Embeddings Magic

Embeddings turn language into math.

They are the foundation of modern AI. Many people treat them like a black box. This post explains how they work.

Keyword search fails when words do not match.

If you search for "How do I reset my password?", a keyword search looks for those exact words. If a document says "Steps to recover your account credentials", the search might fail. You know the meaning is the same. Computers do not.

Embeddings solve this problem.

An embedding is a list of numbers. These numbers represent the meaning of text. An embedding model maps words into a high dimensional space.

A single word like "cat" becomes a vector: [0.18, -0.42, 0.91, ...]

The numbers alone mean nothing. What matters is the position of the vector.

Think of a map. Cities near each other share similar climates and borders. Embeddings work the same way. Text with similar meanings sits near each other in vector space.

  • Dog and Cat sit close together.
  • Car and Truck sit close together.
  • Car and Dog sit far apart.

The distance between these points represents similarity.

This allows for semantic search. You can find information based on intent rather than spelling.

To compare these vectors, we use cosine similarity. This metric measures the angle between two vectors.

  • Small angle means high similarity.
  • Large angle means low similarity.

Embeddings also power Retrieval Augmented Generation (RAG). In a RAG pipeline, the process looks like this:

  1. Convert documents into vectors using an embedding model.
  2. Store vectors in a vector database.
  3. Convert a user query into a vector.
  4. Find the closest vectors in the database.
  5. Send the relevant documents to the LLM.

The LLM does not search your files directly. It searches the embedding space for the closest matches.

If you build AI applications, you must understand embeddings. They power everything from search engines to recommendation systems. Their strength lies in how they organize meaning.

Source: https://dev.to/tahaboussaden/embeddings-magic-2hlb

Optional learning community: https://t.me/GyaanSetuAi