Vector Database Tutorial

A comprehensive, hands-on tutorial that takes you from zero to advanced understanding of vectors, embeddings, vector databases, and Retrieval Augmented Generation (RAG) patterns for NLP and LLM applications.

🚀 Quick Start

# Clone and navigate to the repository
cd cloned_repo

# Run with Docker Compose - specific module (recommended)
MODULE=1 docker compose up      # Module 1: Vector Basics
MODULE=2 docker compose up      # Module 2: Text Embeddings
MODULE=3 docker compose up      # Module 3: Similarity Search
MODULE=4 docker compose up      # Module 4: Vector Databases
MODULE=5 docker compose up      # Module 5: Advanced Techniques
MODULE=6 docker compose up      # Module 6: RAG Patterns
MODULE=ALL docker compose up    # Run all modules sequentially

# Or run locally (requires Python 3.11+)
pip install -r requirements.txt
python main.py

📚 What You'll Learn

This tutorial is structured into 6 progressive modules:

Module 1: Introduction to Vectors (Beginner)

• What are vectors and why they matter
• Basic vector operations (addition, multiplication, dot product)
• Vector magnitude and normalization
• Introduction to cosine similarity
• Working with high-dimensional vectors

Module 2: Text Embeddings (Beginner)

• Understanding text embeddings
• From bag-of-words to neural embeddings
• Using Sentence Transformers
• Creating embeddings for real documents
• Embedding properties and characteristics
• Choosing the right embedding model

Module 3: Vector Similarity and Semantic Search (Intermediate)

• Distance metrics (cosine, euclidean, dot product)
• Building a semantic search engine from scratch
• Semantic search vs keyword search
• Ranking strategies and similarity thresholds
• Advanced features: multi-query, re-ranking, query expansion
• Performance optimization

Module 4: Vector Databases (Intermediate)

• Why vector databases are essential
• ChromaDB fundamentals
• Storing and querying embeddings
• CRUD operations
• Collections and data management
• Performance and scaling considerations

Module 5: Advanced Vector Database Techniques (Advanced)

• Complex metadata filtering
• Hybrid search (semantic + keyword + filters)
• Re-ranking strategies
• Multi-collection architecture
• Query optimization techniques
• Production-ready patterns

Module 6: Retrieval Augmented Generation (RAG) (Advanced)

• RAG architecture and motivation
• Document chunking strategies
• Building a complete RAG system
• Advanced patterns: query expansion, HyDE, parent document retrieval
• Common challenges and solutions
• Evaluation metrics and monitoring

🎯 Learning Path

Zero Knowledge → Beginner → Intermediate → Advanced
     ↓              ↓            ↓             ↓
  Module 1     Modules 1-2   Modules 1-4   All Modules

Estimated Time:

• Complete tutorial: 3-4 hours
• Individual module: 20-40 minutes

🛠️ Technology Stack

• Python 3.11+: Programming language
• NumPy: Vector operations
• Sentence Transformers: Text embeddings
• ChromaDB: Vector database
• scikit-learn: ML utilities
• Docker: Containerization

📁 Project Structure

cloned_repo/
├── main.py                           # Main tutorial runner
├── module1_vectors_basics.py         # Module 1: Vector fundamentals
├── module2_text_embeddings.py        # Module 2: Text embeddings
├── module3_similarity_search.py      # Module 3: Semantic search
├── module4_vector_databases.py       # Module 4: Vector databases
├── module5_advanced_techniques.py    # Module 5: Advanced techniques
├── module6_rag_patterns.py           # Module 6: RAG patterns
├── example_corpus/                   # Sample job ad documents
│   ├── job_ad_1.txt
│   ├── job_ad_2.txt
│   └── ...
├── outputs/                          # Generated visualizations
├── chroma_db/                        # Persistent vector database
├── requirements.txt                  # Python dependencies
├── Dockerfile                        # Docker configuration
├── docker-compose.yml                # Docker Compose setup
└── README.md                         # This file

🐳 Docker Usage

Run Specific Module (Recommended)

MODULE=1 docker compose up       # Module 1: Vector Basics
MODULE=2 docker compose up       # Module 2: Text Embeddings
MODULE=3 docker compose up       # Module 3: Similarity Search
MODULE=4 docker compose up       # Module 4: Vector Databases
MODULE=5 docker compose up       # Module 5: Advanced Techniques
MODULE=6 docker compose up       # Module 6: RAG Patterns
MODULE=ALL docker compose up     # Run all modules sequentially

Alternative: Direct Module Execution

docker compose run --rm vector-tutorial python module1_vectors_basics.py

Interactive Mode (requires terminal interaction)

docker compose run --rm vector-tutorial python main.py

💻 Local Usage (Without Docker)

Prerequisites

• Python 3.11 or higher
• pip

Installation

# Install dependencies
pip install -r requirements.txt

# Run the tutorial
python main.py

Command Line Options

# Interactive mode (default)
python main.py

# Run all modules
python main.py --all

# Run specific module
python main.py 1   # Module 1
python main.py 2   # Module 2
# ... etc

# Run individual module directly
python module1_vectors_basics.py

📊 Example Outputs

The tutorial generates visualizations and outputs in the outputs/ directory:

• Vector visualizations
• Similarity matrices
• Search result rankings
• Performance comparisons

🎓 Learning Features

• Interactive: Press Enter to proceed through lessons
• Hands-on: Work with real job advertisement data
• Progressive: Builds knowledge step-by-step
• Practical: Production-ready patterns and best practices
• Visual: Includes diagrams and visualizations
• Code Examples: Complete, runnable code for every concept

🔑 Key Concepts Covered

Vectors & Embeddings

• Vector spaces and dimensionality
• Semantic meaning representation
• Transformer-based embeddings
• Embedding model selection

Similarity & Search

• Cosine similarity
• Euclidean distance
• Semantic vs keyword search
• Ranking algorithms

Vector Databases

• CRUD operations
• Metadata filtering
• Hybrid search
• Indexing strategies
• Scalability patterns

RAG Patterns

• Document chunking
• Context retrieval
• Prompt engineering
• Query expansion
• Evaluation metrics

🚀 Next Steps After Completion

1. Build Your Own RAG App: Use the patterns learned to build a Q&A system
2. Explore Other Vector DBs: Try Pinecone, Weaviate, or Milvus
3. Fine-tune Embeddings: Learn to fine-tune models on your domain
4. Production Deployment: Scale your vector database for production
5. Integrate LLMs: Connect to GPT-4, Claude, or open-source LLMs

📚 Additional Resources

• ChromaDB Documentation
• Sentence Transformers
• RAG Papers and Research

🤝 Contributing

This is a learning project. Feel free to:

• Add more example documents
• Create additional modules
• Improve explanations
• Add more visualizations
• Suggest improvements

📝 License

This tutorial is provided for educational purposes.

⚠️ Note

This tutorial uses:

• Pre-trained models that will be downloaded on first run (~80MB)
• Local storage for ChromaDB (persistent across runs)
• The example corpus provided (job advertisements)

🎉 Credits

Built with:

• Sentence Transformers by UKPLab
• ChromaDB by Chroma
• Python open-source ecosystem

💡 Tips

1. Go at your own pace: Each module is self-contained
2. Experiment: Modify the code and see what happens
3. Use your own data: Replace the job ads with your own documents
4. Ask questions: The code is heavily commented
5. Build projects: Apply what you learn to real problems

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Happy Learning! 🚀

Start your journey into the world of vector databases and modern AI applications!