A layman example of Vector database
Here’s a layman-friendly example of representing a word as a 2D vector (just two numbers) — even though real embeddings are often 384, 768, or 1536 dimensions.
Let’s take the word: “king”
We want to represent this word using just two numbers, like:
arduinoCopyEdit"king" → [0.8, 0.4]
What do these numbers mean?
Imagine you’re placing words on a 2D graph, like an X-Y plane.
Let’s define:
- X-axis → “Power” (from weak to powerful)
- Y-axis → “Gender” (from female to male)
Example placements:
| Word | Vector | Meaning |
|---|---|---|
| “king” | [0.8, 0.4] | High power, more male |
| “queen” | [0.8, -0.4] | High power, more female |
| “man” | [0.2, 0.4] | Less power, male |
| “woman” | [0.2, -0.4] | Less power, female |
| “child” | [0.1, 0.0] | Low power, gender-neutral |
Now, using math on these vectors:
“king” – “man” + “woman” = ?
csharpCopyEdit[0.8, 0.4] - [0.2, 0.4] + [0.2, -0.4] = [0.8, -0.4] → "queen"
That’s how semantic meaning can be captured with simple vector math — even in just 2 dimensions.
In the real world, we use 768D or 1536D vectors where such patterns become incredibly powerful and accurate.
How do we arrive at [0.8, 0.4] for king
Great question — this is the core of embeddings: how do we get from "king" to [0.8, 0.4] (or [0.01, 0.02, ..., 0.99] in real systems)?
Short Answer:
We don’t manually assign numbers like [0.8, 0.4]. Instead, we train a machine learning model (like Word2Vec, GloVe, BERT, or OpenAI’s embedding models) on massive text corpora, and the model learns these numbers based on how words appear in context.
Layman Analogy:
Imagine you’re trying to understand a new word by looking at how it’s used in thousands of books and articles.
If “king” is often used near “throne,” “queen,” “kingdom,” “rule,” and “crown” — and rarely near “microwave” or “banana” — the model will learn its meaning through patterns.
How it’s done (simplified):
- Massive text is scanned, sentence by sentence: “The king sat on his throne.”
- The model notices:
- “king” appears near “throne”, “queen”, “rule”, etc.
- It adjusts its internal vector for “king” to match these relationships.
- It does this for every word, adjusting vector positions so that:
- Similar words have similar vectors
- Relationships like “king – man + woman ≈ queen” are possible via vector math
Example with Word2Vec (classic method):
Suppose the model sees these pairs:
- (“king”, “queen”) → very close in meaning
- (“king”, “man”) → similar gender
- (“king”, “ruler”) → similar role
The algorithm adjusts the vector for “king” so it’s:
- Near “queen” in vector space
- Far from unrelated words like “car” or “spoon”
This might converge to something like:
iniCopyEditking = [0.8, 0.4]
queen = [0.8, -0.4]
man = [0.2, 0.4]
Final Note:
The actual numbers like [0.8, 0.4] are:
- Learned from context
- Optimized using neural networks
- Stored in embedding tables that you can query later (like from OpenAI or HuggingFace)
