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Résumé : In recent years, large language models (LLMs) have demonstrated remarkable progress in natural language understanding and generation. However, they continue to face significant challenges, particularly in handling time-sensitive queries and mitigating hallucination issues. The Retrieval-Augmented Generation (RAG) framework has emerged as a promising solution to these challenges by integrating external knowledge retrieval into the generation process. Nevertheless, existing RAG approaches exhibit a critical limitation: they over-rely on the original user query during retrieval, often failing to account for query ambiguity, complexity, and contextual nuances. This suboptimal alignment between retrieved content and user intent ultimately compromises response quality. To address these limitations, we propose Reasoning Query Optimize, a novel logic driven adaptive query optimization framework. This method dynamically analyzes the logical structure of user queries and performs targeted optimizations to mitigate their inherent ambiguities and complexities, thereby enhancing the retrieval precision, comprehension robustness, and response quality of LLMaugmented systems.