Vector search is primarily designed for handling unstructured data, such as text, images, and audio, where traditional search methods may fall short. However, it can also be applied to structured data, albeit with some considerations. Structured data is typically organized in a tabular format, with clear relationships between data points, making traditional database queries efficient. However, when the goal is to find semantically similar data points or to perform similarity searches, vector search can be beneficial.
In structured data scenarios, vector search can be used to enhance the search experience by creating embeddings for the data, which represent the semantic meaning of data points in a high-dimensional vector space. This allows for the discovery of patterns and relationships that might not be apparent through traditional keyword search methods. For example, in a retail database, vector search can identify similar products based on customer reviews or descriptions, even if they don't share the same keywords.
The challenge with using vector search for structured data lies in the computational cost and complexity of generating and maintaining vector embeddings. It is crucial to balance the benefits of enhanced search results with the resources required to implement and maintain a vector-based system. Additionally, vector search may need to be integrated with traditional search methods in a hybrid search approach to ensure accurate and efficient retrieval of both structured and unstructured data.