A support corpus contains exact product codes and conceptual descriptions. The team wants both lexical precision and semantic similarity. Which retrieval approach should it evaluate?

1. Disable the index and ask the model to guess private product codes
2. Random document selection
3. Vector similarity alone as a guarantee of exact-code matching
4. Hybrid search combining keyword and vector signals