Embeddings are vector representations of unstructured data, such as text, in the form of fixed-length numerical vectors. Such a vector encodes the semantic meaning of an object so that semantically similar objects are placed close to each other in the vector space.
Because of this, embeddings are used for semantic search, clustering, recommendation, deduplication, and retrieval in RAG systems.
Once embeddings have been generated and stored, we can compare them using metrics such as cosine similarity or run a nearest neighbor search to find the most relevant items by meaning, not just by exact word matches.
For example, the phrases “How to connect Node.js to PostgreSQL” and “Node.js PostgreSQL database connection” will have similar embeddings even though the words differ. That is why embeddings let you search for information not only by exact word matches but also by meaning, which makes it far more effective than ordinary full-text search.
This diagram shows how an embedding model turns unstructured data, such as текст, зображення, or аудіо, into numerical vectors. As a result, semantically similar objects are placed next to each other in the vector space, which makes it possible to find related items and perform semantic search.
