AI Videos

Understanding Basic Vector Search With KNN | Vector Databases for Beginners | Part 12

The video introduces the K‑Nearest Neighbors (KNN) algorithm as the core of vector search, explaining how each query and document is transformed into a numeric embedding that lives in a multi‑dimensional space. By measuring the distance between these vectors, the system can rank documents by semantic similarity rather than exact keyword matches. Key insights include the process of generating embeddings with an encoder model, persisting them in a vector‑oriented database, and then converting incoming queries into the same space to compute similarity scores. Various distance metrics—such as cosine similarity or Euclidean distance—can be applied, each influencing the relevance of returned results. The presenter illustrates the concept with a simple example: a query for "kitten" yields nearby vectors like "cat" and "dog," while unrelated terms like "fruit" lie farther away. He contrasts this with traditional keyword search, which would only return items containing the exact term "cola," whereas vector search would also surface related brands like Pepsi or Fanta. For businesses, adopting vector search can dramatically improve user experience by delivering contextually relevant results, reducing reliance on exact phrasing, and enabling more natural language interactions across e‑commerce, support, and content platforms.