Embeddings & Vector Search

Also known as: Vector Embeddings, Semantic Vectors, Text Embeddings, Representation Learning

Numerical representations of text or data in high-dimensional vector spaces that capture semantic meaning for search and analysis.

Embeddings are dense numerical representations of text (words, phrases, documents) in high-dimensional vector spaces—typically 768 to 3072 dimensions—where semantically similar elements are geometrically close to each other. Models such as OpenAI's text-embedding-3, Cohere Embed, or Sentence-Transformers generate these representations.

Vector Search uses these representations to find documents semantically similar to a query, even when they share no exact words. It is the technical foundation of RAG systems, semantic search engines, and content recommenders.

In market research, embeddings enable: (1) semantic search in verbatim databases, (2) clustering of similar responses without exact keyword matching, (3) detection of similar themes across studies, and (4) powering RAG systems that allow LLMs to answer questions about large collections of research data.

Atlantia uses embeddings to index and make searchable the history of studies and insights for its clients.