Local Supabase Development

Prerequisite: You should have installed Docker and followed the Development setup guide.

Overview

Tuturuuu utilizes Supabase for database management and authentication. This guide explains how to work efficiently with Supabase in the local development workflow.

Basic Commands

Starting Supabase

To start a local Supabase instance tailored for Tuturuuu:

pnpm sb:start

This command launches a local Supabase instance on your machine, accessible at http://localhost:8003.

Stopping Supabase

To stop the local Supabase instance:

pnpm sb:stop

Checking Status

To view the current URLs and status of your local Supabase instance:

pnpm sb:status

Development Workflow

Recommended Startup

When developing for Tuturuuu, you have several options to start your environment:

Standard Approach: Start Next.js apps and Supabase separately.

pnpm dev      # Starts all Next.js apps
pnpm sb:start # Starts Supabase

Enhanced Development Experience: Use the devx command for a streamlined setup.
```
pnpm devx
```
This command:
- Stops any running Supabase instance and saves current data as backup
- Installs all dependencies
- Starts a new Supabase instance (using backed up data)
- Starts all Next.js apps in development mode
Fresh Database Setup: When switching branches with potential schema changes.
```
pnpm sb:devrs
```
This command:
- Stops any running Supabase instance (without backup)
- Installs all dependencies
- Starts a new Supabase instance
- Resets the database to use the latest schema
- Starts all Next.js apps in development mode

Use pnpm sb:devrs when switching between branches that might have different database migrations to ensure your local database schema matches the branch you’re working on.

Syncing With Schema Changes

If you’re keeping your Next.js server running but need to reset your database to match the current branch’s schema:

pnpm sb:reset

This command will reset your local database to use the latest schema definitions and automatically regenerate TypeScript types.

Database Schema Management

Making Schema Changes

There are two approaches to modifying the database schema:

1. Using the Supabase UI

Navigate to your local Supabase Studio at http://localhost:8003
Make your changes through the UI
Generate a migration file:
```
pnpm sb:diff
```
This creates a migration file based on the differences between your current schema and the previous state.

Be cautious when using sb:diff for schema changes. If you rename columns or tables, the migration will drop the old ones and create new ones, which can result in data loss in production environments.

2. Creating Manual Migrations

For more control, you can create empty migration files and populate them manually:

pnpm sb:new

This creates a new empty migration file in apps/db/supabase/migrations that you can edit to include your desired schema changes.

Applying Migrations

After creating a migration, apply it to your local database:

pnpm sb:up

This same process is used to keep our production database up-to-date with the schema defined in the production branch.

Generating TypeScript Types

After schema changes, regenerate the TypeScript types to keep your code in sync with the database schema:

pnpm sb:typegen

Alternatively, you can use the shorthand:

pnpm typegen

This step is automatically performed when running pnpm sb:reset, making it useful when catching up with a new branch’s schema.

Using Generated TypeScript Types

The Supabase-generated TypeScript types are available at packages/types/src/supabase.ts. These types are accessible to all apps that have the @tuturuuu/types package installed.

You can use these types to ensure type safety when working with Supabase data:

import type { Database } from '@tuturuuu/types/supabase';

// Type-safe access to tables
const { data, error } = await supabase
  .from<
    Database['public']['Tables']['workspace_members']['Row']
  >('workspace_members')
  .select('*')
  .eq('ws_id', workspaceId);

// Type-safe access to specific columns
const { data: workspace } = await supabase
  .from('workspaces')
  .select('id, name, handle')
  .eq('id', workspaceId)
  .single();

// TypeScript knows the structure of 'workspace' with proper types
const workspaceName: string = workspace?.name;

Short-hand Type Access

For more convenient access to common table types, Tuturuuu also provides short-hand type definitions in packages/types/src/db.ts. These are easier to use and remember than the full database type paths:

import type { WorkspaceCourse, WorkspaceRole } from '@tuturuuu/types/db';

// Use short-hand types directly
const { data: roles } = await supabase
  .from('workspace_roles')
  .select('*')
  .eq('ws_id', workspaceId);

// Type is now WorkspaceRole[]
roles?.forEach((role: WorkspaceRole) => {
  console.log(role.name, role.permissions);
});

// Short-hand types can also include extended properties
const course: WorkspaceCourse = {
  id: 'course-id',
  ws_id: 'workspace-id',
  name: 'Course Name',
  created_at: new Date().toISOString(),
  updated_at: new Date().toISOString(),
  href: '/courses/course-id', // Extended property not in the database
};

You can add your own short-hand types to db.ts for tables you frequently work with. This is especially useful for tables that have complex structures or need additional client-side properties.

This ensures that your code correctly interacts with the database schema, reducing runtime errors and improving development experience.

Migration Files

All migration files are stored in apps/db/supabase/migrations. These files:

Contain SQL commands that create and modify the database schema
Are executed in order based on the timestamp prefix in their filenames
Include descriptive names after the timestamp to help developers understand their purpose

When contributing new migrations in a Pull Request, always add them after the latest migration file from the main branch. This maintains the correct execution order and prevents issues when syncing the production database.

Local Authentication

A local mail server (InBucket) is automatically set up by Supabase to handle authentication emails. You can access it at http://localhost:8004.

With InBucket, you can:

Receive all authentication emails sent by your local Supabase instance
Test any email combination without needing actual mail delivery
View password reset links, confirmation emails, and other authentication flows
Troubleshoot email templates and content

This makes it easy to test different authentication scenarios without configuring a real email service or waiting for actual email delivery.

Five seed accounts are pre-configured for local development:

These accounts are already set up with the necessary data, allowing you to quickly test the app’s functionality. However, you can register any new email address and the authentication emails will be captured by InBucket for you to inspect.

Further Information

For more details about Supabase CLI usage, refer to the Supabase CLI documentation.

Row Level Security (RLS)

Row Level Security (RLS) is a powerful Postgres feature that allows you to control access to rows in a database table based on the user making the request. In Tuturuuu, we use RLS extensively to ensure data security.

Enabling RLS

RLS should be enabled on all tables in exposed schemas (like public). When creating tables through the Supabase UI, RLS is enabled by default. For tables created using SQL, you need to explicitly enable RLS:

alter table <schema_name>.<table_name> enable row level security;

Creating RLS Policies

Policies define the conditions under which users can access or modify data. Here are some common patterns used in Tuturuuu:

Organization-based Access

In Tuturuuu, most resources belong to an organization (workspace). Here’s how to create policies for organization-based access:

-- Allow users to select data from their organizations
create policy "Users can view data from their organizations"
on public.table_name
for select
to authenticated
using (
  auth.uid() in (
    select user_id from public.workspace_members
    where workspace_id = table_name.workspace_id
  )
);

-- Allow organization admins to insert data
create policy "Organization admins can insert data"
on public.table_name
for insert
to authenticated
with check (
  auth.uid() in (
    select user_id from public.workspace_members
    where workspace_id = table_name.workspace_id
    and role = 'admin'
  )
);

-- Allow organization admins to update data
create policy "Organization admins can update data"
on public.table_name
for update
to authenticated
using (
  auth.uid() in (
    select user_id from public.workspace_members
    where workspace_id = table_name.workspace_id
    and role = 'admin'
  )
)
with check (
  auth.uid() in (
    select user_id from public.workspace_members
    where workspace_id = table_name.workspace_id
    and role = 'admin'
  )
);

-- Allow organization admins to delete data
create policy "Organization admins can delete data"
on public.table_name
for delete
to authenticated
using (
  auth.uid() in (
    select user_id from public.workspace_members
    where workspace_id = table_name.workspace_id
    and role = 'admin'
  )
);

Role-based Access

For more granular control based on user roles:

-- Allow members with specific permission to access a resource
create policy "Members with view_projects permission can view projects"
on public.projects
for select
to authenticated
using (
  exists (
    select 1 from public.workspace_member_permissions
    where user_id = auth.uid()
    and workspace_id = projects.workspace_id
    and permission = 'view_projects'
  )
);

Performance Optimization for RLS

For better performance in your RLS policies:

Wrap function calls in subqueries:

-- Instead of this
using (auth.uid() = user_id);

-- Use this
using ((select auth.uid()) = user_id);

Use security definer functions for complex access logic:

create or replace function private.can_access_workspace(workspace_uuid uuid)
returns boolean
language plpgsql
security definer
as $$
begin
  return exists (
    select 1 from public.workspace_members
    where user_id = auth.uid()
    and workspace_id = workspace_uuid
  );
end;
$$;

-- Use the function in your policy
create policy "Users can access workspace data"
on public.table_name
for select
to authenticated
using (private.can_access_workspace(workspace_id));

Add explicit filters in your queries even when you have RLS:

// Even though RLS will filter by workspace_id, adding the filter explicitly improves performance
const { data } = await supabase
  .from('projects')
  .select()
  .eq('workspace_id', workspaceId);

Testing RLS Policies

To test your RLS policies during local development:

Create a SQL file in apps/db/supabase/tests with your test cases
Use the pnpm sb:test command to run the tests

Example test file:

-- Test normal user can view their own workspace
BEGIN;
  SET LOCAL ROLE authenticated;

  -- Mock the auth.uid() function
  CREATE OR REPLACE FUNCTION auth.uid() RETURNS UUID LANGUAGE SQL AS 'SELECT ''user1-uuid''::UUID';

  -- Should return data
  SELECT is(
    (SELECT count(*) FROM public.projects WHERE workspace_id = 'workspace1-uuid'),
    1,
    'User should be able to see their workspace project'
  );

  -- Should return no data
  SELECT is(
    (SELECT count(*) FROM public.projects WHERE workspace_id = 'workspace2-uuid'),
    0,
    'User should not be able to see projects from other workspaces'
  );
ROLLBACK;

Database Triggers

Triggers in Postgres allow you to automatically execute a function when a specified database event occurs (INSERT, UPDATE, DELETE). In Tuturuuu, we use triggers for various purposes like:

Maintaining audit logs
Syncing data between tables
Enforcing complex business rules

Creating Triggers

Here’s how to create a trigger in your Tuturuuu development workflow:

First, create a trigger function:

create or replace function public.handle_new_user()
returns trigger
language plpgsql
security definer
as $$
begin
  -- Create a personal workspace for the new user
  insert into public.workspaces (id, name, created_by)
  values (gen_random_uuid(), new.email || '''s workspace', new.id);

  -- Make the user an admin of their personal workspace
  insert into public.workspace_members (workspace_id, user_id, role)
  values (
    (select id from public.workspaces where created_by = new.id),
    new.id,
    'admin'
  );

  return new;
end;
$$;

Then, create the trigger:

create trigger on_auth_user_created
  after insert on auth.users
  for each row
  execute function public.handle_new_user();

Common Triggers in Tuturuuu

Audit Logging

create or replace function private.audit_log_changes()
returns trigger
language plpgsql
security definer
as $$
begin
  insert into public.audit_logs (
    table_name,
    record_id,
    action,
    old_data,
    new_data,
    performed_by
  )
  values (
    TG_TABLE_NAME,
    coalesce(new.id, old.id),
    TG_OP,
    case when TG_OP = 'DELETE' or TG_OP = 'UPDATE' then row_to_json(old) else null end,
    case when TG_OP = 'INSERT' or TG_OP = 'UPDATE' then row_to_json(new) else null end,
    coalesce(auth.uid(), '00000000-0000-0000-0000-000000000000'::uuid)
  );
  return coalesce(new, old);
end;
$$;

-- Apply this trigger to a table
create trigger projects_audit_trigger
  after insert or update or delete
  on public.projects
  for each row
  execute function private.audit_log_changes();

Automated Timestamps

create or replace function public.update_timestamp()
returns trigger
language plpgsql
as $$
begin
  new.updated_at = now();
  return new;
end;
$$;

-- Apply to a table
create trigger update_projects_timestamp
  before update
  on public.projects
  for each row
  execute function public.update_timestamp();

Testing Triggers

You can test triggers by running SQL commands in the local Supabase instance and verifying the results:

-- Insert a test user
insert into auth.users (id, email)
values ('test-uuid', 'test@example.com');

-- Verify the trigger created a workspace
select * from public.workspaces where created_by = 'test-uuid';

-- Verify the user is an admin of the workspace
select * from public.workspace_members
where user_id = 'test-uuid' and role = 'admin';

Seeding Your Database

Database seeding is the process of populating your database with initial data. In Tuturuuu, we use seeding to:

Create test users and workspaces for local development
Initialize lookup tables with standard values
Ensure a consistent starting point for all developers

Seed Files Location

In Tuturuuu, seed files are stored in apps/db/supabase/seed.sql. This file is automatically executed when you run pnpm sb:reset or start a fresh Supabase instance.

Real Examples from Tuturuuu’s Seed File

Let’s look at some real examples from Tuturuuu’s seed.sql file:

1. Authentication Users

The seed file creates five default test users with pre-set passwords:

-- Populate auth users
INSERT INTO
    "auth"."users" (
        "instance_id",
        "id",
        "aud",
        "role",
        "email",
        "encrypted_password",
        "email_confirmed_at",
        /* other fields... */
    )
VALUES
    (
        '00000000-0000-0000-0000-000000000000',
        '00000000-0000-0000-0000-000000000001',
        'authenticated',
        'authenticated',
        'local@tuturuuu.com',
        crypt('password123', gen_salt('bf')),
        '2023-02-18 23:31:13.017218+00',
        /* other values... */
    ),
    /* additional users... */

All seed users have the same password: password123, making it easy to log in for testing.

2. Workspaces

The seed creates several workspaces for testing different scenarios:

-- Populate workspaces
insert into
    public.workspaces (id, name, handle, creator_id)
values
    (
        '00000000-0000-0000-0000-000000000000',
        'Tuturuuu',
        'tuturuuu',
        '00000000-0000-0000-0000-000000000001'
    ),
    (
        '00000000-0000-0000-0000-000000000001',
        'Prototype All',
        'prototype-all',
        null
    ),
    /* additional workspaces... */

3. Workspace Members and Roles

The seed also sets up relationships between users and workspaces with different roles:

-- Populate workspace_members
insert into
    public.workspace_members (user_id, ws_id, role)
values
    (
        '00000000-0000-0000-0000-000000000001',
        '00000000-0000-0000-0000-000000000000',
        'OWNER'
    ),
    (
        '00000000-0000-0000-0000-000000000002',
        '00000000-0000-0000-0000-000000000000',
        'ADMIN'
    ),
    (
        '00000000-0000-0000-0000-000000000003',
        '00000000-0000-0000-0000-000000000000',
        'MEMBER'
    ),
    /* additional members... */

4. Workspace Features Configuration

The seed file configures workspace features using secrets:

-- Populate workspace_secrets
insert into
    public.workspace_secrets (ws_id, name, value)
values
    (
        '00000000-0000-0000-0000-000000000000',
        'ENABLE_CHAT',
        'true'
    ),
    (
        '00000000-0000-0000-0000-000000000000',
        'ENABLE_EDUCATION',
        'true'
    ),
    /* additional features... */

5. Domain-specific Data

The seed includes domain-specific data for different workspace types. For example, healthcare data:

-- Populate healthcare_vitals
insert into
    public.healthcare_vitals (id, ws_id, name, unit)
values
    (
        '00000000-0000-0000-0000-000000000001',
        '00000000-0000-0000-0000-000000000003',
        'Nhiệt độ',
        '°C'
    ),
    (
        '00000000-0000-0000-0000-000000000002',
        '00000000-0000-0000-0000-000000000003',
        'Chiều cao',
        'cm'
    ),
    /* additional vitals... */

Creating Seed Data

Here’s how to create and modify seed data:

Edit the apps/db/supabase/seed.sql file
Add SQL statements to insert your data
Run pnpm sb:reset to apply the seed data

Example seed data format:

-- Create a new user group
INSERT INTO public.workspace_user_groups (id, name, ws_id)
VALUES
  ('your-uuid-here', 'New Group', 'workspace-uuid-here')
ON CONFLICT (id) DO NOTHING;

-- Add a new workspace feature
INSERT INTO public.workspace_secrets (ws_id, name, value)
VALUES
  ('workspace-uuid-here', 'ENABLE_NEW_FEATURE', 'true')
ON CONFLICT (ws_id, name) DO UPDATE
SET value = EXCLUDED.value;

Creating a Custom Seed File

Sometimes you might want to create a custom seed file for specific testing scenarios:

Create a new SQL file in the apps/db/supabase directory
Add your custom seed data
Run it with the Supabase CLI:

pnpm supabase db reset --db-url=postgresql://postgres:postgres@localhost:54322/postgres
psql postgresql://postgres:postgres@localhost:54322/postgres -f apps/db/supabase/my_custom_seed.sql

Exporting Current Data as Seed

You can also export your current database data to use as seed data:

# Export only data (not schema) to seed.sql
pnpm supabase db dump --db-url=postgresql://postgres:postgres@localhost:54322/postgres --data-only > apps/db/supabase/new_seed.sql

This is helpful when you’ve set up data manually and want to preserve it for future development environments.

Recommended Seeding Workflow

For Tuturuuu development, we recommend:

Start with a fresh database: pnpm sb:reset
Make changes through the UI or your app
When you’re satisfied, export the data: pnpm supabase db dump --data-only > apps/db/supabase/new_seed.sql
Edit the generated SQL to keep only what you need
Update the main seed.sql file with your changes
Test by running pnpm sb:reset again

AI Integration with Vercel AI SDK

Tuturuuu uses Vercel’s AI SDK for its AI features, utilizing structured data generation capabilities that integrate with Supabase. This section covers how to work with AI features in the development workflow.

Overview of AI SDK in Tuturuuu

The AI SDK standardizes integrating various AI models across supported providers into Tuturuuu applications. It enables structured data generation, tool calling, and streaming responses to create rich AI-powered features.

The main libraries used are:

ai - Core Vercel AI SDK package
@ai-sdk/google - Provider-specific integration for Google models
@tuturuuu/supabase - Supabase client with Tuturuuu-specific utilities

Generating Structured Data

Tuturuuu uses the AI SDK’s structured data generation capabilities to create typed responses from AI models. This approach ensures type safety and consistent data structures for features like:

Flashcards generation
Quiz generation
Learning plans
Task management

Example: Flashcard Generation

The structured data pattern used in Tuturuuu follows this workflow:

Define a schema using Zod
Connect to Supabase for authentication and workspace validation
Generate structured data using the AI SDK
Stream the response to the client

Here’s an example from Tuturuuu’s codebase:

// 1. Define the schema
import { z } from 'zod';

export const flashcardSchema = z.object({
  flashcards: z.array(
    z.object({
      front: z.string().describe('Question. Do not use emojis or links.'),
      back: z.string().describe('Answer. Do not use emojis or links.'),
    })
  ),
});

// 2. Setup API endpoint
export async function POST(req: Request) {
  const sbAdmin = await createAdminClient();
  const { wsId, context } = await req.json();

  // Validate user and workspace permissions
  const {
    data: { user },
  } = await supabase.auth.getUser();
  if (!user) return new Response('Unauthorized', { status: 401 });

  // Check workspace feature flag
  const { count, error } = await sbAdmin
    .from('workspace_secrets')
    .select('*', { count: 'exact', head: true })
    .eq('ws_id', wsId)
    .eq('name', 'ENABLE_CHAT')
    .eq('value', 'true');

  if (error) return new Response(error.message, { status: 500 });
  if (count === 0)
    return new Response('You are not allowed to use this feature.', {
      status: 401,
    });

  // 3. Generate structured data using AI SDK
  const result = streamObject({
    model: google('gemini-2.0-flash-001', {
      safetySettings: [
        // Safety settings configuration...
      ],
    }),
    prompt: `Generate 10 flashcards with the following context: ${context}`,
    schema: flashcardSchema,
  });

  // 4. Stream the response to the client
  return result.toTextStreamResponse();
}

Available Models

Tuturuuu supports multiple AI models through Vercel AI SDK. You can define which models are available in your application by updating the models.ts file in the packages/ai directory:

export const models = [
  {
    value: 'gemini-2.0-flash-001',
    label: 'gemini-2.0-flash',
    provider: 'Google',
    description: 'Gemini 2.0 Flash delivers next-gen features...',
    context: 1000000,
  },
  // Add other models here...
];

export const defaultModel = models.find(
  (model) =>
    model.value === 'gemini-2.0-flash-001' && model.provider === 'Google Vertex'
);

Creating Custom Schema Types

To create new structured data types for AI generation, add your schema definition to the packages/ai/src/object/types.ts file:

export const myNewSchema = z.object({
  items: z.array(
    z.object({
      name: z.string().describe('Name of the item'),
      description: z.string().describe('Description of the item'),
      priority: z.enum(['high', 'medium', 'low']).describe('Priority level'),
    })
  ),
});

Integration with Supabase

Tuturuuu’s AI features leverage Supabase for:

Authentication - Validating users before making AI requests
Authorization - Checking workspace permissions via workspace_secrets
Feature Flags - Using workspace_secrets to enable/disable AI features per workspace
Storage - Storing AI-generated content for later use

To enable AI features for a workspace, ensure the appropriate flags are set in the workspace_secrets table:

-- Enable AI chat features for a workspace
INSERT INTO public.workspace_secrets (ws_id, name, value)
VALUES ('your-workspace-id', 'ENABLE_CHAT', 'true');

-- Enable AI document features for a workspace
INSERT INTO public.workspace_secrets (ws_id, name, value)
VALUES ('your-workspace-id', 'ENABLE_DOCS', 'true');

Testing AI Features Locally

When testing AI features in your local environment:

Ensure you have the required API keys set in your .env.local file:
```
GOOGLE_GENERATIVE_AI_API_KEY=your-api-key
```

Verify the workspace has the necessary feature flags enabled in your local database

SELECT * FROM workspace_secrets WHERE ws_id = 'your-workspace-id' AND name = 'ENABLE_CHAT';

Use the AI-enabled accounts from the seed data (local@tuturuuu.com) as they often have additional permissions

Error Handling

When integrating AI features, implement proper error handling to account for:

Missing API keys
Model unavailability
Invalid user input
Exceeded token limits

Example error handling pattern used in Tuturuuu:

try {
  // AI SDK code here
} catch (error: any) {
  console.log(error);
  return NextResponse.json(
    {
      message: `## Edge API Failure\nCould not complete the request. Please view the **Stack trace** below.\n\`\`\`bash\n${error?.stack}`,
    },
    {
      status: 200,
    }
  );
}

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Development Monorepo Architecture

On this page

Overview
Basic Commands
Starting Supabase
Stopping Supabase
Checking Status
Development Workflow
Recommended Startup
Syncing With Schema Changes
Database Schema Management
Making Schema Changes
1. Using the Supabase UI
2. Creating Manual Migrations
Applying Migrations
Generating TypeScript Types
Using Generated TypeScript Types
Short-hand Type Access
Migration Files
Local Authentication
Further Information
Row Level Security (RLS)
Enabling RLS
Creating RLS Policies
Organization-based Access
Role-based Access
Performance Optimization for RLS
Testing RLS Policies
Database Triggers
Creating Triggers
Common Triggers in Tuturuuu
Audit Logging
Automated Timestamps
Testing Triggers
Seeding Your Database
Seed Files Location
Real Examples from Tuturuuu’s Seed File
1. Authentication Users
2. Workspaces
3. Workspace Members and Roles
4. Workspace Features Configuration
5. Domain-specific Data
Creating Seed Data
Creating a Custom Seed File
Exporting Current Data as Seed
Recommended Seeding Workflow
AI Integration with Vercel AI SDK
Overview of AI SDK in Tuturuuu
Generating Structured Data
Example: Flashcard Generation
Available Models
Creating Custom Schema Types
Integration with Supabase
Testing AI Features Locally
Error Handling

Get Started

Features

AI Features

Personalization

Experiments

Prompt Engineering

​Overview

​Basic Commands

​Starting Supabase

​Stopping Supabase

​Checking Status

​Development Workflow

​Recommended Startup

​Syncing With Schema Changes

​Database Schema Management

​Making Schema Changes

​1. Using the Supabase UI

​2. Creating Manual Migrations

​Applying Migrations

​Generating TypeScript Types

​Using Generated TypeScript Types

​Short-hand Type Access

​Migration Files

​Local Authentication

​Further Information

​Row Level Security (RLS)

​Enabling RLS

​Creating RLS Policies

​Organization-based Access

​Role-based Access

​Performance Optimization for RLS

​Testing RLS Policies

​Database Triggers

​Creating Triggers

​Common Triggers in Tuturuuu

​Audit Logging

​Automated Timestamps

​Testing Triggers

​Seeding Your Database

​Seed Files Location

​Real Examples from Tuturuuu’s Seed File

​1. Authentication Users

​2. Workspaces

​3. Workspace Members and Roles

​4. Workspace Features Configuration

​5. Domain-specific Data

​Creating Seed Data

​Creating a Custom Seed File

​Exporting Current Data as Seed

​Recommended Seeding Workflow

​AI Integration with Vercel AI SDK

​Overview of AI SDK in Tuturuuu

​Generating Structured Data

​Example: Flashcard Generation

​Available Models

​Creating Custom Schema Types

​Integration with Supabase

​Testing AI Features Locally

​Error Handling

Overview

Basic Commands

Starting Supabase

Stopping Supabase

Checking Status

Development Workflow

Recommended Startup

Syncing With Schema Changes

Database Schema Management

Making Schema Changes

1. Using the Supabase UI

2. Creating Manual Migrations

Applying Migrations

Generating TypeScript Types

Using Generated TypeScript Types

Short-hand Type Access

Migration Files

Local Authentication

Further Information

Row Level Security (RLS)

Enabling RLS

Creating RLS Policies

Organization-based Access

Role-based Access

Performance Optimization for RLS

Testing RLS Policies

Database Triggers

Creating Triggers

Common Triggers in Tuturuuu

Audit Logging

Automated Timestamps

Testing Triggers

Seeding Your Database

Seed Files Location

Real Examples from Tuturuuu’s Seed File

1. Authentication Users

2. Workspaces

3. Workspace Members and Roles

4. Workspace Features Configuration

5. Domain-specific Data

Creating Seed Data

Creating a Custom Seed File

Exporting Current Data as Seed

Recommended Seeding Workflow

AI Integration with Vercel AI SDK

Overview of AI SDK in Tuturuuu

Generating Structured Data

Example: Flashcard Generation

Available Models

Creating Custom Schema Types

Integration with Supabase

Testing AI Features Locally

Error Handling