A vector in the context of vector search is a numerical representation of data, such as text, images, or user behavior, transformed into a sequence of numbers. These numbers capture the essential features or attributes of the data in a multi-dimensional space. For example, a sentence might be converted into a 300-dimensional vector where each number represents a specific semantic or syntactic feature. This allows algorithms to compare vectors mathematically, measuring similarity using metrics like cosine similarity or Euclidean distance. The core idea is that items with similar characteristics will have vectors that are closer together in this mathematical space.
Vector search relies on embedding models to generate these numerical representations. Embedding models, such as word2vec for text or convolutional neural networks (CNNs) for images, process raw data and output vectors that preserve meaningful relationships. For instance, in natural language processing, the word “king” might be embedded as a vector close to “queen” but farther from “apple.” Similarly, an image of a cat would have a vector distinct from one of a car. These models are trained to ensure that semantic or visual similarities translate into geometric proximity in the vector space. Once data is embedded, vector search engines use indexing techniques like approximate nearest neighbor (ANN) algorithms to efficiently find similar vectors without exhaustively comparing every entry in a database.
Developers implementing vector search must consider factors like dimensionality, indexing strategies, and model choice. High-dimensional vectors (e.g., 1,000 dimensions) can capture fine-grained details but may require more computational resources. Libraries like FAISS, Annoy, or HNSW optimize vector search by partitioning data into clusters or building hierarchical graphs to speed up queries. For example, a recommendation system might use FAISS to index user preference vectors and quickly find products with similar vectors. Practical challenges include balancing search speed with accuracy—approximate methods trade some precision for faster results—and selecting embedding models that align with the data type (e.g., BERT for text, ResNet for images). Proper normalization of vectors and choice of distance metric also significantly impact performance, making experimentation critical for tuning vector search systems.