Alternatively, you can find the value in the Azure OpenAI Studio > Playground > Code View. This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. To successfully make a call against Azure OpenAI, you'll need an endpoint and a key. NET client library with: dotnet add package Azure.AI.OpenAI -prerelease The build output should contain no warnings or errors. You can build the application with: dotnet build dotnet new console -n azure-openai-quickstartĬhange your directory to the newly created app folder. This command creates a simple "Hello World" project with a single C# source file: Program.cs. In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name azure-openai-quickstart. I ran into an issue with the prerequisites. The Davinci based model in this example is well-suited to this type of summarization, whereas a Codex based model wouldn't perform as well at this particular task. The accuracy of the response can vary per model. These incredibly dense objects are incredibly fascinating due to their strange properties and their potential for phenomena such as extreme gravitational forces and a strong magnetic field. You should get a result that resembles the following text: Tl dr A neutron star is the collapsed core of a supergiant star. Azure OpenAI will attempt to capture the context of text and rephrase it succinctly. Select Summarize Text from the Examples dropdown. If your resource doesn't have a deployment, select Create a deployment and then revisit this step. Select your deployment from the Deployments dropdown. Select GPT-3 Playground at the top of the landing page. Select the subscription and OpenAI resource to work with. To use the Azure OpenAI for text summarization in the GPT-3 Playground, follow these steps: You can write an application to complete the same task with the OpenAI Python SDK, curl, or other REST API client. Just select View code next to the examples dropdown. In the GPT-3 playground you can also view Python and curl code samples pre-filled according to your selected settings. For more information, see the content filter article. The prompts or responses may be filtered if harmful content is detected. Select the Regenerate button to complete an undo and generation call together.Īzure OpenAI also performs content moderation on the prompt inputs and generated outputs.Select the Undo button to undo the prior generation call.Selecting the Generate button will send the entered text to the completions API and stream the results back to the text box.You can read more about each parameter in the REST API. You can experiment with the configuration settings such as temperature and pre-response text to improve the performance of your task. For more information about model deployment, see the resource deployment guide. If your resource doesn't have a deployment, select Create a deployment and follow the instructions provided by the wizard. You can select a deployment and choose from a few pre-loaded examples to get started. From this page, you can quickly iterate and experiment with the capabilities. It's simply a text box where you can submit a prompt to generate a completion. Start exploring Azure OpenAI capabilities with a no-code approach through the GPT-3 Playground. Go to the Playground for experimentation and fine-tuning workflow. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.įrom the Azure OpenAI Studio landing page navigate further to explore examples for prompt completion, manage your deployments and models, and find learning resources such as documentation and community forums. Navigate to Azure OpenAI Studio at and sign-in with credentials that have access to your OpenAI resource.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |