Example Concept: Semantic search with vector databases typically involves using pre-trained language models to convert text into embeddings (numerical vectors). These embeddings capture the semantic meaning of the text. The vectors are then stored in a vector database, which allows for efficient similarity searches using metrics like cosine similarity or Euclidean distance. When a search query is made, it is also converted into an embedding, and the database retrieves the most similar vectors, thus providing semantically relevant results.