Interaction Loop

This section explains how to create an interaction loop in Colang 2.0.

Usage

In various LLM-based application, there is a need for the LLM to keep interacting with the user in a continuous interaction loop. The example below shows how a simple interaction loop can be implemented using the while construct and how the bot can be proactive when the user is silent.

examples/v2_x/tutorial/interaction_loop/main.co

import core
import llm
import avatars
import timing

flow main
  activate automating intent detection
  activate generating user intent for unhandled user utterance

  while True
    when unhandled user intent
      $response = ..."Response to what user said."
      bot say $response
    or when user was silent 12.0
      bot inform about service
    or when user expressed greeting
      bot say "Hi there!"
    or when user expressed goodbye
      bot inform "That was fun. Goodbye"

flow user expressed greeting
  user said "hi"
    or user said "hello"

flow user expressed goodbye
  user said "goodbye"
    or user said "I am done"
    or user said "I have to go"

flow bot inform about service
  bot say "You can ask me anything!"
    or bot say "Just ask me something!"

The main flow above activates the generating user intent for unhandled user utterance flow from the avatars module which uses the LLM to generate the canonical form for a user message (a.k.a., the user intent). Also, when the LLM generates an intent that is not handled by the Colang script, the unhandled user intent flow is triggered (line 11).

Line 14 in the example above shows how to use the pre-defined user silent event to model time-driven interaction.

This example also uses the when / or when syntax, which is a mechanism for branching a flow on multiple paths. When a flow reaches a branching point, it will start monitoring all the branches and continue the interaction as soon as a branch is matched.

Testing

$ nemoguardrails chat --config=examples/v2_x/tutorial/interaction_loop

> hi

Hi there!

<< pause for 12 seconds >>

You can ask me anything!

> how are you?

I am doing well, thank you for asking! How can I assist you today?

The next example will show you how to create LLM-driven flows.