[Most Important LLM System Design #6] Brain of LLMs - Transformers, Vector Embeddings and Vector Databases: How They Actually Work - Part 6
All the technical details you need to know...
If you've ever wondered how ChatGPT maintains context across conversations, how Google Search understands the intent behind your queries, or how recommendation systems know exactly what you want before you do—the answer lies in a revolutionary technology that's reshaping the AI landscape: Vector Databases.
The explosion of Large Language Models has created an unprecedented challenge: how do we give these incredibly powerful AI systems access to real-time, domain-specific knowledge without retraining them from scratch? How do we overcome their context limitations while maintaining lightning-fast response times?
The answer isn't just impressive—it's game-changing. Vector databases represent the missing link between static AI models and dynamic, contextual intelligence. They're the secret sauce that transforms a good language model into an exceptional one, enabling everything from unlimited conversational memory to real-time knowledge retrieval.
Read previous parts -
Understanding Transformers & Large Language Models: How They Actually Work - Part 1
Understanding Transformers & Large Language Models: How They Actually Work - Part 2
[LLM System Design #3] Large Language Models: Pre-Training LLMs: How They Actually Work - Part 3
Here's what most developers don't realize:
Vector databases aren't just a storage solution—they're the foundation of next-generation AI applications that can understand, remember, and reason about information at unprecedented scale.
Today, I’ll diving deep into the world of vector databases and their synergy with Large Language Models.
Part 1: Vector Embeddings - The Language of AI Understanding
What Are Vector Embeddings?
Vector embeddings are the fundamental building blocks that enable machines to understand and process human language, images, audio, and other unstructured data. Think of them as a universal translation system that converts the complexity of human communication into mathematical representations that computers can process, compare, and reason about.
At its core, a vector embedding is a dense, high-dimensional numerical representation of data—typically ranging from 128 to thousands of dimensions—where each dimension captures a specific aspect of meaning or relationship. Unlike traditional keyword-based approaches that treat words as isolated symbols, embeddings understand that "king" and "monarch" are semantically related, that "Paris" and "France" have a geographical relationship, and that "happy" and "joyful" express similar emotions.
The magic happens through sophisticated neural networks trained on massive datasets that learn to encode semantic meaning, contextual relationships, and abstract concepts into these mathematical vectors. When similar concepts are embedded, their vectors cluster together in the high-dimensional space, creating a rich landscape of meaning that AI systems can navigate.
Diagram 1: Vector Embedding Creation Pipeline
EMBEDDING GENERATION PROCESS
INPUT DATA SOURCES:
┌─────────────────────────────────────────────────────────┐
│ Text: "The AI revolution is transforming business" │
│ Image: [Photo of a robot in an office] │
│ Audio: [Recording of a business presentation] │
└─────────────────────────────────────────────────────────┘
↓
PREPROCESSING STAGE:
┌─────────────────────────────────────────────────────────┐
│ Text: Tokenization, normalization, chunking │
│ Image: Resizing, normalization, augmentation │
│ Audio: Spectogram generation, noise reduction │
└─────────────────────────────────────────────────────────┘
↓
EMBEDDING MODEL SELECTION:
┌─────────────────────────────────────────────────────────┐
│ Text Models: │
│ • Sentence-BERT: General purpose, 384-768 dims │
│ • OpenAI text-embedding-ada-002: 1536 dims │
│ • Cohere embed-english-v3.0: 1024 dims │
│ │
│ Multimodal Models: │
│ • CLIP: Joint text-image understanding │
│ • DALL-E: Image generation embeddings │
│ • Whisper: Audio-to-text embeddings │
└─────────────────────────────────────────────────────────┘
↓
VECTOR GENERATION:
┌─────────────────────────────────────────────────────────┐
│ Input → Neural Network → Dense Vector │
│ │
│ "AI revolution" → [0.234, -0.567, 0.891, ..., 0.123] │
│ ↑ │
│ 768 dimensions │
│ │
│ Properties: │
│ • Dense: Most values are non-zero │
│ • High-dimensional: 100s to 1000s of features │
│ • Normalized: Typically unit length │
│ • Semantic: Similar meaning = similar vectors │
└─────────────────────────────────────────────────────────┘
↓
OUTPUT VECTOR EMBEDDINGS:
┌─────────────────────────────────────────────────────────┐
│ Ready for storage, search, and similarity comparison │
│ │
│ Applications: │
│ • Semantic search and retrieval │
│ • Content recommendation │
│ • Clustering and classification │
│ • Anomaly detection │
└─────────────────────────────────────────────────────────┘
Diagram 2: Semantic Vector Space Visualization
HIGH-DIMENSIONAL SEMANTIC SPACE REPRESENTATION
CONCEPT CLUSTERING IN VECTOR SPACE:
┌─────────────────────────────────────────────────────────┐
│ Vector Space Map │
│ │
│ Technology Cluster Business Cluster │
│ ↙ ↘ ↙ ↘ │
│ AI ML Revenue Profit │
│ ↓ ↙ ↓ ↙ │
│ Robot Enterprise │
│ │
│ Animal Cluster Emotion Cluster │
│ ↙ ↘ ↙ ↘ │
│ Dog Cat Happy Joy │
│ ↓ ↙ ↓ ↙ │
│ Pet Excited │
│ │
│ Distance = Semantic Similarity │
│ Closer vectors = More related concepts │
└─────────────────────────────────────────────────────────┘
MATHEMATICAL RELATIONSHIPS:
┌─────────────────────────────────────────────────────────┐
│ Analogical Reasoning in Vector Space: │
│ │
│ King - Man + Woman ≈ Queen │
│ [0.2,0.8,0.1] - [0.1,0.3,0.0] + [0.0,0.3,0.9] │
│ = [0.1,0.8,1.0] ≈ Queen vector │
│ │
│ Paris - France + Italy ≈ Rome │
│ Technology + Education ≈ EdTech │
│ Happy + Intensity ≈ Ecstatic │
│ │
│ These relationships emerge automatically from training! │
└─────────────────────────────────────────────────────────┘
SIMILARITY CALCULATION METHODS:
┌─────────────────────────────────────────────────────────┐
│ Vector A: [0.2, 0.8, 0.1, 0.5] │
│ Vector B: [0.3, 0.7, 0.2, 0.4] │
│ │
│ Cosine Similarity: │
│ cos(θ) = (A·B) / (||A|| × ||B||) │
│ = 0.91 (very similar) │
│ │
│ Euclidean Distance: │
│ d = √[(0.2-0.3)² + (0.8-0.7)² + (0.1-0.2)² + (0.5-0.4)²] │
│ = 0.24 (small distance = high similarity) │
│ │
│ Dot Product: │
│ A·B = (0.2×0.3) + (0.8×0.7) + (0.1×0.2) + (0.5×0.4) │
│ = 0.88 (higher = more similar) │
└─────────────────────────────────────────────────────────┘
Diagram 3: Multi-Modal Embedding Architecture
UNIFIED MULTI-MODAL EMBEDDING SYSTEM
INPUT MODALITIES:
┌─────────────────────────────────────────────────────────┐
│ Text Input: "A red sports car driving fast" │
│ Image Input: [Photo of red Ferrari on highway] │
│ Audio Input: [Sound of engine revving] │
└─────────────────────────────────────────────────────────┘
↓
MODALITY-SPECIFIC ENCODERS:
┌─────────────────────────────────────────────────────────┐
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────┐ │
│ │ Text Encoder │ │ Vision Encoder │ │Audio Encoder│ │
│ │ │ │ │ │ │ │
│ │ Transformer │ │ ResNet/ViT │ │ Wav2Vec │ │
│ │ BERT/RoBERTa │ │ Convolutional │ │ Spectrogram │ │
│ │ 768 dimensions │ │ 2048 dimensions │ │ 512 dims │ │
│ └─────────────────┘ └─────────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────┘
↓
CROSS-MODAL ALIGNMENT:
┌─────────────────────────────────────────────────────────┐
│ Shared Embedding Space │
│ │
│ Text: "red car" → [0.2, 0.8, -0.3, ..., 0.5] │
│ Image: [red car] → [0.3, 0.7, -0.2, ..., 0.6] │
│ Audio: [engine] → [0.1, 0.4, -0.1, ..., 0.3] │
│ │
│ Contrastive Learning: │
│ • Matching pairs get similar embeddings │
│ • Non-matching pairs get dissimilar embeddings │
│ • Cross-modal retrieval becomes possible │
└─────────────────────────────────────────────────────────┘
↓
UNIFIED APPLICATIONS:
┌─────────────────────────────────────────────────────────┐
│ Cross-Modal Search: │
│ Query: "red sports car" (text) │
│ Results: [Images of red cars, car sounds, reviews] │
│ │
│ Content Generation: │
│ Input: [Image of car] │
│ Output: "High-performance vehicle description" │
│ │
│ Similarity Matching: │
│ Find images that match audio descriptions │
│ Discover text that relates to video content │
└─────────────────────────────────────────────────────────┘
EMBEDDING QUALITY METRICS:
┌─────────────────────────────────────────────────────────┐
│ Evaluation Criteria: │
│ │
│ Semantic Consistency: 94.7% │
│ Cross-modal Alignment: 87.3% │
│ Retrieval Accuracy: 91.2% │
│ Dimensionality Efficiency: 8.5x compression │
│ │
│ Real-world Performance: │
│ • Search relevance improvement: +35% │
│ • Recommendation click-through: +28% │
│ • Content discovery engagement: +42% │
└─────────────────────────────────────────────────────────┘
Why Vector Embeddings Are Revolutionary
Semantic Understanding: Unlike traditional keyword matching that treats "happy" and "joyful" as completely different terms, embeddings understand their semantic relationship, enabling more intelligent and contextual search and retrieval.
Below are the top 10 System Design Case studies for this week
Billions of Queries Daily : How Google Search Actually Works
100+ Million Requests per Second : How Amazon Shopping Cart Actually Works
Serving 132+ Million Users : Scaling for Global Transit Real Time Ride Sharing Market at Uber
3 Billion Daily Users : How Youtube Actually Scales
$100000 per BTC : How Bitcoin Actually Works
$320 Billion Crypto Transactions Volume: How Coinbase Actually Works
100K Events per Second : How Uber Real-Time Surge Pricing Actually Works
Processing 2 Billion Daily Queries : How Facebook Graph Search Actually Works
7 Trillion Messages Daily : Magic Behind LinkedIn Architecture and How It Actually Works
1 Billion Tweets Daily : Magic Behind Twitter Scaling and How It Actually Works
12 Million Daily Users: Inside Slack's Real-Time Messaging Magic and How it Actually Works
3 Billion Daily Users : How Youtube Actually Scales
1.5 Billion Swipes per Day : How Tinder Matching Actually Works
500+ Million Users Daily : How Instagram Stories Actually Work
2.9 Billion Daily Active Users : How Facebook News Feed Algorithm Actually Works
20 Billion Messages Daily: How Facebook Messenger Actually Works
8+ Billion Daily Views: How Facebook's Live Video Ranking Algorithm Works
How Discord's Real-Time Chat Scales to 200+ Million Users
80 Million Photos Daily : How Instagram Achieves Real Time Photo Sharing
Serving 1 Trillion Edges in Social Graph with 1ms Read Times : How Facebook TAO works
How Lyft Handles 2x Traffic Spikes during Peak Hours with Auto scaling Infrastructure..
Dimensional Efficiency: Embeddings compress complex, unstructured data into manageable mathematical representations while preserving essential relationships and meanings.
Universal Compatibility: The same embedding techniques work across text, images, audio, and video, enabling unified AI systems that can understand and relate content across modalities.
Scalable Similarity: Computing similarity between embeddings is mathematically efficient, enabling real-time comparison across millions of items with sub-second response times.
Transfer Learning: Pre-trained embedding models capture general knowledge that transfers to specific domains with minimal additional training.
How to Create High-Quality Embeddings
The quality of embeddings directly impacts the performance of downstream applications. Modern embedding models are trained on massive datasets using sophisticated architectures that capture both local and global patterns in data.
Domain Specialization: While general-purpose embeddings work well for many applications, domain-specific models trained on specialized corpora often deliver superior performance for particular use cases.
Embedding Dimensions: Higher dimensions can capture more nuanced relationships but require more storage and computation. The optimal dimension count balances expressiveness with efficiency.
Training Objectives: Modern embedding models use sophisticated training objectives like contrastive learning, triplet loss, and masked language modeling to learn meaningful representations.