Azure Prompt Flow for Developers

Azure Prompt Flow is a game-changer for developers. It allows you to build conversational interfaces with ease, using a simple and intuitive flow-based approach.

With Azure Prompt Flow, you can create custom prompts that are tailored to your specific needs, using a variety of input types such as text, numbers, and dates.

The flow-based approach enables you to visualize and manage the conversation flow in a more structured and organized way. This makes it easier to build complex conversational interfaces with multiple steps and branches.

Azure Prompt Flow supports a wide range of triggers and actions, including HTTP requests, Azure Functions, and even custom code. This gives you the flexibility to integrate your conversational interface with other services and systems.

To get started with Azure Prompt Flow, you'll need a few things. First, you'll need an Azure Machine Learning workspace, which you can create by following the instructions at the link provided in the prerequisites section.

You'll also need a local Python environment with the Azure Machine Learning Python SDK v2 installed. This is separate from the environment the compute session uses to run the flow, which you define as part of the flow.

Visual Studio Code is the recommended IDE for developing Prompt Flow applications, and you'll need to have the Python and Prompt Flow extensions installed.

To streamline your development process, consider using a source code repository and a continuous integration/continuous deployment (CI/CD) pipeline. This will help you manage your code, collaborate with team members, and ensure that your application is always up-to-date.

Creating and developing a prompt flow in Azure AI Foundry is a straightforward process. You can create a flow by cloning samples from the gallery or creating one from scratch. If you have existing flow files, you can import them to create a flow.

To create a flow from the gallery, sign in to Azure AI Foundry, select your project, and navigate to the Prompt flow section. Select + Create, and then choose the Standard flow tile to create a new flow. On the Create a new flow page, enter a folder name and select Create to start authoring your flow.

You can also work locally with prompt flow, using the VS Code community version, the VS Code Prompt flow extension, and the prompt flow local SDK and CLI. This allows you to make and test changes quickly without needing to update the main code repository each time.

To collaborate on flow development, use a cloud-based source control system like GitHub or Azure Repos to track changes, manage versions, and integrate modifications into the final project. The prompt flow SDK/CLI and the VS Code Prompt flow extension facilitate easy collaboration on code-based flow development within a source code repository.

Here are the main steps to create a flow using the SDK:

To create a prompt flow, you can either clone samples from the gallery or start from scratch. You can also import flow files from local or file share if you already have them.

Cloning from the gallery is a great way to get started, as it provides a sample flow that you can use as a starting point. To clone a sample flow, sign in to Azure AI Foundry, select your project, and navigate to the Prompt flow section.

Once you're in the Prompt flow section, click on the + Create button, and then select the Standard flow tile. Choose Create, and enter a folder name. This will open the prompt flow authoring page, where you can start building your flow.

You can add more tools to your flow by selecting + More tools, which will give you access to additional tools like LLM, Prompt, and Python. You can also add a connection and deployment in the LLM tool editor.

To run your flow, select Run, and the flow will start executing. You can view the flow run status and output in the Outputs section once the flow is completed.

For local development and testing, you can use the VS Code community version, the VS Code Prompt flow extension, and the prompt flow local SDK and CLI. This allows you to make and test changes quickly without needing to update the main code repository each time.

Here are some key features of the prompt flow SDK and CLI:

Using the prompt flow SDK and CLI, you can trigger a single flow run for testing by using the prompt flow CLI or SDK in the terminal. This will return the test logs and outputs.

If you prefer to work directly in code, you can directly modify the YAML code in the flow.dag.yaml file. This will allow you to make changes to your flow and then trigger a flow run using the prompt flow CLI or SDK.

Enabling conditional control in your flow is a game-changer for complex tasks.

This feature allows you to associate each node in a flow with an activate config, which is essentially a "when" statement that determines when a node should be executed.

With conditional control, you can configure your nodes to execute only when the specified conditions are met, making your workflow more efficient.

You can set the activate config for a node by selecting the Activate config button in the node card.

This gives you the power to add "when" statements and set conditions, making your flow more dynamic and responsive to changing circumstances.

You can reference the flow input or node output to set the conditions, for example, by using ${input.[input name]} as a specific value or ${[node name].output} as a specific value.

If the condition isn't met, the node is skipped, and the node status is shown as "Bypassed".

Deploying an Azure Prompt Flow is a crucial step in making it available for use. You can deploy your flow as an online endpoint in Azure Machine Learning, which integrates it into your application.

To make your flow accessible, you'll need to follow the instructions in the article "Deploy flows to Azure Machine Learning managed online endpoint for real-time inference". This will give you a seamless experience.

Managing your flows effectively is essential for optimal performance. You can utilize the CLI and SDK on Azure AI to manage your flows.

Deploying your flow as an online endpoint is a crucial step in making it accessible to your application. This process involves integrating your flow into your application and making it available for use.

To deploy your flow as an online endpoint, you'll need to follow the instructions in the Azure Machine Learning documentation. Specifically, you can find more information on how to deploy your flow by reading the article "Deploy flows to Azure Machine Learning managed online endpoint for real-time inference."

Deploying your flow as an online endpoint allows you to make it available for real-time inference. This means you can use your flow to make predictions or take actions in real-time, which can be incredibly powerful for applications that require fast and accurate decision-making.

The process of deploying your flow as an online endpoint is straightforward and can be completed using the Azure Machine Learning platform. By following the instructions in the documentation, you can easily integrate your flow into your application and start using it in real-time.

Managing your flows is crucial for optimal performance, and Azure AI provides tools to help you do just that.

To effectively manage your flows, you'll want to familiarize yourself with the CLI and SDK, which offer a range of commands and tools to help you get the job done.

Understanding the tools available to you is essential for managing flows using the CLI and SDK on Azure AI.

You can manage your flows using the CLI and SDK, which provide a comprehensive overview of how to utilize these tools for optimal flow management.

The CLI and SDK offer a range of commands and tools to help you manage your flows, including managing flows with CLI and SDK.

By utilizing these tools, you'll be able to effectively manage your flows and get the most out of your Azure AI experience.

To use Azure Prompt Flow, you can work directly in code or use an integrated development environment (IDE) like Jupyter, PyCharm, or Visual Studio. You can modify the YAML code in the flow.dag.yaml file and trigger a single flow run for testing using the Prompt Flow CLI or SDK in the terminal.

Azure AI Studio is a comprehensive platform for creating bespoke copilots and advanced generative AI applications. It offers project management, generative AI development, AI model exploration, and retrieval augmented generation (RAG) features.

You can use the Promptflow Python Library Reference for detailed documentation on SDK usage. The primary class for flow operations is promptflow.azure.PFClient, which provides common operations such as flow creation, modification, and deletion.

Azure AI Studio requires at least one Azure AI Hub to utilize its features and capabilities. An Azure AI Hub can host one or more projects, each encapsulating the tools and resources used to create a specific AI solution.

An Azure AI Hub is the central hub for AI development projects on Azure, allowing you to define shared resources that can be used across multiple projects.

To utilize the features and capabilities of AI Studio, at least one Azure AI Hub is required. This hub can host one or more projects, each encapsulating the tools and resources used to create a specific AI solution.

Each project in an Azure AI Hub is a collaborative workspace for the development and management of AI solutions. This means you can create a project to facilitate collaboration between data scientists and developers in building a custom Copilot business application or process.

An Azure AI Hub provides a workspace for collaborative AI development, and you can create it while creating a new project or by using Azure AI Studio to create an AI Hub resource in your Azure subscription. This creates an AI Hub resource in the resource group you specify.

Additional Azure resources are created to provide supporting services, including a storage account, a key vault, a container registry, an Application Insights resource, and an Azure OpenAI Service resource.

Azure AI Studio allows you to build a custom copilot using your own data to ground prompts, ensuring your copilot's responses are grounded in reality and specific context.

This approach is crucial for efficient data search, which is essential when creating a copilot that uses your own data to generate accurate responses.

You can upload files or folders to the storage used by your AI Studio project, giving you more control over the data your copilot uses.

Azure AI Studio, integrated with Azure AI Search, allows you to retrieve relevant context in your chat flow, making it easier to build a grounded copilot.

Azure AI Search is a retriever you can include when building a language model application with Prompt Flow, allowing you to bring your own data and index it for efficient querying.

This capability enables you to query the index to retrieve any needed information, making your copilot's responses even more accurate and relevant.

Azure AI Studio is a comprehensive platform that empowers developers and data scientists to create bespoke copilots and advanced, market-ready, responsible generative AI applications.

To use Azure AI Studio, you'll need to create an Azure AI Hub, which serves as a collaborative workspace for the development and management of AI solutions. An Azure AI Hub can host one or more projects, each encapsulating the tools and resources used to create a specific AI solution.

Azure AI Studio provides robust tools for the assessment and monitoring of your prompt flows and AI models, ensuring they meet the desired performance metrics. This includes features like AI Model Evaluation and Retrieval Augmented Generation (RAG), which enhance the quality and relevance of the generated content.

To collaborate effectively on an AI project, it's essential to set up a centralized code repository. This enables efficient organization, change tracking, and collaboration among team members.

You can use Azure AI Studio to create an Azure AI Hub, or you can create a hub while creating a new project. This creates an AI Hub resource in your Azure subscription in the resource group you specify, providing a workspace for collaborative AI development.

Here are some key features of Azure AI Studio:

By following the steps outlined in the collaborative development best practices, you can efficiently develop and refine your AI solutions. This includes setting up a centralized code repository, authoring and testing your flow locally, submitting batch runs and evaluation runs to the cloud, and managing run results in the Azure Machine Learning studio workspace UI.

Now that you've set up your Azure prompt flow, it's time to take it to the next level. You can do this by running a batch with more data to evaluate the flow's performance.

To improve your flow's performance, you can also tune your prompts using variants. This will help you get the most out of your Azure AI capabilities.

Here are some specific next steps to consider:

Consider exploring the following resources to further enhance your understanding and capabilities with Azure AI: