How do vector databases work?

How are embeddings searched in vector databases?

Vector Databases are at the heart of modern AI systems, especially for tasks like search, recommendations, and retrieval-augmented generation (RAG). But how do they efficiently search embeddings?

🔹 Vector databases store unstructured data (e.g. texts and images)

🔹 Vector DBs use a vector search called Approximate Nearest Neighbor.

🔹 ANN is much faster than KNN which has a slow search time.

🔹 ANN groups vectors, searches the nearest group, and refines the search within the group.

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