Beyond Embeddings: Why Code Search Needs Semantic and Lexical Fusion

Semantic similarity alone fails for code repositories because operationally distinct functions (like create_user, update_user, delete_user) can cluster together despite having completely different purposes. KernelMind solves this by combining embeddings with BM25 lexical search through Reciprocal Rank Fusion, achieving both semantic understanding and exact keyword matching. This hybrid approach is critical for any code-aware AI system aiming to reliably retrieve and understand the right code from large repositories.