Welcome to Vector Databases & Embeddings! This course will teach you about one of the most critical technologies in modern AI applications - how to represent, store, and search through data using vector representations.

What are Embeddings?

Embeddings are dense vector representations of data (text, images, audio) that capture semantic meaning in a high-dimensional space. Similar concepts are positioned close together in this space, enabling machines to understand relationships and similarities that would be impossible with traditional keyword-based approaches.

For example, in a well-trained embedding space:

• "King" - "Man" + "Woman" ≈ "Queen"
• "Paris" is close to "France" just as "Tokyo" is close to "Japan"
• "Happy" is near "joyful" and "pleased" but far from "sad"

Why Embeddings Matter for AI Applications

Embeddings revolutionize how we work with unstructured data:

• Semantic Understanding - Capture meaning, not just exact keywords. "automobile" matches "car" even without exact word match.
• Similarity Search - Find related content based on meaning, not just text overlap.
• Transfer Learning - Use pre-trained embedding models for new tasks without training from scratch.
• Dimensionality Reduction - Compress high-dimensional data into dense, meaningful representations.
• Cross-Modal Search - Search images with text, or find similar audio clips using text descriptions.
• RAG Foundation - Embeddings power Retrieval Augmented Generation systems that give LLMs access to external knowledge.

Evolution of Text Embeddings

1. One-Hot Encoding (Early Days)

Each word is a sparse vector with a single 1 and all 0s. Vocabulary of 50,000 words = 50,000-dimensional vectors. No semantic information captured.

"cat" = [1, 0, 0, 0, 0, ...]  (position 0)
"dog" = [0, 1, 0, 0, 0, ...]  (position 1)
"animal" = [0, 0, 1, 0, 0, ...]  (position 2)

Problem: "cat" and "dog" are equidistant from "animal" and from each other
         No semantic similarity captured!

2. Word2Vec (2013)

First widely successful dense word embeddings. Trained using either Skip-gram (predict context from word) or CBOW (predict word from context).

• Dense vectors (typically 100-300 dimensions)
• Captures semantic relationships
• Famous for analogies: king - man + woman ≈ queen
• Limitation: One embedding per word, regardless of context

3. GloVe (2014)

Global Vectors for Word Representation. Combines global co-occurrence statistics with local context.

4. FastText (2016)

Word embeddings with subword information. Can handle out-of-vocabulary words by composing subword embeddings.

5. Contextual Embeddings - BERT (2018)

Bidirectional Encoder Representations from Transformers. Different embeddings for the same word based on context.

"I deposited money at the bank"
  → "bank" embedding captures financial meaning

"I sat by the river bank"
  → "bank" embedding captures geographical meaning

Same word, different embeddings based on context!

6. Sentence Transformers (2019+)

Models specifically trained to produce meaningful sentence/document embeddings. Optimized for semantic similarity tasks. These are what we use for RAG.

What are Vector Databases?

Vector databases are specialized storage systems designed to efficiently store, index, and query high-dimensional vector embeddings. Unlike traditional databases that search for exact matches, vector databases find similar items based on proximity in vector space.

Key Differences from Traditional Databases

Key Concepts

• Vector Space - Multi-dimensional space where each dimension represents a learned feature. Typical embedding dimensions: 384, 768, 1024, 1536, 3072.
• Similarity Metrics - Measures of how "close" two vectors are: cosine similarity, Euclidean distance, dot product.
• Approximate Nearest Neighbor (ANN) - Algorithms that trade perfect accuracy for speed. Essential for searching millions of vectors.
• Indexing Algorithms - Structures like HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), and LSH for efficient search.
• Hybrid Search - Combining vector similarity search with traditional keyword (BM25) search for comprehensive retrieval.
• Metadata Filtering - Constraining vector search by document attributes (date, source, category).

Choosing a Vector Database

The vector database ecosystem includes several powerful options:

Real-World Applications

Vector databases and embeddings power modern AI applications:

• Semantic Search - Understanding user intent, not just matching keywords. "cheap flights to europe" matches "budget airfare to Paris".
• RAG Systems - Retrieval Augmented Generation for AI chatbots with custom knowledge.
• Recommendation Systems - Finding similar products, content, or users based on learned representations.
• Image Search - Finding visually similar images using CLIP or other vision embeddings.
• Question Answering - Finding relevant documents from knowledge bases to answer questions.
• Anomaly Detection - Identifying outliers in data by finding points far from normal clusters.
• Deduplication - Finding duplicate or near-duplicate content across large datasets.
• Personalization - Matching user preference vectors with content vectors.

How It Works: The Workflow

1. EMBEDDING GENERATION
   ┌─────────────────────────────────────────────────────┐
   │ Text/Image/Audio → Embedding Model → Dense Vector  │
   │ "The quick brown fox" → [0.12, -0.34, 0.56, ...]   │
   └─────────────────────────────────────────────────────┘

2. STORAGE
   ┌─────────────────────────────────────────────────────┐
   │ Vector + Metadata → Vector Database (Indexed)      │
   │ [0.12, -0.34, ...] + {"source": "doc1.pdf"}       │
   └─────────────────────────────────────────────────────┘

3. QUERY
   ┌─────────────────────────────────────────────────────┐
   │ Query → Embedding Model → Query Vector             │
   │ "fast fox" → [0.14, -0.32, 0.58, ...]             │
   └─────────────────────────────────────────────────────┘

4. SEARCH
   ┌─────────────────────────────────────────────────────┐
   │ Query Vector → ANN Search → Top-K Similar Vectors  │
   │ Find k nearest neighbors in high-dimensional space │
   └─────────────────────────────────────────────────────┘

5. RETRIEVE
   ┌─────────────────────────────────────────────────────┐
   │ Top-K Vectors → Original Content + Metadata        │
   │ Return the most similar items with their data      │
   └─────────────────────────────────────────────────────┘

What You'll Learn in This Course

This comprehensive course covers:

• Understanding Embeddings - Word, sentence, and document embeddings; how they're trained and why they work
• Vector Similarity - Distance metrics (cosine, Euclidean, dot product) and when to use each
• Indexing Algorithms - HNSW, IVF, LSH, and PQ for efficient approximate search
• Vector Database Deep Dives - Hands-on with Pinecone, Weaviate, Qdrant, ChromaDB, and Milvus
• Semantic Search - Building production semantic search systems
• Hybrid Search - Combining vector and keyword search for best results
• Performance Optimization - Tuning index parameters, quantization, and scaling strategies
• Production Deployment - Best practices for deploying vector search at scale

Prerequisites

• Basic machine learning concepts
• Python programming experience
• Understanding of APIs and databases
• Familiarity with basic linear algebra (vectors, dot product)

By the end of this course, you'll be able to build sophisticated semantic search and retrieval systems using vector databases and embeddings - the foundation for modern AI applications.

Let's explore the world of vector databases and embeddings!