Vector databases have become a cornerstone of modern AI infrastructure, powering applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG). Unlike traditional relational databases that store data in rows and columns, vector databases are designed to store and query high-dimensional vectors, typically embeddings generated by machine learning models. They excel at similarity search, allowing developers to find items closest to a given query vector using distance metrics like cosine similarity or Euclidean distance. This capability is crucial for building features such as 'find similar products' or 'semantic document search'. Popular vector databases include Pinecone, Weaviate, Milvus, and Qdrant, each offering unique features like hybrid search, filtering, and scalability. For developers, understanding vector databases is key to unlocking the full potential of AI models, as they bridge the gap between raw embeddings and practical, user-facing applications. This guide provides a foundational overview, helping engineers decide when and how to integrate vector databases into their stack.
Vector databases are essential for AI applications, enabling efficient similarity search on embeddings. This guide explains their core concepts, use cases, and how they differ from traditional databases.