Discover how modern vector databases and embeddings models are transforming the way we conduct semantic searches. By converting user queries into embeddings, relevant document chunks are matched and passed to large language models, enabling them to generate human-like responses. This innovative approach marks a significant leap from traditional keyword-based searches, highlighting the growing importance of embeddings in various applications.
