Vector search systems typically rely on fixed indexing methods and standard similarity measures, such as Euclidean distance, assuming a universal notion of semantic similarity. This assumption overlooks the subjective and context-dependent nature of human similarity perception. Prior approaches proposed personalised querying via Mahalanobis distance parameterised by learned user-profile matrices, but did not address strategies for acquiring real user relevance feedback. We present an end-to-end pipeline designed to integrate semantic feedback directly from user interactions. We explore three feedback strategies, evaluating them across ten established metric learning models. Through an empirical analysis, we assess how these strategies affect key performance indicators, such as retrieval precision. We also outline three practical operational scenarios – balancing precision and computational costs, prioritising computational efficiency, and optimising for precision – and provide concrete model and system configuration recommendations. Experiments show that the recommended setups make search efficient while improving retrieval precision, showing the practicality of the approach.