A Generative AI Engineer built a RAG application on Databricks. They embedded a corpus of 50,000 product manual chunks using the 'bge-large-en' embedding model and stored the vectors in a Databricks Vector Search index. During testing, retrieval quality is poor even for queries that clearly match documents in the corpus. On inspection, the engineer discovers that the query embeddings at search time are being generated by a different model ('all-MiniLM-L6-v2') than the one used to build the index. Which action correctly resolves the root cause of the poor retrieval?