What is Vector Search?
FAQs
Vector search has a wide range of use cases across different industries, from improving search accuracy in e-commerce with product recommendations to powering semantic search in content discovery and enhancing image search capabilities.
Unlike traditional keyword search, which relies on matches where two words or phrases share a significant degree of similarity in their spelling or structure, vector search understands the semantic similarity between the query and the content, allowing it to return more relevant and contextually related results even if the exact keywords are absent.
Yes, vector search excels in dealing with unstructured data by converting it into numerical representations that can be quickly processed and analyzed, making it particularly useful for handling natural language text and images.
Machine learning is fundamental to vector search, from creating vector representations of data to employing algorithms like approximate nearest neighbor (ANN) for efficient search and retrieval.