Glossary · AI & Agents
Text Embeddings
Overview
What text embeddings are
A text embedding is a list of numbers, called a vector, that represents a piece of text in a way that captures its meaning rather than its exact wording. Similar meanings map to nearby points in a high-dimensional space, so "car" and "automobile" land close together while "car" and "banana" sit far apart.
Embeddings are generated by embedding models, such as those from OpenAI, Cohere, Voyage AI, or open-source options. Each model outputs vectors of a fixed length, often several hundred to a few thousand dimensions.
How they work
An embedding model is trained so that text with related meaning produces vectors that are close by a distance measure like cosine similarity. To search or compare, you embed your query and the candidate texts, then rank candidates by how close their vectors are to the query vector.
At scale, embeddings are stored in a vector database or index, which performs fast nearest-neighbor search across millions of vectors. This is the retrieval backbone of semantic search and RAG pipelines.
Why they matter
Embeddings let systems match on meaning instead of exact keywords, which is what makes modern semantic search, question answering, and RAG possible. They also power clustering, deduplication, classification, and recommendations.
In a retrieval-augmented generation system, embeddings find the most relevant chunks of your knowledge base so a language model can answer from real, current content rather than guessing. The choice of embedding model and chunking strategy directly affects retrieval quality.
Where we use it
Related Zen in Tech services
How our team puts Text Embeddings to work in real projects.
FAQ
Text Embeddings — common questions
What is a text embedding in simple terms?
A text embedding is a numeric fingerprint of text that captures its meaning. Text with similar meaning gets similar numbers, so software can measure how related two pieces of text are.
What are text embeddings used for?
They power semantic search, retrieval-augmented generation, recommendations, clustering, classification, and deduplication, anywhere a system needs to compare or find text by meaning rather than exact keywords.
Where are embeddings stored?
Embeddings are usually stored in a vector database or vector index, such as Pinecone or pgvector, which performs fast nearest-neighbor search to find the most similar vectors to a query.
Keep exploring
Related terms
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