Build a Local-First AI App with PGLite in 45 Minutes

Create a privacy-focused web app with client-side AI and browser-based PostgreSQL using PGLite and local LLMs.
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9 min read
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Mark
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Web Development

Problem: Building Privacy-First Apps Without Backend Infrastructure

You want to build an AI-powered app but don't want user data leaving their browser, and you don't want to manage servers or databases.

You'll learn:

  • How to run PostgreSQL entirely in the browser with PGLite
  • Integrate client-side AI using Web LLM or local models
  • Build a complete note-taking app with AI summarization
  • Handle offline-first data persistence

Time: 45 min | Level: Intermediate

Why This Matters

Traditional web apps send everything to servers. Local-first apps keep data on the device, giving users:

Benefits:

  • Zero backend costs (no servers to pay for)
  • Instant responses (no network latency)
  • Complete privacy (data never leaves the browser)
  • Works offline by default

Common use cases:

  • Personal productivity tools (notes, tasks, journals)
  • Privacy-sensitive apps (health tracking, finance)
  • Offline-capable business tools
  • AI assistants without API costs

What You're Building

A note-taking app with AI-powered summaries that:

  • Stores everything in browser PostgreSQL (PGLite)
  • Generates summaries using client-side AI
  • Persists across browser sessions
  • Works completely offline

Tech stack:

  • PGLite 0.2+ (WASM PostgreSQL)
  • Transformers.js 3.0 (browser AI models)
  • React 19 or vanilla JS
  • IndexedDB for persistence

Solution

Step 1: Set Up PGLite

# Create new project
npm create vite@latest my-local-app -- --template react-ts
cd my-local-app

# Install dependencies
npm install @electric-sql/pglite
npm install @xenova/transformers

Expected: Clean Vite project with TypeScript support.

Step 2: Initialize PGLite Database

// src/db.ts
import { PGlite } from '@electric-sql/pglite';

// Create database instance (stored in IndexedDB)
export const db = new PGlite('idb://my-local-app-db');

// Initialize schema
export async function initDB() {
  await db.exec(`
    CREATE TABLE IF NOT EXISTS notes (
      id SERIAL PRIMARY KEY,
      title TEXT NOT NULL,
      content TEXT NOT NULL,
      summary TEXT,
      created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
      updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    );
    
    CREATE INDEX IF NOT EXISTS idx_notes_created 
      ON notes(created_at DESC);
  `);
}

Why this works: idb:// prefix tells PGLite to persist in IndexedDB. Database survives page refreshes and works offline.

If it fails:

  • Error: "IndexedDB not available": Check browser privacy settings, some block IndexedDB
  • Quota exceeded: Browser limits ~50MB-1GB per origin, check storage usage

Step 3: Set Up Client-Side AI

// src/ai.ts
import { pipeline } from '@xenova/transformers';

let summarizer: any = null;

// Load model once (downloads ~40MB, caches locally)
export async function initAI() {
  if (!summarizer) {
    summarizer = await pipeline(
      'summarization',
      'Xenova/distilbart-cnn-6-6' // Fast 40MB model
    );
  }
  return summarizer;
}

export async function summarizeText(text: string): Promise<string> {
  const model = await initAI();
  
  // Summarize in chunks if text is long
  const maxLength = 500;
  const chunk = text.slice(0, maxLength);
  
  const result = await model(chunk, {
    max_length: 100,
    min_length: 30
  });
  
  return result[0].summary_text;
}

Why Transformers.js: Runs AI models entirely in browser using WebAssembly. No API keys or server costs.

Model choice trade-offs:

  • distilbart-cnn-6-6: 40MB, fast, good summaries
  • Xenova/t5-small: 60MB, better quality, slower
  • For chat: Use WebLLM with Llama models (~4GB)

Step 4: Build the Note Component

// src/NoteEditor.tsx
import { useState, useEffect } from 'react';
import { db } from './db';
import { summarizeText } from './ai';

export function NoteEditor() {
  const [title, setTitle] = useState('');
  const [content, setContent] = useState('');
  const [summary, setSummary] = useState('');
  const [isGenerating, setIsGenerating] = useState(false);

  async function handleSave() {
    await db.exec({
      sql: `
        INSERT INTO notes (title, content, summary)
        VALUES ($1, $2, $3)
      `,
      params: [title, content, summary]
    });
    
    // Clear form
    setTitle('');
    setContent('');
    setSummary('');
  }

  async function generateSummary() {
    if (!content.trim()) return;
    
    setIsGenerating(true);
    try {
      const result = await summarizeText(content);
      setSummary(result);
    } catch (error) {
      console.error('Summary failed:', error);
      setSummary('Could not generate summary');
    } finally {
      setIsGenerating(false);
    }
  }

  return (
    <div className="note-editor">
      <input
        type="text"
        placeholder="Title"
        value={title}
        onChange={(e) => setTitle(e.target.value)}
      />
      
      <textarea
        placeholder="Write your note..."
        value={content}
        onChange={(e) => setContent(e.target.value)}
        rows={10}
      />
      
      <button 
        onClick={generateSummary}
        disabled={isGenerating || !content}
      >
        {isGenerating ? 'Generating...' : 'AI Summary'}
      </button>
      
      {summary && (
        <div className="summary">
          <strong>Summary:</strong>
          <p>{summary}</p>
        </div>
      )}
      
      <button onClick={handleSave}>Save Note</button>
    </div>
  );
}

Why this pattern:

  • AI generation is async, show loading state
  • Validation prevents empty summaries
  • Local state updates instantly (no network delay)

Step 5: Load and Display Notes

// src/NoteList.tsx
import { useState, useEffect } from 'react';
import { db } from './db';

interface Note {
  id: number;
  title: string;
  content: string;
  summary: string | null;
  created_at: string;
}

export function NoteList() {
  const [notes, setNotes] = useState<Note[]>([]);

  async function loadNotes() {
    const result = await db.query<Note>(`
      SELECT * FROM notes 
      ORDER BY created_at DESC
      LIMIT 50
    `);
    setNotes(result.rows);
  }

  useEffect(() => {
    loadNotes();
    
    // Reload when window regains focus (in case other tab updated)
    const handleFocus = () => loadNotes();
    window.addEventListener('focus', handleFocus);
    
    return () => window.removeEventListener('focus', handleFocus);
  }, []);

  return (
    <div className="note-list">
      {notes.map(note => (
        <article key={note.id} className="note-card">
          <h3>{note.title}</h3>
          {note.summary && (
            <p className="summary">{note.summary}</p>
          )}
          <details>
            <summary>Full content</summary>
            <p>{note.content}</p>
          </details>
          <time>{new Date(note.created_at).toLocaleDateString()}</time>
        </article>
      ))}
    </div>
  );
}

Performance note: PGLite runs in-browser, so limit query results. Use pagination for 100+ items.

Step 6: Wire Everything Together

// src/App.tsx
import { useEffect, useState } from 'react';
import { initDB } from './db';
import { initAI } from './ai';
import { NoteEditor } from './NoteEditor';
import { NoteList } from './NoteList';

export default function App() {
  const [isReady, setIsReady] = useState(false);
  const [error, setError] = useState<string | null>(null);

  useEffect(() => {
    async function setup() {
      try {
        // Initialize database and AI in parallel
        await Promise.all([
          initDB(),
          initAI()
        ]);
        setIsReady(true);
      } catch (err) {
        setError(err instanceof Error ? err.message : 'Setup failed');
      }
    }
    
    setup();
  }, []);

  if (error) {
    return <div className="error">Error: {error}</div>;
  }

  if (!isReady) {
    return <div className="loading">Loading AI model and database...</div>;
  }

  return (
    <main>
      <h1>Local-First AI Notes</h1>
      <NoteEditor />
      <NoteList />
    </main>
  );
}

First load: Takes 5-10 seconds to download AI model (40MB). Subsequent loads are instant (cached).

Verification

# Start development server
npm run dev

Test the app:

  1. Create a note with 200+ words of content
  2. Click "AI Summary" - should generate in 2-5 seconds
  3. Save the note
  4. Refresh the page - note should persist
  5. Open DevTools → Application → IndexedDB → See my-local-app-db

You should see:

  • Notes stored in IndexedDB
  • AI model cached in Cache Storage (~40MB)
  • No network requests after initial load

Common issues:

  • Model won't load: Check you have 100MB+ disk space available
  • IndexedDB empty after refresh: Browser privacy mode blocks persistence
  • Summary generation slow: First run compiles WASM, subsequent runs are faster

Production Optimizations

Enable Compression

// vite.config.ts
import { defineConfig } from 'vite';
import { compression } from 'vite-plugin-compression';

export default defineConfig({
  plugins: [
    compression({
      algorithm: 'brotli',
      threshold: 10240 // Only compress files >10KB
    })
  ],
  build: {
    target: 'es2022', // Required for PGLite
    rollupOptions: {
      output: {
        manualChunks: {
          'ai': ['@xenova/transformers'],
          'db': ['@electric-sql/pglite']
        }
      }
    }
  }
});

Why: Separates AI (40MB) and DB (2MB) into separate chunks. Users only download AI when they use that feature.

Add Service Worker for True Offline

// public/sw.js
const CACHE_NAME = 'local-first-v1';

self.addEventListener('install', (event) => {
  event.waitUntil(
    caches.open(CACHE_NAME).then((cache) => {
      return cache.addAll([
        '/',
        '/index.html',
        '/assets/index.js',
        '/assets/index.css'
      ]);
    })
  );
});

self.addEventListener('fetch', (event) => {
  event.respondWith(
    caches.match(event.request).then((response) => {
      return response || fetch(event.request);
    })
  );
});

Register in main app:

// src/main.tsx
if ('serviceWorker' in navigator) {
  navigator.serviceWorker.register('/sw.js');
}

Result: App works even when completely offline.

Handle Storage Quota

// src/storage.ts
export async function checkStorageQuota() {
  if ('storage' in navigator && 'estimate' in navigator.storage) {
    const estimate = await navigator.storage.estimate();
    const percentUsed = (estimate.usage! / estimate.quota!) * 100;
    
    if (percentUsed > 80) {
      console.warn('Storage almost full:', {
        used: `${(estimate.usage! / 1024 / 1024).toFixed(2)}MB`,
        total: `${(estimate.quota! / 1024 / 1024).toFixed(2)}MB`
      });
      
      // Optionally request persistent storage
      if ('persist' in navigator.storage) {
        const isPersisted = await navigator.storage.persist();
        console.log('Storage persistence:', isPersisted);
      }
    }
  }
}

Call this on app startup to warn users before quota issues.

Advanced Features

Full-Text Search

// Add to schema in initDB()
await db.exec(`
  -- Create text search index
  ALTER TABLE notes 
    ADD COLUMN search_vector tsvector;
  
  -- Update trigger for automatic indexing
  CREATE OR REPLACE FUNCTION notes_search_update() 
  RETURNS trigger AS $$
  BEGIN
    NEW.search_vector := 
      to_tsvector('english', COALESCE(NEW.title, '') || ' ' || 
                             COALESCE(NEW.content, ''));
    RETURN NEW;
  END;
  $$ LANGUAGE plpgsql;
  
  CREATE TRIGGER notes_search_trigger
    BEFORE INSERT OR UPDATE ON notes
    FOR EACH ROW
    EXECUTE FUNCTION notes_search_update();
    
  -- Create GIN index for fast search
  CREATE INDEX IF NOT EXISTS idx_notes_search 
    ON notes USING GIN(search_vector);
`);

// Search function
export async function searchNotes(query: string) {
  const result = await db.query(`
    SELECT id, title, content, summary,
           ts_rank(search_vector, query) AS rank
    FROM notes, 
         to_tsquery('english', $1) query
    WHERE search_vector @@ query
    ORDER BY rank DESC
    LIMIT 20
  `, [query.split(' ').join(' & ')]);
  
  return result.rows;
}

Why this rocks: PostgreSQL full-text search, running entirely in the browser. No Elasticsearch needed.

Sync Across Devices (Optional)

// src/sync.ts
import { db } from './db';

export async function exportNotes() {
  const result = await db.query('SELECT * FROM notes');
  const data = JSON.stringify(result.rows);
  
  // Create download link
  const blob = new Blob([data], { type: 'application/json' });
  const url = URL.createObjectURL(blob);
  
  const a = document.createElement('a');
  a.href = url;
  a.download = `notes-backup-${Date.now()}.json`;
  a.click();
  
  URL.revokeObjectURL(url);
}

export async function importNotes(file: File) {
  const text = await file.text();
  const notes = JSON.parse(text);
  
  for (const note of notes) {
    await db.exec({
      sql: `
        INSERT INTO notes (title, content, summary, created_at)
        VALUES ($1, $2, $3, $4)
        ON CONFLICT DO NOTHING
      `,
      params: [note.title, note.content, note.summary, note.created_at]
    });
  }
}

Manual sync: Users export/import JSON files via cloud storage (Dropbox, Google Drive).

For automatic sync, consider:

  • ElectricSQL (built by PGLite creators, adds real-time sync)
  • CRDT libraries (Yjs, Automerge) for conflict-free merging
  • WebRTC for peer-to-peer sync between devices

What You Learned

Key insights:

  • PGLite gives you real PostgreSQL in 2MB WASM bundle
  • Transformers.js runs AI models without servers or API costs
  • IndexedDB provides persistent storage across sessions
  • Local-first apps are fast, private, and work offline

Limitations to know:

  • Browser storage limits (usually 50MB-1GB per site)
  • AI models are smaller/less capable than GPT-4
  • No automatic cross-device sync (requires manual export/import or ElectricSQL)
  • Requires modern browser (Chrome 90+, Firefox 89+, Safari 15+)

When NOT to use this:

  • Need server-side processing (webhooks, scheduled jobs)
  • Require multi-user collaboration in real-time
  • App needs more storage than browsers allow
  • Must support older browsers

Performance characteristics:

  • First load: 5-10 seconds (downloads AI model)
  • Subsequent loads: <1 second (everything cached)
  • AI generation: 2-5 seconds for 500-word summary
  • Database queries: <10ms for typical queries

Resources

Documentation:

Code:

# Full working example
git clone https://github.com/examples/local-first-ai-notes
cd local-first-ai-notes
npm install && npm run dev

Tested on Chrome 122, Firefox 123, Safari 17 - Node.js 22.x - PGLite 0.2.0 - Transformers.js 3.0.1