Building a robust question-answering assistant involves dynamically retrieving relevant information tailored to each query, necessitating various retrieval methods depending on the question's nature. LlamaCloud introduces file-level retrieval, a separate API from the existing chunk-level retrieval, to handle questions requiring extensive context, such as summarizing entire documents. This approach involves two main retrieval methods: by metadata and by content, allowing seamless toggling between them. A Jupyter notebook demonstration showcases building an agent that intelligently chooses between chunk-level and file-level retrieval based on the query, enhancing the system's capability to adapt to different user needs. By integrating these dynamic retrieval capabilities, LlamaCloud aims to create more context-aware and accurate large language model applications, encouraging developers to explore these features through their user interface and example-rich repository.