Vector search plays a critical role in generative AI by enabling efficient retrieval of relevant information from large datasets. Generative AI models, such as those used for text or image generation, often require context or reference data to produce accurate outputs. Vector search helps by quickly identifying data points similar to a given query, allowing the generative model to focus on the most pertinent information. This is especially useful when working with unstructured data like text, images, or audio, where traditional keyword-based search methods fall short.
At a technical level, vector search relies on embeddings—numerical representations of data that capture semantic meaning. For example, a sentence or image is converted into a high-dimensional vector using a machine learning model. When a user submits a query, the system generates an embedding for that query and searches a precomputed database of embeddings to find the closest matches. This process uses similarity metrics like cosine similarity or Euclidean distance. For instance, a generative AI chatbot might use vector search to retrieve relevant snippets from a knowledge base before generating a response, ensuring the output is factually grounded.
A practical example is in retrieval-augmented generation (RAG), where vector search and generative AI work together. Suppose a developer builds a question-answering system. The system first converts a user’s question into a vector and searches a database of document embeddings to find relevant passages. The generative model then uses those passages to construct a coherent answer. Without vector search, the model might hallucinate or miss key details. Vector search also scales well, making it feasible to handle large datasets in real-time applications like recommendation systems or content moderation tools. By narrowing the scope of data the generative model processes, it improves both efficiency and accuracy.