Amazon Bedrock supports multi-turn conversational applications by enabling developers to integrate conversation history into each API request sent to its foundation models. When a user interacts with a chatbot, the application can pass the full history of the conversation (user inputs and prior model responses) as part of the prompt. The model processes this context to generate relevant, coherent replies that account for previous interactions. For example, if a user asks, “What’s the weather in Seattle?” and follows up with “Will it rain tomorrow?”, the model uses the stored context to infer that “tomorrow” refers to Seattle. Bedrock’s models are designed to handle such sequential dependencies, allowing developers to build applications that mimic natural, context-aware dialogues.
To implement this, developers structure their code to maintain and update the conversation state. For instance, a chat application might store each exchange as a list of message objects (e.g., [{role: "user", content: "..."}, {role: "assistant", content: "..."}]). With each new user input, the app appends the message to the list and sends the entire history to Bedrock via the API. The model then generates a response based on the accumulated context. However, since models have token limits (e.g., Claude’s 100K token context window), developers may need to truncate older messages or summarize past interactions to stay within bounds. For example, after 10 exchanges, the app could remove the earliest messages or use a separate prompt to condense the conversation history into a shorter summary before feeding it back into the model.
Bedrock’s flexibility allows developers to combine it with AWS services for end-to-end solutions. For example, an app could use Amazon DynamoDB to store conversation histories tied to user sessions, ensuring context persists across multiple interactions. AWS Lambda functions could handle the logic of retrieving the history, invoking Bedrock’s API, and updating the database. Additionally, Bedrock’s support for multiple models (e.g., Anthropic Claude, Amazon Titan) lets developers choose the best model for specific tasks. For instance, a customer support bot might use Claude for complex reasoning and Titan for concise answers, switching models mid-conversation based on the user’s needs. This combination of state management, scalability, and model choice simplifies building chatbots that maintain coherent, long-term context.