1. Managing AI conversations
Hello! Welcome to this video on designing conversational AI applications.
2. Conversation state management in Bedrock
When building conversational AI applications with Bedrock, managing conversation state is fundamental to creating natural, context-aware interactions. A conversation isn't just about the current message, but rather a flow of information that includes previous exchanges and important metadata like user preferences or session details - it's context. The Claude models are particularly good at handling this context, when we provide it - they can understand and reference earlier parts of the conversation, making interactions feel more coherent and natural. Think of it like maintaining a memory of the conversation, allowing the AI to respond with full awareness of what's been discussed.
3. Implementing conversation management
Let's look at how we implement conversation management in practice. We've created a ConversationManager class that handles the core functionality of our conversational application. The class initializes a Bedrock client and maintains a conversation history. Each message is stored with its role - either 'user' or 'assistant' - and its content. This structure allows us to maintain a clear record of the conversation flow and ensures that the model can understand the context of each interaction. This modular structure ensures that our system remains scalable and adaptable as conversations grow in complexity.
4. Using ConversationManager in practice
Now, let's see the 'ConversationManager' class in action.
First, we create an instance of the class and add a user message to the conversation.
By adding this message, we ensure the user's input is logged and can be referenced when generating a response. Tracking the conversation history like this helps our model maintain context and provide more relevant answers.
5. Using ConversationManager in practice
Next, we prepare the conversation history to send to the model as a message. Since LLMs have token limits, we need to manage the amount of text we include.
Here, we limit the conversation history to the two most recent messages. This helps us stay within the model's context limit while still providing enough information for meaningful responses.
Finally, we send this formatted conversation history to the model.
This approach ensures the response stays relevant without exceeding length limits. This combination of tracking, limiting, and formatting the conversation history improves response quality and makes our solution practical for real-world use.
6. Let's practice!
Let's practice conversations with some exercises!