Pinecone Vector Databases

Traditional databases store data in rows and columns, query by exact match, find a customer by ID, find a transaction by date. In 2021, AI models worked differently. They converted data into vectors, mathematical representations of meaning. A question became a vector, a document became a vector. Similarity meant finding vectors close in mathematical space. Existing databases could not do this efficiently. Edo Liberty saw the gap, he built pinecone as a vector database designed specifically for semantic search. Store a document, convert it to a vector, query by meaning, not keywords. The database instantly returns the most semantically similar documents. No traditional database could do this at scale. Queries that required sorting through millions of documents ran in milliseconds.