Python Classes for Messages

Overview

This module provides several classes to represent scripted interactions between participants in a conversation. The key classes are Message, SimpleMessage, Observation, ScriptBackground, ScriptEnvironmentResponse, AgentAction, and ScriptInteraction.

Classes

`Message`

An interface for messages. There is only one required method:

Methods

to_natural_language(self) -> str: Raises NotImplementedError.

`SimpleMessage`

A simple message with a single string field.

Attributes

message: str: The message content.

Methods

to_natural_language(self) -> str: Returns the message as a string.

`Observation`

Represents an observation in a conversation.

Attributes

last_turn: str: The last turn of the conversation.
turn_number: int: The turn number of the conversation.
available_actions: list[ActionType]: The available actions.

Methods

to_natural_language(self) -> str: Returns a string describing the conversation turn.

`ScriptBackground`

Contains the background information for a scripted episode.

Attributes

scenario: str: The scenario of the episode.
p1_name: str: The name of participant 1.
p2_name: str: The name of participant 2.
p1_background: str: The background of participant 1.
p2_background: str: The background of participant 2.
p1_goal: str: The goal of participant 1.
p2_goal: str: The goal of participant 2.

Methods

to_natural_language(self) -> str: Returns a string describing the episode context.

`ScriptEnvironmentResponse`

Represents the environment's response to the interaction.

Attributes

terminated: bool: Whether the conversation is terminated.
p1_rate: float | tuple[float, dict[str, float]] | None: Rating of participant 1.
p2_rate: float | tuple[float, dict[str, float]] | None: Rating of participant 2.
comments: str | None: Supporting comments for the termination and rating.

Methods

to_natural_language(self) -> str: Returns a string describing the environment's response.

`AgentAction`

Represents an action taken by an agent.

Attributes

action_type: ActionType: The type of action.
argument: str: The argument associated with the action.

Methods

to_natural_language(self) -> str: Returns a string describing the agent's action.

`ScriptInteraction`

Represents the entire interaction between participants.

Attributes

interactions: str: The script of the interaction.

Methods

to_natural_language(self) -> str: Returns the raw interaction script.
parse(self, agent_names: list[str], background: str) -> tuple[list[list[tuple[str, str, Message]]], list[tuple[str, Message]]]: Parses the interaction script.
parse_single_dialogue(self, dialogue: str) -> dict[str, str | int | AgentAction | None]: Parses a single dialogue line.
split_by_turn(self, input_string: str) -> list[str]: Splits the input script by turns.
default_value_for_return_type() -> ScriptInteractionReturnType: Provides a default return type value.

Usage Example

# Create instances of different message types
simple_msg = SimpleMessage(message="Hello, world!")
print(simple_msg.to_natural_language())
 
observation = Observation(
    last_turn="Hi there!",
    turn_number=1,
    available_actions=["speak", "none"]
)
print(observation.to_natural_language())
 
script_background = ScriptBackground(
    scenario="Business Meeting",
    p1_name="Alice",
    p2_name="Bob",
    p1_background="Engineer",
    p2_background="Manager",
    p1_goal="Discuss project",
    p2_goal="Approve budget"
)
print(script_background.to_natural_language())
 
response = ScriptEnvironmentResponse(
    terminated=True,
    p1_rate=9.5,
    p2_rate=7.0,
    comments="A lively discussion."
)
print(response.to_natural_language())
 
action = AgentAction(action_type="speak", argument="Hello, how can I help you?")
print(action.to_natural_language())
 
interaction_script = """
Turn #1
Alice said: "Hello, Bob!"
 
Turn #2
Bob said: "Hi, Alice! How's the project going?"
"""
script_interaction = ScriptInteraction(interactions=interaction_script)
print(script_interaction.to_natural_language())

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