AI Fundamentals

Embedding

An embedding is the mathematical representation of text, images, or other data as a numerical vector in a high-dimensional space. Semantically similar content is positioned close together. Embeddings are the foundation for vector databases, RAG systems, and semantic search — making meaning computable for machines.

Why does this matter?

Embeddings enable searching company data by meaning instead of keywords. "Find all complaints about delivery delays" works even when the phrase "delivery delay" does not appear in the text. This dramatically improves knowledge management, customer service, and internal research.

How IJONIS uses this

We deploy embedding models from OpenAI, Cohere, and open-source alternatives (E5, BGE) — depending on language and privacy requirements. For German texts, we test multiple models since quality varies significantly for non-English languages. Embeddings are indexed in vector databases and regularly updated.

Frequently Asked Questions

What is the difference between an embedding and a keyword index?

A keyword index only finds exact word matches. An embedding captures meaning: "car" and "automobile" are close together in vector space despite being different words. This lets you find content even when you cannot recall the exact phrasing.

How large are embedding models and what do they cost?

Embedding models are significantly smaller than LLMs — they run easily on standard servers without GPUs. Cloud APIs cost fractions of a cent per text. Even millions of documents can be embedded for just a few euros per month.

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