Which keyword is necessary to use when querying with a vector index?

Utilizing the keyword "APPROX" in queries involving a vector index is essential because it enables the retrieval of results that may not exactly match the search criteria but are similar enough based on the vector representations of the data. This is particularly important in vector searches, which are designed to handle high-dimensional data and focus on retrieving relevant results efficiently rather than requiring an exact match for the query.

The nature of vector search involves finding items in a large dataset that are closest to the specified vector, which naturally leads to the need for an approximation. This enhances performance and allows the algorithm to return results based on similarity rather than strict equivalence, which is critical in many machine learning and AI applications. Thus, using "APPROX" allows for a more flexible and efficient search that aligns with the objectives of vector indexing.