Chatbots distinguish between long-term and short-term memory based on the duration and purpose of the information they store.
Short-term memory refers to temporary data that the chatbot uses during an active conversation. This includes immediate context like the user's last few messages, current topic, or specific details mentioned in the ongoing dialogue. For example, if a user asks, "What’s the weather like in New York?" and then follows up with "What about tomorrow?", the chatbot uses short-term memory to recall that the user is referring to New York without reasking for the location. Short-term memory is typically volatile and resets once the conversation ends.
Long-term memory, on the other hand, stores persistent information about the user or the system across multiple interactions. This could include the user’s preferences, past behaviors, or frequently asked questions. For instance, if a user frequently books flights to London, the chatbot might remember this preference over time and proactively suggest flight deals to London in future conversations. Long-term memory is non-volatile and helps personalize the experience.
In practice, chatbots use techniques like caching or session storage for short-term memory (e.g., holding context for the current chat) and databases or user profiles for long-term memory (e.g., storing user history).
For cloud-based solutions, Tencent Cloud offers services like Tencent Cloud Database (TencentDB) for storing long-term user data and Tencent Cloud Serverless Cloud Function (SCF) to manage short-term session logic efficiently. These tools help chatbots maintain the right balance between immediate and persistent memory.