The use of information retrieval systems to bring data to AI models for personalized inference has exploded in popularity. For years, vector embeddings have been used to extract salient and semantically relevant features from text, image, audio, video, and tabular data.
With the rise of deep learning approaches, vector embeddings can capture semantic relevancy with high quality. The recent rise in both small and large language models has introduced a wave of model options, services, and APIs to generate vector embeddings across a spectrum of tasks and domains. Unfortunately, many of today’s options fall short of satisfying the requirements of modern and robust AI applications.
Simply put, developers need solutions that manage and index vectors effectively. Read this white paper to learn about Microsoft’s solution.
Read the paper.