I'll create a table with the specified columns and pagination functionality. This type of table is commonly used for storing vector embeddings in AI applications 1.

This table displays three columns:

1. ID: A simple numeric identifier
2. Metadata: JSON data displayed in a formatted pre tag
3. Embedding: An array of numbers (representing vector embeddings) 1

The pagination controls at the bottom allow you to navigate through the data in increments of 10 items at a time. The table shows the current range of items being displayed out of the total.

In a real application, you would typically:

1. Fetch this data from a database like Supabase or Neon that supports vector columns 21
2. Use server actions or API routes to handle the pagination logic
3. Possibly add filtering or searching capabilities, especially for the metadata fields

If you want to implement this with a real database, you could create a table structure similar to this in Supabase or Neon with pgvector enabled 31.

I'll create a cleaner version with smaller rows and a sheet that opens when you click on a row to show the full details.

This improved version has several enhancements:

1. Cleaner Table Rows:

   • Rows are now more compact with just essential information
   • Metadata shows only the title and tags
   • Embedding column just shows the dimension count
   • Added a visual indicator that rows are clickable

2. Detail Sheet:

   • When you click on any row, a sheet slides in from the right
   • The sheet displays the complete JSON metadata in a formatted view 1
   • The full embedding array is shown with proper formatting
   • Added a close button at the bottom of the sheet

3. Maintained Pagination:

   • The pagination controls work the same way, incrementing by 10 items

This approach is much more user-friendly for tables with complex data like JSON metadata and vector embeddings 23. It keeps the main view clean while still providing access to all the details when needed.

The pattern of showing a summary in the table and details in a sheet/modal is commonly used in applications that deal with structured and unstructured data, like vector databases for AI applications 2.