model = os.getenv("CHAT_MODEL") 15 | embedding_model = os.getenv("EMBEDDING_MODEL") 16 | search_url = os.getenv("SEARCH_ENDPOINT") 17 | search_key = os.getenv("SEARCH_KEY") 18 | index_name = os.getenv("INDEX_NAME") 19 | 20 | 21 | # Get an Azure OpenAI chat client 22 | chat_client = AzureOpenAI( 23 | api_version = "2024-12-01-preview", 24 | azure_endpoint = open_ai_endpoint, 25 | api_key = open_ai_key 26 | ) 27 | 28 | 29 | # Initialize prompt with system message 30 | prompt = [ 31 | {"role": "system", "content": "You are a travel assistant that provides information on travel services available from Margie's Travel."} 32 | ] 33 | 34 | # Loop until the user types 'quit' 35 | while True: 36 | # Get input text 37 | input_text = input("Enter the prompt (or type 'quit' to exit): ") 38 | if input_text.lower() == "quit": 39 | break 40 | if len(input_text) == 0: 41 | print("Please enter a prompt.") 42 | continue 43 | 44 | # Add the user input message to the prompt 45 | prompt.append({"role": "user", "content": input_text}) 46 | 47 | # Additional parameters to apply RAG pattern using the AI Search index 48 | rag_params = { 49 | "data_sources": [ 50 | { 51 | # he following params are used to search the index 52 | "type": "azure_search", 53 | "parameters": { 54 | "endpoint": search_url, 55 | "index_name": index_name, 56 | "authentication": { 57 | "type": "api_key", 58 | "key": search_key, 59 | }, 60 | # The following params are used to vectorize the query 61 | "query_type": "vector", 62 | "embedding_dependency": { 63 | "type": "deployment_name", 64 | "deployment_name": embedding_model, 65 | }, 66 | } 67 | } 68 | ], 69 | } 70 | 71 | # Submit the prompt with the data source options and display the response 72 | response = chat_client.chat.completions.create( 73 | model=chat_model, 74 | messages=prompt, 75 | extra_body=rag_params 76 | ) 77 | completion = response.choices[0].message.content 78 | print(completion) 79 | 80 | # Add the response to the chat history 81 | prompt.append({"role": "assistant", "content": completion}) 82 | 83 | except Exception as ex: 84 | print(ex) 85 | 86 | if __name__ == '__main__': 87 | main() -------------------------------------------------------------------------------- /labfiles/rag-app/python/requirements.txt: -------------------------------------------------------------------------------- 1 | python-dotenv 2 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Develop Generative AI Solutions in Azure 2 | 3 | The exercises in this repo are designed to provide you with a hands-on learning experience in which you'll explore common tasks that developers perform when building generative AI solutions on Microsoft Azure. 4 | 5 | > **Note**: To complete the exercises, you'll need an Azure subscription in which you have sufficient permissions and quota to provision the necessary Azure resources and generative AI models. If you don't already have one, you can sign up for an [Azure account](https://azure.microsoft.com/free). There's a free trial option for new users that includes credits for the first 30 days. 6 | 7 | View the exercises in the [GitHub Pages site for this repo](https://go.microsoft.com/fwlink/?linkid=2310724). 8 | 9 | 10 | > **Note**: While you can complete these exercises on their own, they're designed to complement modules on [Microsoft Learn](https://aka.ms/mslearn-generative-ai); in which you'll find a deeper dive into some of the underlying concepts on which these exercises are based. 11 | 12 | ## Reporting issues 13 | 14 | If you encounter any problems in the exercises, please report them as **issues** in this repo. 15 | -------------------------------------------------------------------------------- /text-files/sample-text.txt: -------------------------------------------------------------------------------- 1 | The process of making maple syrup begins by tapping a spout (sometimes called a spile) into the sugar maple tree. The spile is inserted into the tree about 2 inches deep and the sap is collected as it flows out. The sap is then taken to a sugar shack where it is boiled down to concentrate the sugars. As the sap boils, water in the sap is evaporated and the syrup becomes more and more thick. Once the syrup reaches the right sugar content, which is usually when the boiling point reaches 219 degrees Fahrenheit, it is bottled and enjoyed. --------------------------------------------------------------------------------