--- ## Highlights Embeddings capture the “relatedness” of text, images, video, or other types of information. This relatedness is most commonly used for: - **Search:** how similar is a search term to a body of text? - **Recommendations:** how similar are two products? - **Classifications:** how do we categorize a body of text? - **Clustering:** how do we identify trends? ([View Highlight](https://read.readwise.io/read/01h00ndrykpepfvhvk4s8xsng4)) Your process might look something like this: 1. Pre-process your knowledge base and generate embeddings for each page 2. Store your embeddings to be referenced later (more on this) 3. Build a search page that prompts your user for input 4. Take user's input, generate a one-time embedding, then perform a similarity search against your pre-processed embeddings. 5. Return the most similar pages to the user ([View Highlight](https://read.readwise.io/read/01h00nj35fgefvr60wp21mnswx))