Embedding

An embedding is the translation of text, an image, or other data into a sequence of numbers that captures its meaning. Content with similar meaning gets similar number sequences. Embeddings are the basis for an AI to search semantically and find the matching passages in your knowledge base.

What is an embedding?

An embedding turns content into a long sequence of numbers, a so-called vector. These numbers aren't random: they describe the meaning of the content in a mathematical space. Texts that are close in content get similar vectors, even when they use different words. “Parcel on its way” and “Shipment is being delivered” end up close together this way.

How embeddings capture meaning

During training, an AI model learned the contexts in which words appear. From that it infers what belongs together in content. The result is embeddings, with which similarity can be measured: the closer two vectors lie, the more related their content. This very measurement is what makes search in a vector database possible.

The role in RAG

In a RAG system, your documents are broken into embeddings and stored. When a customer asks a question, it too is converted into an embedding and compared with the stored ones. The most similar passages provide the material from which a Large Language Model then phrases the answer. Without embeddings there would be no reliable semantic search, and therefore no dependable AI answer from your own content.

Embeddings at SendSeven

SendSeven is a unified messaging platform with a built-in AI assistant. When you load content into your knowledge base, it is prepared for semantic search so the AI finds the matching passages for every customer question. The technical details run in the background; you only maintain your content. The knowledge base guide shows how to store content for semantic search. In line with GDPR, Made in Germany. Try it free for 14 days.