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intelligent memory retrieval architecture that enables large language models (LLMs) to maintain long-term contextual understanding and deliver faster, more accurate responses. By dynamically embedding and ranking past interactions and external data based on relevance, recency, and confidence, it ensures the model always works with the most meaningful context. The design continuously updates and summarizes chat history, adapting to user behavior and question type, which significantly reduces retrieval costs while improving personalization and consistency.