DeepRAG is an adaptive retrieval-augmented generation system that addresses the limitations of traditional large language models (LLMs) by combining LLMs with external knowledge sources like databases or search engines. It breaks down complex questions into smaller subqueries and decides at each stage whether to rely on internal knowledge or fetch external data, reducing wasted searches and improving answer accuracy. DeepRAG's adaptive process mirrors how humans approach complex questions, using a retrieval narrative that follows a logical sequence of subqueries to gradually form a complete answer. It integrates Markov Decision Process (MDP) modeling, binary tree search, imitation learning, and chain of calibration to balance efficiency and accuracy in answering questions. By integrating with vector databases like Milvus and Zilliz Cloud, DeepRAG can further enhance its retrieval capabilities, making it well-suited for real-world applications where efficient and accurate information retrieval is critical. Future research directions include multimodal retrieval integration, context-aware retrieval decisions, and real-time data retrieval.