In LORS, users can ingest collections of documents, preprocess texts into embeddings, and store them in a vector database. When a query or summarization task is issued, LORS performs semantic retrieval to identify the most relevant text segments. It then feeds these segments into a large language model to produce concise, context-aware summaries. The modular design allows swapping embedding models, adjusting retrieval thresholds, and customizing prompt templates. LORS supports multi-document summarization, interactive query refinement, and batching for high-volume workloads, making it ideal for academic literature reviews, corporate reporting, or any scenario requiring rapid insight extraction from massive text corpora.