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Christian Liebel
@christianliebel
Consultant
Making Angular Apps Smarter
with Generative AI
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Hello, it’s me.
Making Angular Apps Smarter with Generative AI
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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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Original
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
15:30–17:00 Block 4
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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
17 hands-on labs
What not to expect
Deep dive into AI specifics, RAG,
model finetuning or training
Stable libraries or specifications
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Expectations
Huge downloads! High requirements! Things may break!
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Making Angular Apps Smarter with Generative AI
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Workshop Slides
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Making Angular Apps Smarter with Generative AI
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DEMO
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(Workshop Edition)
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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)
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Setup
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webgpureport.org
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WebGPU
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git clone https://github.com/thinktecture/ijs-
munich-2025-genai.git
cd ijs-munich-2025-genai
npm i
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Setup
LAB #0
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Making Angular Apps Smarter with Generative AI
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Generative AI everywhere
Source: https://www.apple.com/chde/apple-intelligence/
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Run locally on the user’s system
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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
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Progressive Web Apps
Service
Worker
Internet
Website
HTML/JS
Cache
fetch
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Overview
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Generative AI
Text
OpenAI GPT
Mistral
…
Audio/Music
Musico
Soundraw
…
Images
DALL·E
Firefly
…
Video
Sora
Runway
…
Speech
Whisper
tortoise-tts
…
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Overview
Making Angular Apps Smarter with Generative AI
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Generative AI
Text
OpenAI GPT
Mistral
…
Audio/Music
Musico
Soundraw
…
Images
DALL·E
Firefly
…
Video
Sora
Runway
…
Speech
Whisper
tortoise-tts
…
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Examples
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Generative AI Cloud Providers
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Drawbacks
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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?
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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)
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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.
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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
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Large Language Models
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Size Comparison
Model:Parameters Size
phi3:3.8b 2.2 GB
mistral:7b 4.1 GB
deepseek-r1:8b 5.2 GB
gemma3n:e4b 7.5 GB
gemma3:12b 8.1 GB
llama4:16x17b 67 GB
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Large Language Models
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https://webllm.mlc.ai/
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WebLLM
DEMO
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On NPM
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WebLLM
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npm i @mlc-ai/web-llm
npm start -- -o
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LAB #1
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(1/3)
In src/app/todo/todo.ts, add the following lines at the top of the class:
protected readonly progress = signal(0);
protected readonly ready = signal(false);
protected engine?: MLCEngine;
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Downloading a model LAB #2
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(2/3)
In todo.ts (ngOnInit()), add the following lines:
this.engine = await CreateMLCEngine(MODEL, {
initProgressCallback: ({ progress }) =>
this.progress.set(progress)
});
this.ready.set(true);
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Downloading a model LAB #2
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(3/3)
In todo.html, change the following lines:
@if(!ready()) {
}
…
The progress bar should begin to move.
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Downloading a model LAB #2
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Storing model files locally
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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
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Cache API
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Making Angular Apps Smarter with Generative AI
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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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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,
Safari 26, and Firefox 141 on
Windows
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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
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WebNN
Source: https://webmachinelearning.github.io/webnn-intro/
DEMO
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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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(1/4)
In todo.ts, add the following line at the top of the class:
protected readonly reply = signal('');
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Model inference LAB #3
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(2/4)
In the runPrompt() method, add the following code:
this.reply.set('…');
const chunks = languageModel === 'webllm'
? this.inferWebLLM(userPrompt)
: this.inferPromptApi(userPrompt);
let reply = '';
for await (const chunk of chunks) {
reply += chunk;
this.reply.set(reply);
}
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Model inference LAB #3
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(3/4)
In the inferWebLLM() method, add the following code:
await this.engine!.resetChat();
const messages: ChatCompletionMessageParam[] = [{role: "user", content: userPrompt}];
const chunks = await this.engine!.chat.completions.create({messages, stream: true});
for await (const chunk of chunks) {
yield chunk.choices[0]?.delta.content ?? '';
}
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Model inference LAB #3
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(4/4)
In todo.html, change the following line:
{{ reply() }}
You should now be able to send prompts to the model and see the
responses in the template.
⚠ Note: Browsers support better options for streaming LLM responses:
https://developer.chrome.com/docs/ai/render-llm-responses
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Model inference LAB #3
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Stop the development server (Ctrl+C) and run
npm run build
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LAB #4
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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 10MB.
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).
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Build issues LAB #4
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(1/2)
In todo.ts, add the following signal at the top:
protected readonly todos = signal([]);
Add the following line to the addTodo() method:
const text = prompt() ?? '';
this.todos.update(todos => [...todos,
{ done: false, text }]);
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Todo management LAB #5
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(2/2)
In todo.html, add the following lines to add todos from the UI:
@for (todo of todos(); track $index) {
{{ todo.text }}
}
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Todo management LAB #5
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@for (todo of todos(); track $index) {
{{ todo.text }}
}
⚠ Boo! This pattern is not recommended. Instead, you should set the
changed values on the signal. But this messes up with Angular Material…
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Todo management (extended) LAB #6
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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
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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: …
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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.
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Chat with data
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Using a system & user prompt
Adjust the code in inferWebLLM() to include the system prompt:
const systemPrompt = `Here's the user's todo list:
${JSON.stringify(this.todos())}`;
const messages: ChatCompletionMessageParam[] = [
{ role: "system", content: systemPrompt },
{ role: "user", content: userPrompt }
];
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Chat with data LAB #7
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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.
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Prompt Engineering
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/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: ${JSON.stringify(this.todos())}`;
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Prompt Engineering LAB #8
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Alternatives
Prompt Engineering
Retrieval Augmented
Generation
Fine-tuning
Custom model
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Prompt Engineering
Effort
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Adjust todo.ts as follows:
const chunks = await this.engine!.chat.completions.create({ messages,
stream: true, stream_options: { include_usage: true } });
for await (const chunk of chunks) {
console.log(chunk.usage);
yield chunk.choices[0]?.delta.content ?? '';
}
Ask a new question and check your console for performance statistics.
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Performance LAB #9
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Workshop Participants
Device Tokens/s (Decode)
Apple MacBook M4 Max 33.96
DELL AMD Ryzen AI 11.09
DELL 1.7
Lenovo Core i5 0.83
DELL i7 (integrated graphics) 3.66
Apple MacBook M4 Pro 33.63
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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
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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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In current version of Chrome or Edge:
about://flags
Enables optimization guide on device à
EnabledBypassPerfRequirement
Prompt API for Gemini Nano à Enabled
await LanguageModel.create();
about://components
about://on-device-internals
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Prompt API
LAB #10
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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
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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 "dom-chromium-ai" to the
types in tsconfig.app.json
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Prompt API
LAB #11
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Add the following lines to inferPromptApi():
const systemPrompt = `
The user will ask questions about their todo list.
Here's the user's todo list: ${JSON.stringify(this.todos())}`;
const languageModel = await LanguageModel.create({
initialPrompts: [{ role: "system", content: systemPrompt }]
});
const chunks = languageModel.promptStreaming(userPrompt);
for await (const chunk of chunks) {
yield chunk;
}
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Local AI Models LAB #12
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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 Windows, Linux,
macOS
https://ollama.ai/
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Local AI Models
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https://webml-demo.vercel.app/
https://github.com/jacoblee93/fully-local-pdf-chatbot/
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Local RAG Demo
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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
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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
https://huggingface.co/docs/transformers.js
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Local AI Models
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Making Angular Apps Smarter with Generative AI
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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
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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
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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: [''],
});
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Idea
Nice, here is my address:
Peter Müller, Rheinstr. 7, 04435
Schkeuditz
Smart
Form
Filler
(LLM)
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Form Field
“Insurance numbers
always start with
INS.”
“Try to determine the
country based on the
input.”
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(1/2)
Add the following code to form.ts:
private fb = inject(NonNullableFormBuilder);
protected formGroup = this.fb.group({
name: '',
city: '',
});
async fillForm(value: string) {}
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Form Field LAB #13
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(2/2)
Add the following code to form.html:
Fill form
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Form Field LAB #13
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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
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Prompt Generator
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(1/2)
Add the following code to form.ts:
async paste() {
const content = await navigator.clipboard.readText();
await this.fillForm(content);
}
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Async Clipboard API LAB #14
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(2/2)
Add the following code to form.html
(after the “Fill form” button):
Paste
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Async Clipboard API LAB #14
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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" }
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Prompt Generator
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Add the following code to the fillForm() method:
const languageModel = await LanguageModel.create({
initialPrompts: [{
role: 'system',
content: `Extract the information to a JSON object of this
shape: ${JSON.stringify(this.formGroup.value)}`,
}],
});
const result = await languageModel.prompt(value);
console.log(result);
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Prompt Generator LAB #15
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Add the following code to form.ts (fillForm() method):
const result = await languageModel.prompt(value, {
responseConstraint: {
type: 'object',
properties: {
name: { type: 'string' },
city: { type: 'string' }
}
}
});
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Prompt Generator (Structured Output) LAB #16
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Prompt Parser
Assistant
message
• { "name": "Peter",
"city": "Berlin" }
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Add the following code to form.ts (fillForm() method):
this.formGroup.setValue(JSON.parse(result));
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Prompt Parser LAB #17
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Assistant message
Parsing the assistant message
as text/JSON/…
JSON Mode
Tool calling
Specifying a well-defined
interface via a JSON schema
called by the LLM (safer, growing
support)
Structured Output
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Prompt Parser
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Cross-Origin Storage
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Cross-Origin Storage
https://chromewebstore.google.com/detail/cross-origin-
storage/denpnpcgjgikjpoglpjefakmdcbmlgih
https://github.com/web-ai-community/cross-origin-storage-extension
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Bonus Content
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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
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Summary
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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 are shipping AI models with their devices
– Devices are becoming more powerful for running AI workloads
– Experiment with the AI APIs and make your Angular App smarter!
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Summary
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Thank you
for your kind attention!
Christian Liebel
@christianliebel
[email protected]