Explore Azure OpenAI Playground Features and Tools

The Azure OpenAI Playground is a fantastic tool that lets you experiment with OpenAI's cutting-edge technology right in your browser. You can access it for free with an Azure account.

One of the most exciting features is the ability to generate text based on a prompt, which can be as simple as a sentence or as complex as a paragraph. This is made possible by the Playground's integration with Azure's Cognitive Services.

With the Playground, you can also fine-tune pre-trained models to suit your specific needs, which is a game-changer for many applications. This means you can customize the model to perform better on your particular use case.

The Playground also includes a range of tools and features to help you get started with OpenAI technology, including a model selector, a text input field, and a results pane.

The Azure OpenAI Playground is an intuitive platform that allows you to explore the capabilities of OpenAI's models without requiring any prior coding experience.

To get started, you'll first need to sign up for an Azure account, which can be done in just a few minutes.

Once you have an Azure account, you can access the OpenAI Playground by navigating to the Azure portal and searching for "OpenAI Playground" in the search bar.

To get started with Azure OpenAI, you'll first need to create a deployment. This is where you select the OpenAI model you want to use, such as gpt-35-turbo.

You can create a new deployment by clicking the "Create New Deployment" button on the notice page, or by navigating to the "Management" menu and selecting "Deployments". From there, click the link "Create New Deployment" on the manage deployments page.

A box will pop up where you can select a model, a version, and name the deployment. For this tutorial, use "gpt-35-turbo" and leave the model version as the default. Then, name the deployment and click on "Create".

Once you have a deployment, you can click on the link for the home page and start exploring the available playgrounds.

To use the Playground, you'll need to be aware of the limitations of the models. Currently, the models don't support the same set of parameters as other models, so you can't use common parameters like temperature.

You may need to upgrade your version of the OpenAI Python library to take advantage of the new max_completion_tokens parameter. This is a good opportunity to check if your library is up to date.

Azure OpenAI Embeddings QnA is a simple web application that uses Azure OpenAI Service to create embeddings vectors from documents.

This application enables document search with OpenAI capabilities, making it a powerful tool for information retrieval.

It retrieves the most relevant document for a user's question and then uses GPT-3 to extract the matching answer, making it a seamless experience for users.

OpenAI's completions feature allows users to build on existing text, such as articles, emails, or chat logs, by generating new text that continues the conversation or expands on the original content.

With completions, users can interact with a model in a more natural way, as if they're having a conversation with a human. This feature is particularly useful for tasks like writing articles, creating content, or even just generating ideas.

The model can complete a wide range of tasks, from answering questions to generating entire paragraphs of text. It can even complete code snippets, making it a valuable tool for developers.

One of the key benefits of completions is its ability to generate text that's relevant to the context, making it easier to build on existing ideas and create new ones. This feature is also helpful for users who want to explore different creative directions without starting from scratch.

The model's ability to learn from context and adapt to the user's style and tone makes it a powerful tool for content creation and idea generation.

You can configure and train an AI chatbot using the Chat Playground. There are three panels to set up and use the chatbot: one for assistant setup, one for the chat itself, and one for configuration and parameters.

The chat session panel is where you type in questions for the chatbot and click the send button. The model will respond based on the information provided in the system message.

You can select a built-in template for the system message using a dropdown, like "Marketing Writing Assistant." Remember that the system message counts against the 4000 token limit.

To use the chatbot, you'll need to set up the system message in the assistant setup panel. You can modify the built-in system messages before using them, but keep in mind that changing the system message will reset the chat window.

In the configuration panel, you can find the "Parameters" tab, which holds parameters similar to what you saw in the Completions Playlist. The main difference is that it has a "Max Response" setting instead of "Max length."

The "Max Response" setting limits the number of tokens used for any one response in the chat.

GPT-4 Models are quite impressive. GPT-4o integrates text and images in a single model, enabling it to handle multiple data types simultaneously.

This multimodal approach enhances accuracy and responsiveness in human-computer interactions. It matches GPT-4 Turbo in English text and coding tasks while offering superior performance in non-English languages and vision tasks.

GPT-4o and GPT-4o mini models are available for deployment. You need to create or use an existing resource in a supported standard or global standard region where the model is available.

Here are the model names for programmatic deployment:

To deploy the models, simply create your resource in a supported region and use the corresponding model name.

DALL-E is a model that generates images from text prompts. It's available for use with the REST APIs.

DALL-E 3 is generally available for use with the REST APIs. This means you can start using it right away to create images from text.

DALL-E 2 and DALL-E 3 with client SDKs are in preview. This suggests that they're still being tested and refined before they're fully released.

OpenAI's text to speech models are currently in preview, which means you can use them to synthesize text into speech.

You can access these models via Azure AI Speech, making it easy to integrate them into your existing workflow.

The OpenAI text to speech voices can be used via Azure OpenAI Service or via the Azure AI Speech guide for more information on how to get started.

OpenAI's text to speech models are a powerful tool for creating engaging audio content, and being in preview means you can experiment with them now.

To access the Azure OpenAI o1-preview and o1-mini models, you need to create or use an existing resource in a supported region where the model is available.

The o1-preview model is the most capable in the o1 series, offering enhanced reasoning abilities, and can handle input requests of up to 128,000 tokens and output responses of up to 32,768 tokens.

To deploy the GPT-4o models, you can use the model names gpt-4oVersion2024-08-06, gpt-4o, Version2024-05-13, or gpt-4o-miniVersion2024-07-18.

Here's a quick reference table for the o1-preview and o1-mini models:

GPT-3.5 models can understand and generate natural language or code. The most capable and cost-effective model in the GPT-3.5 family is GPT-3.5 Turbo, which has been optimized for chat and works well for traditional completions tasks as well.

GPT-3.5 Turbo is available for use with the Chat Completions API, and it's recommended over legacy GPT-3.5 and GPT-3 models.

The GPT-3.5 family includes several models, each with its own capabilities and limitations. Here are some key facts about each model:

It's worth noting that GPT-3.5 Turbo Instruct has similar capabilities to text-davinci-003 using the Completions API instead of the Chat Completions API.

The o1-preview and o1-mini models are specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability.

These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.

The o1-preview model is the most capable model in the o1 series, offering enhanced reasoning abilities.

It has a maximum request limit of 128,000 input tokens and 32,768 output tokens.

The o1-mini model is a faster and more cost-efficient option in the o1 series, ideal for coding tasks requiring speed and lower resource consumption.

It has a maximum request limit of 128,000 input tokens and 65,536 output tokens.

Here's a comparison of the two models:

To access the GPT-4o and GPT-4o mini models, you'll need to create or use an existing resource in a supported standard or global standard region where the model is available.

You can deploy the GPT-4o models once your resource is created. If you're performing a programmatic deployment, the model names are gpt-4oVersion2024-08-06, gpt-4o, Version2024-05-13, and gpt-4o-miniVersion2024-07-18.

The GPT-4o and GPT-4o mini models are available for standard and global-standard model deployment. This means you can use them in various regions, but you'll need to check the specific requirements for each region.

To deploy the GPT-4o models, you'll need to know the correct model names. Here are the model names you'll need to use for programmatic deployment:

API Management is a crucial aspect of Model Access and Management. It helps organizations to securely expose their models as APIs, making them accessible to various stakeholders.

APIs can be consumed by different applications, such as web, mobile, or IoT devices, which can greatly expand the reach of a model.

A well-implemented API Management strategy can also help to reduce the complexity of managing multiple APIs, by providing a single interface for monitoring, securing, and analyzing API performance.

API gateways can be used to add security features, such as authentication and rate limiting, to protect APIs from unauthorized access and abuse.

API keys can be used to authenticate and authorize API requests, and can be managed through an API Management platform.

You can deploy Azure OpenAI Playground to Azure, which involves clicking on the "Deploy to Azure" button and configuring your settings in the Azure Portal.

To deploy on Azure, you'll need to clone the repo first, as described in the deployment instructions.

You can also deploy GPT-4 Turbo with Vision GA from the Studio UI, where you'll select GPT-4 and choose the turbo-2024-04-09 version from the dropdown menu.

To deploy on WebApp + Redis Stack, click on the Deploy to Azure button and configure your settings in the Azure Portal as described in the Environment variables section. This is where you'll set up the necessary details for a smooth deployment.

First, you'll need to clone the repo, as mentioned in the example. This will give you a local copy of the code to work with.

The Azure Portal is where you'll find the Environment variables section, which is crucial for configuring your settings. Make sure to follow the instructions provided there for a successful deployment.

To deploy the GA model from the Studio UI, select GPT-4 and then choose the turbo-2024-04-09 version from the dropdown menu.

The default quota for the gpt-4-turbo-2024-04-09 model will be the same as the current quota for GPT-4-Turbo.

See the regional quota limits for more information.

The Azure OpenAI Playground is a powerful tool that requires some setup to get started. You'll need to configure environment variables to connect your Azure OpenAI resource.

To begin, you'll need to set the OPENAI_API_BASE variable to the URL of your Azure OpenAI resource, which can be found in the Azure Portal. This is crucial for making API calls to your resource.

Here are some essential environment variables to get you started:

By setting these variables, you'll be able to connect your Azure OpenAI resource and start exploring its capabilities.

Environment Variables are crucial for your Azure OpenAI resource to function smoothly. They are essentially settings that determine how your resource behaves and interacts with other services.

To set up Environment Variables, you'll need to define a few key settings. The OPENAI_ENGINES setting, for example, determines the instruction engines deployed in your Azure OpenAI resource. By default, it's set to text-davinci-003.

You'll also need to define the OPENAI_API_BASE setting, which is the URL for your Azure OpenAI resource. This can be found in the Azure Portal.

Here's a list of the most important Environment Variables you'll need to set up:

These settings will get you started with setting up Environment Variables for your Azure OpenAI resource. Remember to replace the placeholder values with your actual Azure OpenAI resource name, API key, and other settings.

The GPT-4o-realtime-preview model is designed for low-latency, conversational interactions. It's perfect for support agents, assistants, translators, and other use cases that require highly responsive back-and-forth with a user.

To use GPT-4o audio, you need to create or use an existing resource in one of the supported regions, which are the East US 2 (eastus2) and Sweden Central (swedencentral) regions.

GPT-4o audio is available for deployment once your resource is created. If you're performing a programmatic deployment, the model name is gpt-4o-realtime-preview.

You can use the following model for real-time audio processing: gpt-4o-realtime-preview (2024-10-01-preview). It has a maximum request of 128,000 tokens for input and 4,096 tokens for output.

These models can only be used with the Chat Completion API.

Azure OpenAI Playground offers a range of models, including text-davinci-003 and text-curie-001, each with its own strengths and capabilities.

The text-davinci-003 model is more advanced and can generate longer and more coherent responses, while the text-curie-001 model is more concise and suitable for shorter tasks.

One key difference between the two models is their level of creativity, with text-davinci-003 being more capable of generating novel and original ideas.

OpenAI's GPT-4 Turbo model supports JSON mode and function calling for all inference requests.

Azure OpenAI's version of the same model, however, doesn't support JSON mode and function calling when making inference requests with image (vision) input.

But don't worry, text-based input requests do support JSON mode and function calling, so you can still get the most out of your AI model.

Here's a quick summary of the differences in a table:

This highlights the importance of choosing the right AI model for your specific needs.

When comparing the two versions of gpt-4, one key difference stands out: the Azure AI specific Vision enhancements integration.

This integration is not supported for gpt-4 Version: turbo-2024-04-09, which means you won't be able to use features like Optical Character Recognition (OCR), object grounding, or video prompts.

These features are currently available in the preview version, but they will be retired and no longer available once the upgrade to turbo-2024-04-09 is complete.

If you're relying on any of these preview features, be aware that the automatic model upgrade will be a breaking change, so plan accordingly.