Vector search retrieves results by comparing a query vector with stored vectors in a dataset to identify the most similar ones. The process involves three main steps: vector generation, similarity measurement, and retrieval. First, data is converted into vectors using embedding models like Word2Vec or Sentence-BERT. Each vector encapsulates the semantic essence of the corresponding data. For instance, a user query about "climate change" might be transformed into a vector emphasizing related concepts.
Next, similarity metrics like cosine similarity or Euclidean distance compare the query vector against stored vectors. These metrics calculate how closely the vectors align in the high-dimensional space. Finally, the system retrieves and ranks the vectors that are most similar to the query, presenting the user with the most relevant results, such as articles or images related to "climate change."