`SummaryIndex` is a powerful tool in LlamaIndex for handling global questions. Its core mechanism is to **pass the complete document to the large model as context, leveraging the model's comprehension ability to generate a global summary or answer**. With sensible parameter configuration and a streamlined process, you can get high-quality global answers. In practice, it's a good idea to combine `VectorStoreIndex` and `SummaryIndex` and automatically pick the right query engine based on the type of question, so users get more comprehensive and accurate answers. This dual-engine architecture makes the most of both index types: it can handle specific local questions as well as questions that require reading the whole document to answer, providing solid support for building a high-quality Q&A system.