Embeddings
Embeddings Projects
Master vector embeddings for semantic search, similarity, and AI applications
Embeddings Projects
Transform text into powerful vector representations for semantic understanding
Learning Path
┌─────────────────────────────────────────────────────────────────────────────┐
│ EMBEDDINGS LEARNING PATH │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ BASIC │ │
│ │ Semantic Search Engine │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ INTERMEDIATE │ │
│ │ │ │
│ │ Text Clustering ──► Similarity ──► Visualization ──► Fine-tuning │ │
│ │ Recommendations │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ADVANCED │ │
│ │ │ │
│ │ Production Pipeline ──► Multi-Modal ──► Search at Scale │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Semantic Search Engine | Build semantic search with sentence-transformers | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| Text Clustering | Cluster documents using K-means and embeddings | ~3 hours |
| Similarity Recommendations | Build a recommendation system using cosine similarity | ~3 hours |
| Embedding Visualization | Visualize embeddings with t-SNE and UMAP | ~3 hours |
| Fine-tuning Embeddings | Train domain-specific embeddings | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Production Embedding Pipeline | Scalable embedding generation with caching and monitoring | ~3 days |
| Multi-Modal Embeddings | Combine text and image embeddings with CLIP | ~3 days |
| Search at Scale | Billion-scale search with FAISS and approximate nearest neighbors | ~4 days |
Why Learn Embeddings?
| Benefit | Description |
|---|---|
| Foundation | Embeddings power RAG, search, recommendations, and clustering |
| Semantic Understanding | Capture meaning beyond keyword matching |
| Versatility | Same techniques work for text, images, audio, and code |
| Efficiency | Dense vectors enable fast similarity computation |
Case Studies
Real-world implementations showing embeddings in production systems.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| E-commerce Product Discovery | E-commerce | Semantic search and personalized recommendations | Available |
Key Concepts
┌─────────────────────────────────────────────────────────────────────────────┐
│ EMBEDDINGS KEY CONCEPTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────┐ │
│ │Embeddings │ │
│ └─────┬─────┘ │
│ ┌───────────────────────┼───────────────────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────┐ ┌───────────┐ ┌───────────┐ │
│ │ Models │ │Operations │ │Applications│ │
│ └────┬────┘ └─────┬─────┘ └─────┬─────┘ │
│ │ │ │ │
│ • Sentence • Cosine • Semantic Search │
│ Transformers Similarity • Clustering │
│ • OpenAI Ada • Dot Product • Classification │
│ • Cohere • Euclidean • Recommendations │
│ • CLIP Distance │
│ │
│ ┌───────────┐ │
│ │ Scaling │ │
│ └─────┬─────┘ │
│ │ │
│ • ANN Indexes • Quantization • Sharding │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Start with the Semantic Search Engine project to learn the fundamentals.