1. Incremental Static Regeneration for AI Model Documentation on Vercel


    Incremental Static Regeneration (ISR) is a feature provided by Vercel that allows you to update static content after it has been deployed, without the need to rebuild and redeploy the entire site. This feature is especially useful for sites with large amounts of static content that might need frequent updates, like AI model documentation.

    To use ISR with Pulumi to deploy AI model documentation on Vercel, you would typically follow these steps:

    1. Set up your project with documentation – your documentation source files would be organized in your project repository.

    2. Configure ISR settings in Vercel – You can specify ISR settings within the Vercel configuration file (e.g., vercel.json) in your project.

    3. Deploy your site using Pulumi – You define your Vercel project and its deployment settings with Pulumi, which interacts with the Vercel API.

    4. Trigger revalidation – Revalidation for updating content without a full redeployment could be handled through Vercel's revalidation API or by simply re-uploading the changed documentation file using Pulumi.

    Let's assume you have already set up a git repository containing the source files for your AI model documentation and want to deploy this using Pulumi. Here is a Python Pulumi program that defines the necessary resources for deploying a site on Vercel with ISR settings:

    import pulumi import pulumi_vercel as vercel # Step 1: Create a new Vercel Project for your AI model documentation # Replace 'your-team-id' with your actual Vercel team ID. # The 'name' should be the name you wish to give to your Vercel project. project = vercel.Project( 'ai-model-docs-project', team_id='your-team-id', name='ai-model-docs', # Define your ISR and other project settings here project_settings=vercel.ProjectSettingsArgs( framework='nextjs', # Assuming ISR is being used with Next.js build_command='yarn build', # Your build command may vary output_directory='out', # Directory name where your static files are located after the build # ... Other settings ), # Define environment variables, domains, etc., if necessary ) # Step 2: Define your Deployment # You must define the reference of your git repository, and set production to 'True' if you're deploying to production. deployment = vercel.Deployment( 'ai-model-docs-deployment', team_id='your-team-id', projectId=project.id, production=True, # Assuming the structure of your project allows for ISR, no other settings are required here. # Vercel detects ISR settings from your `vercel.json` file or framework configuration. ) # Optional # If you require custom domains or aliases for your deployment, define them here. alias = vercel.Alias( "custom-domain-alias", team_id='your-team-id', deploymentId=deployment.id, alias="docs.example.com", # Replace with your custom domain ) # Export the URL of the deployed site pulumi.export('deployment_url', deployment.url) # Export the alias URL if you have set a custom domain pulumi.export('alias_url', alias.alias)

    This code would create a new Vercel project for your AI model documentation and define a deployment for this project. The details such as team ID, build commands, the output directory for the generated static files, and custom domains would be specific to your project's settings and requirements.

    Please remember to replace placeholders like 'your-team-id' with your actual Vercel Team ID and other settings to match your project's configuration. Additionally, ensure that the Pulumi CLI is set up correctly with access to Vercel through the appropriate API tokens.

    Once you apply your Pulumi program, your site will be deployed to Vercel, and you can take advantage of ISR by following the framework's (such as Next.js) documentation on how to trigger revalidation requests.