In a chatbot application how can I improve context handling when users switch between topics without losing previous conversation data

Question

With the help of proper code example can you tell me In a chatbot application, how can I improve context handling when users switch between topics without losing previous conversation data?

Answer 1

You can use memory-based context management with session storage and embeddings to track user topics and retrieve relevant past interactions dynamically.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Session Memory Storage (session_memory) – Maintains user-specific conversation history.
• Dynamic Context Retention – Truncates and stores only the latest relevant exchanges.
• User-Specific Context Handling – Each user has a separate conversational memory.
• Seamless Context Awareness – The model generates responses based on prior messages.
• Scalability and Efficiency – Keeps history length manageable to optimize performance.

Hence, implementing memory-based context management ensures smooth topic transitions and maintains meaningful conversations without losing prior context.