The relationship between Embedding and vector database

Embeddings are numerical vector representations of data, such as text, images, or audio. Vector databases are specialized systems designed to efficiently store, index, and search these high-dimensional embedding vectors based on similarity.

Vector databases provide the infrastructure specifically optimized to handle embeddings. They utilize specialized indexing algorithms (like HNSW, IVF, or PQ) and distance metrics (like cosine similarity or Euclidean distance) to rapidly find vectors similar to a given query vector. This enables efficient nearest neighbor searches, which is computationally intensive for traditional databases. Vector databases manage the storage, retrieval, and similarity computation operations for embeddings.

This combination powers core applications like semantic search (finding text with similar meaning), recommendation systems (finding similar items), anomaly detection (identifying dissimilar vectors), and retrieval-augmented generation (RAG). By storing embeddings in a vector DB, developers can quickly build applications that rely on finding data points with similar semantic or contextual properties.