Retrieval-Augmented Generation (RAG) systems combine large language models with retrieval mechanisms to generate contextually relevant answers. However, while vector similarity effectively retrieves geometrically close documents, it often lacks the semantic depth required to fully address user queries. This paper investigates the gap between vector similarity and semantic relevance through mathematical formulations and real-world examples. We propose SemantriX, an explainable hybrid retrieval strategy that integrates metadata enrichment and cross-encoder-based reranking. The model is evaluated within a question-answering system applied to the domain of contract management. Experimental results show significant improvements in precision, recall, and F1-score. By aligning retrieval with semantic relevance, our approach enhances the performance and explainability of RAG systems in real-world decision-support scenarios.