The Role of Vector Databases in AI Applications

Vector databases are designed to store high-dimensional embeddings, allowing fast similarity search between complex data points. In AI systems, they are essential for enabling semantic search, which goes beyond keywords to understand user intent. A common use case is Retrieval-Augmented Generation (RAG), where a model queries a vector store to retrieve the most relevant context before generating a response. This not only improves answer accuracy but also enhances transparency and relevance. Modern tools like FAISS, Milvus, and Pinecone support billion-scale embedding queries with real-time latency. By integrating vector databases, companies can build AI systems that are smarter, more context-aware, and personalized.