Vector databases have one thing in common — they support similarity searches. Similarity searches identify the “closest” record in the database to a vector. This is important for operations such as recommendation engines and personalization tools. Unlike keywords and SQL searches, similarity search ...
Inner product– The product of the magnitudes of two vectors and the cosine of the angle between them. Usually used for natural language processing (NLP) vector similarity. Cosine similarity– The cosine of the angle between two vectors in a vector space. Hamming distance– For binary-coded vec...
Vector databases—also known as vector search engines, semantic, or cosine search—look for thenearest neighborsto a given vectorized query. These databases encode information in vector spaces, enabling rapid similarity queries, thereby boosting the efficiency of search applications. ...
but not identical, text blocs are. You should always refine the cosine similarity value to sort or filter on the highest one. As you may have understood by now, embeddings are not meant to be used in a context where you expect a binary answer (0 ...