Vector search has diverse use cases across industries where unstructured data and similarity-based retrieval are critical. One prominent application is in search engines that require semantic understanding of queries to deliver accurate results. For example, in legal or academic fields, vector search helps retrieve contextually relevant documents, even for complex or imprecise queries.
Another common use case is in recommendation systems, where user preferences and item attributes are encoded as vectors. By computing similarity, the system can suggest products, media, or services that align with user interests. For instance, an e-commerce site might recommend items based on browsing history, while a streaming platform suggests shows or movies based on past viewing patterns.
In healthcare, vector search aids in tasks like medical image analysis and drug discovery. Comparing vectors of medical images helps identify patterns for diagnosing conditions, while searching molecular structure vectors accelerates drug candidate identification. Similarly, in fraud detection, vector search analyzes transaction patterns to spot anomalies. These applications demonstrate vector search's versatility in handling high-dimensional, unstructured data.