A Short Introduction to Text Embeddings

Let's start by understanding the shortcomings of traditional text retrieval methods like string matching and TF-IDF (Term Frequency-Inverse Document Frequency). Suppose you search the keywords "solar power" on a search tool that leverages a simple string matching algorithm. Ideally, you'd expect articles about renewable energy.

But instead, you receive documents discussing power laws in astronomy since the keywords "solar" and "power" are taken out of context. This example underscores a critical flaw of conventional text retrieval methods: they often fail to capture the intent of a query. They focus on the surface — the explicit words used, rather than the intended meaning that the words represent.

Transitioning from Keywords to Concepts

This is where text embeddings make a monumental difference. They represent a paradigm shift from keyword-based retrieval to concept-oriented understanding. Text embeddings allow us to encode words and phrases numerically, capturing more than just their literal meaning. This numerical representation encodes words into multi-dimensional spaces, where distances and angles between words convey aspects of their meanings and relationships. Don't worry if this sounds confusing right now — we'll dive into the details later.

This shift from mere keywords to the realm of concepts opens up new possibilities in text retrieval. With embeddings, we're not just looking at the words used in queries and documents like traditional text retrieval methods. Instead, we're comparing the underlying intent of the query with the ideas embodied in each document. This nuanced approach enables us to retrieve documents that are conceptually related to the query, even if they don't share exact keywords, and avoid documents that share keywords but don't match the query's intent.