Welcome back! we learned that a Vector Database like Chroma is essential for searching documents based on their meaning, not just exact keywords. We store numerical representations of text in this database.
But how do we get these numerical representations? How do we convert words and sentences into numbers that a computer can understand and use for similarity comparisons? That's where Text Embeddings come in.
Remember our goal: searching for information based on meaning. A computer fundamentally understands numbers. Text, on the other hand, is made of letters and words, which don't inherently have a numerical value that captures their meaning or relationship to other words.
Consider these sentences:
As a human, you know the first two sentences are very similar in meaning, while the third is quite different. A computer doing a simple keyword comparison wouldn't see this easily. "Cat" and "feline" look completely different, as do "mat" and "rug".
We need a way to translate text into a numerical format where the numbers themselves represent the meaning. This allows a computer to calculate how "close" two pieces of text are in meaning by simply comparing their corresponding numbers.
Text Embeddings are numerical representations of text. Think of them as turning words, sentences, or even larger chunks of text into a list of numbers, like:
[0.1, 0.5, -0.2, 0.9, ..., 1.2]
This list of numbers is often called a vector.
The key idea is that text with similar meanings will have vectors that are numerically "close" to each other. Text with different meanings will have vectors that are numerically "far apart".
Imagine a giant map (in many dimensions, but let's simplify it). Each word or sentence is placed somewhere on this map. The clever part is that words or sentences that mean similar things are placed closer together on the map!
| Text | Analogy |
|---|---|
| "king" | A point on the map |
| "queen" | A point near "king" |
| "prince" | A point near "king" |
| "apple" | A point far from "king" |
| "cat" | A point far from "king" |
| "The cat sat" | A point on the map |
| "A feline sat" | A point very close to "The cat sat" |
| "The dog barked" | A point far from "The cat sat" |
Text Embeddings are the coordinates of these points on the "meaning map". By converting text into these numerical coordinates (vectors), we can then use simple math to calculate the distance between them. A small distance means similar meaning; a large distance means different meaning.
This is what enables semantic search – searching by meaning!