Christian Liebel
@christianliebel
Consultant
Making Angular Apps Smarter with
Generative AI
Local and Offline-capable (Hands-on)
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Hello, it’s me.
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Christian Liebel
X: @christianliebel
Bluesky:
@christianliebel.com
Email:
christian.liebel
@thinktecture.com
Angular, PWA
& Generative AI
Slides:
thinktecture.com
/christian-liebel
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09:00–10:30 Block 1
10:30–11:00 Coffee Break
11:00–12:30 Block 2
12:30–13:30 Lunch Break
13:30–15:00 Block 3
15:00–15:30 Coffee Break
15:30–16:30 Block 4
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Timetable
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What to expect
Focus on web app development
Focus on Generative AI
Up-to-date insights: the ML/AI
field is evolving fast
Live demos on real hardware
Hands-on labs
What not to expect
Deep dive into AI specifics, RAG,
model finetuning or training
Stable libraries or specifications
WebSD in Angular
Polished workshop
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Expectations
Huge downloads! High requirements! Things may break!
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(Workshop Edition)
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Demo Use Case
DEMO
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Setup complete?
(Node.js, Google Chrome, Editor, Git,
macOS/Windows,
20 GB free disk space, 6 GB VRAM)
Making Angular Apps Smarter with Generative AI
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Setup
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https://webgpureport.org/
Making Angular Apps Smarter with Generative AI
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Setup
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ng new genai-app
ng add @angular/material
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
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git clone https://github.com/thinktecture/ijs-muc-
2024-genai.git
cd ijs-muc-2024-genai
npm i
npm start -- --open
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Setup
LAB
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Generative AI everywhere
Source: https://www.apple.com/chde/apple-intelligence/
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Run locally on the user’s system
Making Angular Apps Smarter with Generative AI
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Single-Page Applications
Server-
Logik
Web API
Push Service
Web API
DBs
HTML, JS,
CSS, Assets
Webserver Webbrowser
SPA
Client-
Logik
View
HTML/CSS
View
HTML/CSS
View
HTML/CSS
HTTPS
WebSockets
HTTPS
HTTPS
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Make SPAs offline-capable
Making Angular Apps Smarter with Generative AI
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Progressive Web Apps
Service
Worker
Internet
Website
HTML/JS
Cache
fetch
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Overview
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Generative AI
Text
OpenAI GPT
Mistral
…
Speech
OpenAI Whisper
tortoise-tts
…
Images
DALL·E
Stable Diffusion
…
Audio/Music
Musico
Soundraw
…
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Overview
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Generative AI
Text
OpenAI GPT
Mistral
…
Speech
OpenAI Whisper
tortoise-tts
…
Images
DALL·E
Stable Diffusion
…
Audio/Music
Musico
Soundraw
…
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Examples
Making Angular Apps Smarter with Generative AI
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Generative AI Cloud Providers
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Drawbacks
Making Angular Apps Smarter with Generative AI
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Generative AI Cloud Providers
Require a (stable) internet connection
Subject to network latency and server availability
Data is transferred to the cloud service
Require a subscription
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Can we run GenAI models locally?
Making Angular Apps Smarter with Generative AI
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Large: Trained on lots of data
Language: Process and generate text
Models: Programs/neural networks
Examples:
– GPT (ChatGPT, Microsoft Copilot, …)
– Gemini, Gemma (Google)
– LLaMa (Meta AI)
Making Angular Apps Smarter with Generative AI
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Large Language Models
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Token
A meaningful unit of text (e.g., a word, a part of a word, a character).
Context Window
The maximum amount of tokens the model can process.
Parameters/weights
Internal variables learned during training, used to make predictions.
Making Angular Apps Smarter with Generative AI
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Large Language Models
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Prompts serve as the universal interface
Unstructured text conveying specific semantics
Paradigm shift in software architecture
Natural language becomes a first-class citizen
Caveats
Non-determinism and hallucination, prompt injections
Making Angular Apps Smarter with Generative AI
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Large Language Models
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Size Comparison
Model:Parameters Size
phi3:3b 2.2 GB
mistral:7b 4.1 GB
llama3:8b 4.7 GB
gemma2:9b 5.4 GB
gemma2:27b 16 GB
llama3:70b 40 GB
Making Angular Apps Smarter with Generative AI
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Large Language Models
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https://webllm.mlc.ai/
Making Angular Apps Smarter with Generative AI
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WebLLM
DEMO
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On NPM
Making Angular Apps Smarter with Generative AI
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WebLLM
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npm i @mlc-ai/web-llm
Making Angular Apps Smarter with Generative AI
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LAB
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(1/3)
In app.component.ts, add the following lines:
protected readonly progress = signal(0);
protected readonly ready = signal(false);
protected engine?: MLCEngine;
Making Angular Apps Smarter with Generative AI
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Downloading a model LAB
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(2/3)
In app.component.ts (ngOnInit()), add the following lines:
const model = 'Llama-3.2-3B-Instruct-q4f16_1-MLC';
this.engine = await CreateMLCEngine(model, {
initProgressCallback: ({ progress }) =>
this.progress.set(progress)
});
this.ready.set(true);
Making Angular Apps Smarter with Generative AI
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Downloading a model LAB
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(3/3)
In app.component.html, add the following lines:
Ask
Launch the app via npm start. The progress bar should begin to move.
Making Angular Apps Smarter with Generative AI
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Downloading a model LAB
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Storing model files locally
Making Angular Apps Smarter with Generative AI
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Cache API
Internet
Website
HTML/JS
Cache with
model files
Hugging
Face
Note: Due to the Same-Origin Policy, models cannot be shared across origins.
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Parameter cache
Making Angular Apps Smarter with Generative AI
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Cache API
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
WebAssembly (Wasm)
– Bytecode for the web
– Compile target for arbitrary
languages
– Can be faster than JavaScript
– WebLLM uses a model-
specific Wasm library to
accelerate model
computations
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
WebGPU
– Grants low-level access to the
Graphics Processing Unit (GPU)
– Near native performance for
machine learning applications
– Supported by Chromium-based
browsers on Windows and
macOS from version 113
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– Grants web apps access to
the device’s CPU, GPU and
Neural Processing Unit (NPU)
– In specification by the WebML
Working Group at W3C
– Implementation in progress in
Chromium (behind a flag)
– Even better performance
compared to WebGPU
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
WebNN
Source: https://webmachinelearning.github.io/webnn-intro/
DEMO
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
WebNN: near-native inference performance
Source: Intel. Browser: Chrome Canary 118.0.5943.0, DUT: Dell/Linux/i7-1260P, single p-core, Workloads: MediaPipe solution models (FP32, batch=1)
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(1/3)
In app.component.ts, add the following lines at the top of the class:
protected readonly reply = signal('');
Making Angular Apps Smarter with Generative AI
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Model inference LAB
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(2/3)
In the runPrompt() method, add the following code:
await this.engine!.resetChat();
this.reply.set('…');
const messages: ChatCompletionMessageParam[] = [
{ role: "user", content: value }
];
const reply = await this.engine!.chat.completions.create({ messages });
this.reply.set(reply.choices[0].message.content ?? '');
Making Angular Apps Smarter with Generative AI
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Model inference LAB
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(3/3)
In app.component.html, add the following line:
{{ reply() }}
You should now be able to send prompts to the model and see the
responses in the template.
Making Angular Apps Smarter with Generative AI
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Model inference LAB
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npm run build
Making Angular Apps Smarter with Generative AI
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LAB
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1. In angular.json, increase the bundle size for the Angular project
(property architect.build.configurations.production.budgets[0]
.maximumError) to at least 5MB.
2. Then, run npm run build again. This time, the build should succeed.
3. If you stopped the development server, don’t forget to bring it back up
again (npm start).
Making Angular Apps Smarter with Generative AI
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Build issues LAB
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(1/2)
In app.component.ts, add the following signal at the top:
protected readonly todos = signal([]);
Add the following method:
addTodo(text: string) {
this.todos.update(todos => [...todos, { done: false, text }]);
}
Making Angular Apps Smarter with Generative AI
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Todo management LAB
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(2/2)
In app.component.html, add the following lines to add todos from the UI:
Add
@for(todo of todos(); track $index) {
{{ todo.text }}
}
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Todo management LAB
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In app.component.ts, add the following function
toggleTodo(index: number) {
this.todos.update(todos => todos.map((todo, todoIndex) =>
todoIndex === index ? { ...todo, done: !todo.done } : todo));
}
In app.component.html, add the following content to the
node:
You should now be able to toggle the checkboxes.
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Todo management (extended) LAB
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Concept and limitations
The todo data has to be
converted into natural language.
For the sake of simplicity, we will
add all TODOs to the prompt.
Remember: LLMs have a context
window (Mistral-7B: 8K).
If you need to chat with larger
sets of text, refer to Retrieval
Augmented Generation (RAG).
These are the todos:
* Wash clothes
* Pet the dog
* Take out the trash
Making Angular Apps Smarter with Generative AI
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Chat with data
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System prompt
Metaprompt that defines…
– character
– capabilities/limitations
– output format
– behavior
– grounding data
Hallucinations and prompt injections cannot be eliminated.
You are a helpful assistant.
Answer user questions on todos.
Generate a valid JSON object.
Avoid negative content.
These are the user’s todos: …
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Chat with data
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Flow
System
message
• The user has
these todos:
1. … 2. … 3. …
User
message
• How many
todos do I
have?
Assistant
message
• You have
three todos.
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Chat with data
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Using a system & user prompt
Adjust the implementation in runPrompt() to include the system prompt:
const systemPrompt = `Here's the user's todo list:
${this.todos().map(todo => `* ${todo.text} (${todo.done ?
'done' : 'not done'})`).join('\n')}`;
const messages: ChatCompletionMessageParam[] = [
{ role: "system", content: systemPrompt },
{ role: "user", content: value }
];
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Chat with data LAB
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Techniques
– Providing examples (single shot, few shot, …)
– Priming outputs
– Specify output structure
– Repeating instructions
– Chain of thought
– …
Success also depends on the model.
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Engineering
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering
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const systemPrompt = `You are a helpful assistant.
The user will ask questions about their todo list.
Briefly answer the questions.
Don't try to make up an answer if you don't know it.
Here's the user's todo list:
${this.todos().map(todo => `* ${todo.text} (this todo is
${todo.done ? 'done' : 'not done'})`).join('\n')}
${this.todos().length === 0 ? 'The list is empty, there are
no todos.' : ''}`;
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Engineering LAB
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Alternatives
Prompt Engineering
Retrieval Augmented
Generation
Fine-tuning
Custom model
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Engineering
Effort
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Add the following line to the runPrompt() method:
console.log(reply.usage);
Ask a new question and check your console for performance statistics.
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Performance LAB
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Workshop Participants
Device Tokens/s (Decode)
MacBook i7 (2021) 2.70
MacBook M1 (1) 15.35
MacBook M1 (2) 14.52
MacBook M4 19.70
DELL i7 + Iris (1) 1.43
DELL i7 + Iris (2) 3.15
Making Angular Apps Smarter with Generative AI
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Performance
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Comparison
45 33
1200
0
200
400
600
800
1000
1200
1400
WebLLM (Llama3-8b, M4) Azure OpenAI (gpt-4o-mini) Groq (Llama3-8b)
Tokens/sec
Making Angular Apps Smarter with Generative AI
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Performance
WebLLM/Groq: Own tests (14.11.2024), OpenAI/Azure OpenAI: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/provisioned-throughput (18.07.2024)
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
DEMO
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Just transfer the 17.34 euros to
me, my IBAN is
DE02200505501015871393. I am
with Hamburger Sparkasse
(HASPDEHH).
Data Extraction
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Use Case
Nice, here is my address:
Peter Müller, Rheinstr. 7, 04435
Schkeuditz
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Just transfer the 17.34 euros to
me, my IBAN is
DE02200505501015871393. I am
with Hamburger Sparkasse
(HASPDEHH).
Data Extraction
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Use Case
Nice, here is my address:
Peter Müller, Rheinstr. 7, 04435
Schkeuditz
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protected readonly formGroup
= this.fb.group({
firstName: [''],
lastName: [''],
addressLine1: [''],
addressLine2: [''],
city: [''],
state: [''],
zip: [''],
country: [''],
});
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Idea
Nice, here is my address:
Peter Müller, Rheinstr. 7, 04435
Schkeuditz
Smart
Form
Filler
(LLM)
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Form Field
Prompt
Generator
Model
Backend
Response
Parser
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Architecture
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Form Field
“Try to determine the
country based on the
input.”
“If present, put the
department in this
field.”
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(1/2)
Add the following code to app.component.ts:
private fb = inject(NonNullableFormBuilder);
protected formGroup = this.fb.group({
name: '',
city: '',
});
async fillForm(value: string) {}
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Form Field LAB
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(2/2)
Add the following code to app.component.html:
Fill form
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Form Field LAB
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Async Clipboard API
Allows reading from/writing to
the clipboard in an
asynchronous manner
Reading from the clipboard
requires user consent first
(privacy!)
Supported by Chrome, Edge
and Safari and Firefox
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Generator
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(1/2)
Add the following code to app.component.ts:
async paste() {
const content = await navigator.clipboard.readText();
await this.fillForm(content);
}
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Async Clipboard API LAB
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(2/2)
Add the following code to app.component.html
(after the “Fill form” button):
Paste
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Async Clipboard API LAB
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System
message
• The form has the
following setup:
{ "name": "",
"city": "" }
User
message
• I am Peter from Berlin
Assistant
message
• { "name": "Peter",
"city": "Berlin" }
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Generator
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Add the following code to app.component.ts (fillForm() method):
const messages: ChatCompletionMessageParam[] = [{
role: "system",
content: `Extract the information to a JSON object of this shape:
${JSON.stringify(this.formGroup.value)} Do not add any other text.`
}, {
role: "user", content: value
}];
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Generator LAB
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Model Backend
– Cloud
– WebLLM
– Prompt API
– Ollama (on-premise)
– …
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Add the following code to app.component.ts (fillForm() method):
const reply =
await this.engine!.chat.completions.create({ messages });
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Model Generator LAB
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Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Parser
Assistant
message
• { "name": "Peter",
"city": "Berlin" }
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Add the following code to app.component.ts (fillForm() method):
this.formGroup.setValue(JSON.parse(reply.choices[0]
.message.content ?? ''));
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Prompt Parser LAB
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Assistant message
Parsing the assistant message
as text/JSON/…
Tool calling
Specifying a well-defined
interface via a JSON schema
called by the LLM (safer, growing
support)
Making Angular Apps Smarter with Generative AI
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Prompt Parser
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https://www.google.com/chrome/canary/
about://flags
Enables optimization guide on device à
EnabledBypassPerfRequirement
Prompt API for Gemini Nano à Enabled
await ai.languageModel.create();
about://components
Making Angular Apps Smarter with Generative AI
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Prompt API
LAB
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Making Angular Apps Smarter with Generative AI
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Prompt API
Operating
System
Website
HTML/JS
Browser Internet
Apple Intelligence
Gemini Nano
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Part of Chrome’s Built-In AI initiative
– Exploratory API for local
experiments and use case
determination
– Downloads Gemini Nano into
Google Chrome
– Model can be shared across
origins
– Uses native APIs directly
– Fine-tuning API might follow in
the future
Making Angular Apps Smarter with Generative AI
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Prompt API
https://developer.chrome.com/docs/ai/built-in
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npm i -D @types/dom-chromium-ai
add @types/dom-chromium-ai to the types in
tsconfig.app.json
Making Angular Apps Smarter with Generative AI
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Prompt API
LAB
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Adjust the implementations of runPrompt()/fillForm():
const session =
await window.ai.languageModel.create({ systemPrompt });
const reply = await session.prompt(value);
// runPrompt(): this.reply.set(reply);
// fillForm(): this.formGroup.setValue(JSON.parse(reply));
Making Angular Apps Smarter with Generative AI
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Prompt API LAB
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Alternatives: Ollama
– Local runner for AI models
– Offers a local server a website
can connect to à allows
sharing models across origins
– Supported on macOS and
Linux (Windows in Preview)
https://webml-demo.vercel.app/
https://ollama.ai/
Making Angular Apps Smarter with Generative AI
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Local AI Models
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Alternatives: Hugging Face Transformers
Pre-trained, specialized, significantly smaller models beyond GenAI
Examples:
– Text generation
– Image classification
– Translation
– Speech recognition
– Image-to-text
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Local AI Models
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Alternatives: Transformers.js
– Pre-trained, specialized,
significantly smaller models
beyond GenAI
– JavaScript library to run
Hugging Face transformers
in the browser
– Supports most of the models
https://huggingface.co/docs/transformers.js
Making Angular Apps Smarter with Generative AI
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Local AI Models
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Pros & Cons
+ Data does not leave the
browser (privacy)
+ High availability
(offline support)
+ Low latency
+ Stability
(no external API changes)
+ Low cost
– Lower quality
– High system (RAM, GPU) and
bandwidth requirements
– Large model size, models
cannot always be shared
– Model initialization and
inference are relatively slow
– APIs are experimental
Making Angular Apps Smarter with Generative AI
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Local AI Models
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– Cloud-based models remain the most powerful models
– Due to their size and high system requirements, local generative AI
models are currently rather interesting for very special scenarios (e.g.,
high privacy demands, offline availability)
– Small, specialized models are an interesting alternative (if available)
– Large language models are becoming more compact and efficient
– Vendors start shipping AI models with their devices
– Devices are becoming more powerful for running AI tasks
– Experiment with the AI APIs and make your Angular App smarter!
Making Angular Apps Smarter with Generative AI
Local and Offline-capable (Hands-on)
Summary
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Thank you
for your kind attention!
Christian Liebel
@christianliebel
christian.liebel@thinktecture.com