Why Do We Need Specialized Vector Databases? In the first five articles, we figured out how to chunk documents and generate embeddings. Now where do these vectors live, and how are they efficiently retrieved? You might wonder: "Can't I just store vectors in Redis or PostgreSQL?" No — traditional databases are designed for exact queries (e.g., WHERE id = 123 ), while vector retrieval is Approximate Nearest Neighbor (ANN) search : given a query vector, quickly find the Top-K most similar vectors among hundreds of millions of document vectors. Traditional database indexes (B-trees, hash tables) are powerless against this type of "similarity query." Example: Traditional query: Find user with id=42 → O(1) or O(log n) Vector query: Find 10 people most similar to user A → requires comparing against all vectors, brute-force O(n) is too slow Vector databases use specialized ANN indexes (HNSW, IVF, etc.) to reduce O(n) to O(log n), completing similarity searches across billions of vectors in milliseconds.…