Vector Embeddings & Semantic Search
Understanding Vector Embeddings
Vector embeddings are numerical representations of text that capture semantic meaning. They transform words, sentences, or documents into high-dimensional vectors (arrays of numbers) where similar meanings result in similar vectors.
The Core Idea
Traditional text matching:
Query: "library building"
Matches: Documents containing exactly "library" AND "building"
Misses: "biblioth", "książnica", "knowledge center", "reading hall"Semantic search with embeddings:
Query: "library building"
Embedding: [0.23, -0.45, 0.78, ...]
Matches: Similar vectors representing:
- "library facility"
- "bibliotheca structure"
- "book repository architecture"
- "community learning center"Key Insight: Embeddings allow computers to understand that “library” and “bibliothèque” (French) are semantically similar, even though they share no letters.
Why Embeddings Matter
Search by Meaning, Not Keywords:
- Find “python programming tutorial” when searching for “learning to code with python”
- Match “climate change impact” with “global warming effects”
- Discover relevant content regardless of exact wording
Enable Semantic Applications:
- Question answering systems
- Document similarity detection
- Recommendation engines
- Retrieval-Augmented Generation (RAG)
What You’ll Learn
Visualization
Imagine plotting documents in 2D space (real embeddings have 1,000+ dimensions):
"library" •
"book" • • "bibliothèque"
"reading" •
• "computer" • "knowledge"
(words with similar meanings cluster together)Documents about libraries cluster near each other, even if written in different languages or using different terms.