Code Examples and Templates

This appendix provides information about the production-ready code templates and examples for implementing embedding systems.

GitHub Repository

All code examples from this book are available in the companion GitHub repository:

Repository: github.com/snowch/embeddings-at-scale-book

Code Location: /code_examples/ directory

Repository Structure

The code examples are organized by chapter:

code_examples/
├── README.md                          # Master guide
├── requirements.txt                   # Python dependencies
├── ch01_foundations/                  # Chapter 1 examples
│   ├── README.md
│   └── *.py (16 files)
├── ch02_strategic_architecture/       # Chapter 2 examples
│   ├── README.md
│   └── *.py (30 files)
├── ch03_vector_database_fundamentals/ # Chapter 3 examples
│   └── *.py (21 files)
├── ch04_custom_embedding_strategies/  # Chapter 4 examples
│   └── *.py (21 files)
├── ch05_contrastive_learning/         # Chapter 5 examples
│   └── *.py (21 files)
├── ch06_siamese_networks/             # Chapter 6 examples
│   └── *.py (13 files)
└── ch07-ch30/                         # Remaining chapters
    └── *.py (130+ files)

Total: 253 Python files containing 66,908 lines of code

Getting Started

Clone the Repository

git clone https://github.com/snowch/embeddings-at-scale-book.git
cd embeddings-at-scale-book/code_examples

Install Dependencies

All code examples use a common set of dependencies:

pip install -r requirements.txt

The requirements include:

  • PyTorch (≥2.0.0) - Deep learning framework
  • Transformers (≥4.30.0) - Hugging Face transformers
  • Sentence-Transformers (≥2.2.0) - Embedding models
  • FAISS (≥1.7.4) - Vector similarity search
  • NumPy, Pandas, scikit-learn - Data processing
  • And 15+ additional libraries

Run Examples

Each chapter directory contains a README with specific instructions. General pattern:

# Navigate to chapter directory
cd ch05_contrastive_learning

# Run specific example
python3 infonceloss.py

# Or import in your own code
from infonceloss import InfoNCELoss

Key Code Categories

Embedding Training

Chapters with complete training implementations:

  • Ch05: Contrastive learning (InfoNCE, SimCLR, MoCo)
  • Ch06: Siamese networks (contrastive loss, triplet loss)
  • Ch07: Self-supervised learning (BERT-style, autoencoder)
  • Ch08: Advanced techniques (hyperbolic, dynamic, federated)

Vector Operations

Production-ready vector database code:

  • Ch03: Vector database fundamentals (HNSW, IVF, PQ)
  • Ch11: High-performance operations (quantization, compression)
  • Ch14: Semantic search implementation

Production Engineering

Scalability and deployment:

  • Ch09: Embedding pipelines (MLOps, monitoring)
  • Ch10: Distributed training (multi-GPU, multi-node)
  • Ch12: Data engineering (preprocessing, validation)

Advanced Applications

Complete application examples:

  • Ch13: RAG at scale
  • Ch15: Recommendation systems
  • Ch16: Anomaly detection
  • Ch17: Automated decision systems

Industry Applications

Domain-specific implementations:

  • Ch18: Financial services
  • Ch19: Healthcare and life sciences
  • Ch20: Retail and e-commerce
  • Ch21: Manufacturing and Industry 4.0
  • Ch22: Media and entertainment

Code Quality

All code examples have been:

  • ✅ Syntax checked (98%+ pass rate)
  • ✅ Organized with clear naming conventions
  • ✅ Documented with inline comments
  • ✅ Accompanied by chapter-specific READMEs

Usage Guidelines

Educational Use

All code is provided for educational purposes under the book’s Creative Commons license:

  • ✅ Free to use for learning
  • ✅ Free to modify and experiment
  • ✅ Free to share with attribution

Production Use

The code examples are designed as templates and learning tools. For production use:

  1. Review security: Add authentication, input validation, rate limiting
  2. Add error handling: Production-grade exception handling
  3. Optimize for scale: Add caching, monitoring, logging
  4. Test thoroughly: Unit tests, integration tests, load tests

Additional Resources

Chapter READMEs

Each chapter directory contains a README with:

  • Overview of code examples
  • Key algorithms implemented
  • Usage instructions
  • Dependencies and requirements

Master README

The /code_examples/README.md provides:

  • Complete file listing
  • Quick start guide
  • Common issues and solutions
  • Contribution guidelines

Reporting Issues

If you find issues with the code examples:

  1. Check the chapter README for known issues
  2. Verify you’re using compatible library versions
  3. Report issues on the GitHub repository

Contributing

Contributions are welcome! See the repository’s CONTRIBUTING.md for:

  • Code style guidelines
  • Testing requirements
  • Pull request process

Quick Links: