Chatbots handle multi-turn conversation context by maintaining and utilizing information from previous interactions within the same session to ensure coherent and relevant responses. This is achieved through several techniques:
Context Storage: The chatbot stores key details (e.g., user preferences, previous questions, or entities mentioned) in a temporary memory structure (like a session variable or context object) during the conversation. For example, if a user asks, "What’s the weather in Paris?" and later follows up with "How about tomorrow?", the bot recalls "Paris" from the earlier turn.
Session-Based Memory: Many chatbots use session-based memory to track context within a single user interaction thread. This memory is cleared when the session ends (e.g., after inactivity or a timeout). For instance, a customer support bot might remember a user’s ticket ID across multiple questions to provide updates.
Dialogue Management Models: Advanced chatbots employ dialogue management systems (e.g., rule-based logic or machine learning models like RNNs, Transformers) to interpret the flow of conversation. These models predict the next response based on the entire dialogue history. For example, a virtual assistant might handle a multi-step task like booking a flight by tracking user inputs (destination, dates, preferences) across turns.
Explicit Context Passing: In some architectures, the chatbot explicitly passes context between components (e.g., intent classifiers and response generators). For example, if a user asks, "Tell me about Tesla’s stock," and then says "What about Apple?", the bot infers "Apple" refers to its stock based on the prior financial discussion.
Example: A food delivery chatbot interacts with a user as follows:
Cloud Services: For scalable and reliable context handling, cloud platforms like Tencent Cloud offer services such as Tencent Cloud Chatbot (powered by AI models) and Tencent Cloud Serverless Cloud Function to manage session states and dialogue flows efficiently. These tools help developers build chatbots that maintain context seamlessly across interactions.