Text embedding made simple

Embeddings are crucial for modern semantic search and neural ranking, and Vespa.ai has simplified their creation and use by integrating an embedding feature directly into its platform. Previously, users needed to create embeddings on the client side or develop Java components, but with Vespa 8.54.61 or higher, embedding creation is streamlined through a simple addition to the services.xml file using a BERT-style model and vocabulary, such as the recommended model from Hugging Face. This allows for automatic conversion of text queries into embeddings, facilitating efficient search and evaluation of machine-learned models. Vespa.ai also provides a sample application for semantic search to help users get started, with additional resources available for those interested in exploring the integration of pretrained transformer models and vector search efficiency.