# Performing Similarity Search

In a similarity search, the process begins by converting the user’s query (such as a piece of text or an image) into an embedding—a vector representation that captures the query’s semantic meaning. This embedding is generated using a pre-trained model, such as BERT for text or a neural network for images. Once the query is converted into a vector, it is compared to the embeddings stored in the vector database.