The guide further explains how to connect these goals, set the initial goal, and test the AI assistant. It also provides information on how to use different models and parameters with GoalChain, and how to handle out-of-scope queries and validate inputs. The article concludes with a live example of a conversation with the AI assistant, demonstrating how it handles order confirmation, order cancellation, and order quantity validation. It also shows how to use the data collected by the AI assistant to perform actions, such as processing an order.
Key takeaways:
- GoalChain is a framework that enables goal-oriented conversation flows for human-LLM and LLM-LLM interaction, useful for creating AI assistants.
- It allows the creation of 'Goals' with defined 'Fields' that need to be filled, and these goals can be connected to create a conversation flow. Validators can be used to ensure the input data is valid.
- GoalChain supports the use of different models for each goal, and parameters can be passed to customize the model's behavior. It also allows for the simulation of responses and rephrasing for a more natural interaction.
- The framework provides flexibility in handling out-of-scope queries, order cancellations, and validation of inputs, making it suitable for complex conversation scenarios.