Vector search indexes data by transforming it into a mathematical representation known as vectors. This process begins with the conversion of text, images, or other unstructured data into numerical vectors through machine learning models. These models, often based on neural networks, generate embeddings that capture the semantic meaning of the input data. The resulting vectors are stored in a high-dimensional space, where each dimension represents a feature or aspect of the data.
Once the data is converted into vectors, it is indexed using specialized algorithms designed to efficiently search through high-dimensional spaces. One popular algorithm is the Hierarchical Navigable Small World (HNSW), which organizes the vectors into a graph structure to facilitate quick retrieval. This indexing method allows for approximate nearest neighbors search, which is crucial for finding semantically similar items without incurring high computational costs.
By indexing data as vectors, vector search enables a more nuanced search experience compared to traditional keyword-based search. It allows for similarity search, where queries can retrieve results based on semantic similarities rather than exact keyword matches. This approach is particularly beneficial in scenarios where users seek information that is contextually relevant rather than textually identical.