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Glossary · AI

Embeddings

Embeddings are lists of numbers that represent the meaning of a piece of text, an image or other data. Items with similar meaning get similar numbers, which lets software search and group content by meaning rather than exact words.

In one line

Embeddings are lists of numbers that represent the meaning of a piece of text, an image or other data. Items with similar meaning get similar numbers, which lets software search and group content by meaning rather than exact words.

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Why embeddings matter

Keyword search misses results that use different words for the same idea. Embeddings let a system find "cancel my plan" when the help article says "end your subscription".

They power semantic search, recommendations, clustering and most RAG systems.

How embeddings work

An embedding model converts each item into a vector, a long list of numbers. To find related items, the system compares vectors and returns the closest ones.

Vectors are usually stored in a vector database built for fast similarity search.

Common embedding mistakes

Mixing vectors from different embedding models. Embedding huge documents as one block, which blurs their meaning. Forgetting to re-embed content when it changes.

How Gr8r approaches embeddings

We choose an embedding model based on your content and languages, test search quality with real queries, and often combine embeddings with keyword search for better results.

Example

A product catalogue embeds every item description so shoppers searching for "waterproof walking boots" also see items described as "hiking shoes with a membrane".

FAQ

Questions

  • Are embeddings the same as a language model?

    No. Embedding models turn content into vectors for comparison. Language models generate text. RAG systems usually use both.

  • Do embeddings work across languages?

    Many modern embedding models are multilingual, but quality varies by language, so it is worth testing with your own content.

Related terms.

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