Vector Databases and Semantic Search: A Practical Introduction

Traditional search engines match keywords. If you search for "dog shelters around Gurgaon" and the indexed page says "animal shelters near Delhi," you get no results. The words do not overlap. Semantic search fixes this by converting text into vectors. Similar ideas end up close together in vector space, even when the words differ. From words to vectors An embedding model takes a word or sentence and produces a high-dimensional vector. The key property: semantically similar inputs produce vector