concept

Vector Search

Vector search is a technique for retrieving information by representing data as high-dimensional vectors (embeddings) and finding similar items based on vector similarity measures like cosine similarity or Euclidean distance. It enables semantic search, recommendation systems, and similarity matching by capturing the meaning or context of data rather than relying on exact keyword matches. This approach is widely used in applications involving natural language processing, image recognition, and large-scale data retrieval.

Also known as: Semantic Search, Similarity Search, Nearest Neighbor Search, Embedding Search, Vector Similarity Search

🧊Why learn Vector Search?

Developers should learn vector search when building applications that require semantic understanding, such as chatbots, content recommendation engines, or fraud detection systems, as it improves search relevance beyond traditional keyword-based methods. It is particularly useful in AI-driven projects where data needs to be queried based on similarity, such as in machine learning models for embeddings or real-time search in databases like vector databases.