Used with large language models, RAG retrieves relevant information from a vector database to augment an LLM’s input, improving response accuracy, enabling organizations to safely leverage their own data with commercial LLMs, and reducing hallucinations. This enables developers to build more accurate, flexible, and context-aware AI applications, while offering a level of security, privacy, and governance when safeguards such as encryption and role-based access control are used with the database system.
Supporting AI at scale
Driven by the growing importance of vector search and similarity matching in AI applications, many traditional database vendors are adding vector search capabilities to their offerings. However, whether you’re building a recommendation engine or an image search platform, speed matters. Vector databases are optimized for real-time retrieval, allowing applications to provide instant recommendations, content suggestions, or search results. This capability goes beyond the typical strengths of databases — even with vector capabilities added on.
Some vector databases also are built to scale horizontally, which makes them capable of managing enormous collections of vectors distributed across multiple nodes. This scalability is essential for AI-driven applications, where vectors are generated at an enormous scale (for example, embeddings from deep learning models). With distributed searching capabilities, vector databases can handle large datasets just like search engines, ensuring low-latency retrieval even in massive, enterprise-scale environments.
