chatbot

To build a multi-turn chatbot, you create and manage sessions that maintain conversation history within a configurable context window. This enables continuous dialogue by tracking previous messages and system prompts across interactions.

Session lifecycle management in a multi-turn chatbot involves creating and managing multiple distinct conversations with system prompts and metadata. It ensures context and message history are preserved within configurable windows for each individual session.

You can integrate RAG into a customer-support chatbot using optional knowledge augmentation features. This allows the bot to retrieve external knowledge to answer inquiries accurately and escalate to human agents when needed.

Yes, you can use agent coordination to delegate tasks by calling Coordinators and SubAgents within configurable chatbot sessions. This allows the multi-turn chatbot to orchestrate complex workflows and handle specialized inquiries.

No specific LLM provider dependencies are required. The skill supports provider-agnostic LLM integration, allowing you to connect various language models for dialogue management without being locked into a single vendor.

Configurable context windows limit dialogue management by only retaining a set number of recent messages in session history. This prevents indefinite memory growth but means older conversation context is eventually dropped from the chatbot's active scope.