Vector Database & Retrieval

New generation information systems are progressively moving out of the area of keyword matching and more into the vector database and semantic search domain. A vector database is essentially a purpose-built storage system for handling high-dimensional data points, called embeddings. These embeddings are machine learning models’ numerical encodings of unstructured information (e.g., text, images, audio) intended to encapsulate the meaning and context of the data, by translating these inputs into a multi-dimensional mathematical space, like concepts are positioned closer to one another so computers can “understand” that when you search for “canine,” don’t return results containing the word, dog.

Typically, the process of retrieval is started when a user issues a query, then transformed to its own vector representation by applying the same embedding model to it. Additionally, the database does a similarity search to find the nearest neighbors within the vector space. Since it is infeasible to test a query against billions of data points due to performance reasons, vector databases rely on advanced indexing methods such as Hierarchical Navigable Small World (HNSW) or Product Quantization. These algorithms enable fast Approximate Nearest Neighbor (ANN) searches, sacrificing a small amount of accuracy for huge speedups, allowing milliseconds query time on petabyte-scale datasets.

It is inspired by this technology on which Retrieval-Augmented Generation (RAG) builds, where Large Language Models (LLMs) with vector databases act as external memory to access recent or private information. More than just AI chatbots, these systems fuel the recommendation engines of today, image search tools and anomaly detectors. When coupled with traditional metadata filtering, these new semantic capabilities create a flexible and dynamic architecture that is ready for the next wave of context-aware applications.