The Recursive Abstractive Processing for Tree-Organized Retrieval (RAPTOR) framework deploys a hierarchical tree-structured datastore to integrate local and global context, enabling efficient handling of long documents for language models. This design is especially useful when cloud-based language models are unavailable or undesirable. For instance, with offline confidential patient records or stringent data-privacy requirements. We benchmarked RAPTOR on the QuALITY dataset and a novel Clinical Trial question-answering dataset (CTQA) drawn from over 500 000 registry entries. Experiments varied question complexity (simple vs. complex), four language models, four embedding models, and three chunking strategies. Also incorporated GPT-4o as a cloud-based baseline. Results show that, with optimal settings, RAPTOR combined with smaller local models outperforms GPT-4o on complex CTQA questions, although this gain does not extend to QuALITY. These outcomes highlight RAPTOR’s promise as a practical, locally implementable solution for long-context understanding.