How to Efficiently Handle Long Chat Histories in a RAG-Based Chatbot?

I have developed an AI chatbot using a RAG pipeline and deployed it. To maintain chat history continuity, I store all questions and answers in a Redis chat store. When a user asks a new question, I retrieve the entire history from Redis and include it in the context. However, as the chat history grows, the prompt length increases significantly. Is this the right approach, or is there a more efficient way to handle it?