FSM - User can input speaker transition constraints#

%%capture --no-stderr
%pip install pyautogen[openai,graph]>=0.2.11

import random  # noqa E402

import matplotlib.pyplot as plt
import networkx as nx

import autogen
from autogen.agentchat.conversable_agent import ConversableAgent
from autogen.agentchat.assistant_agent import AssistantAgent
from autogen.agentchat.groupchat import GroupChat
from autogen.graph_utils import visualize_speaker_transitions_dict

print(autogen.__version__)

config_list_gpt4 = {
    "timeout": 600,
    "cache_seed": 44,  # change the seed for different trials
    "config_list": autogen.config_list_from_json(
        "OAI_CONFIG_LIST",
        filter_dict={"tags": ["gpt-4", "gpt-4-32k"]},  # comment out to get all
    ),
    "temperature": 0,
}

agents = [ConversableAgent(name=f"Agent{i}", llm_config=False) for i in range(5)]
allowed_speaker_transitions_dict = {agent: [other_agent for other_agent in agents] for agent in agents}

visualize_speaker_transitions_dict(allowed_speaker_transitions_dict, agents)

agents = [ConversableAgent(name=f"Agent{i}", llm_config=False) for i in range(5)]
allowed_speaker_transitions_dict = {
    agents[0]: [agents[1], agents[2], agents[3], agents[4]],
    agents[1]: [agents[0]],
    agents[2]: [agents[0]],
    agents[3]: [agents[0]],
    agents[4]: [agents[0]],
}

visualize_speaker_transitions_dict(allowed_speaker_transitions_dict, agents)

# Sequential Team Operations
# Create an empty directed graph

speaker_transitions_dict = {}
teams = ["A", "B", "C"]
team_size = 5

def get_agent_of_name(agents, name) -> ConversableAgent:
    for agent in agents:
        if agent.name == name:
            return agent

# Create a list of 15 agents 3 teams x 5 agents
agents = [ConversableAgent(name=f"{team}{i}", llm_config=False) for team in teams for i in range(team_size)]

# Loop through each team and add members and their connections
for team in teams:
    for i in range(team_size):
        member = f"{team}{i}"
        # Connect each member to other members of the same team
        speaker_transitions_dict[get_agent_of_name(agents, member)] = [
            get_agent_of_name(agents, name=f"{team}{j}") for j in range(team_size) if j != i
        ]

# Team leaders connection
print(get_agent_of_name(agents, name="B0"))
speaker_transitions_dict[get_agent_of_name(agents, "A0")].append(get_agent_of_name(agents, name="B0"))
speaker_transitions_dict[get_agent_of_name(agents, "B0")].append(get_agent_of_name(agents, name="C0"))

visualize_speaker_transitions_dict(speaker_transitions_dict, agents)

agents = [ConversableAgent(name=f"Agent{i}", llm_config=False) for i in range(2)]
allowed_speaker_transitions_dict = {
    agents[0]: [agents[0], agents[1]],
    agents[1]: [agents[0], agents[1]],
}

visualize_speaker_transitions_dict(allowed_speaker_transitions_dict, agents)

# Create an empty directed graph
agents = []
speaker_transitions_dict = {}
secret_values = {}

# Outer loop for prefixes 'A', 'B', 'C'
for prefix in ["A", "B", "C"]:
    # Add 3 nodes with each prefix to the graph using a for loop
    for i in range(3):
        node_id = f"{prefix}{i}"
        secret_value = random.randint(1, 5)  # Generate a random secret value
        secret_values[node_id] = secret_value

        # Create an AssistantAgent for each node (assuming AssistantAgent is a defined class)
        agents.append(
            AssistantAgent(
                name=node_id,
                system_message=f"""Your name is {node_id}.
                                          Do not respond as the speaker named in the NEXT tag if your name is not in the NEXT tag. Instead, suggest a relevant team leader to handle the mis-tag, with the NEXT: tag.

                                          You have {secret_value} chocolates.

                                          The list of players are [A0, A1, A2, B0, B1, B2, C0, C1, C2].

                                            Your first character of your name is your team, and your second character denotes that you are a team leader if it is 0.
                                            CONSTRAINTS: Team members can only talk within the team, whilst team leader can talk to team leaders of other teams but not team members of other teams.

                                            You can use NEXT: to suggest the next speaker. You have to respect the CONSTRAINTS, and can only suggest one player from the list of players, i.e., do not suggest A3 because A3 is not from the list of players.
                                            Team leaders must make sure that they know the sum of the individual chocolate count of all three players in their own team, i.e., A0 is responsible for team A only.

                                          Keep track of the player's tally using a JSON format so that others can check the total tally. Use
                                          A0:?, A1:?, A2:?,
                                          B0:?, B1:?, B2:?,
                                          C0:?, C1:?, C2:?

                                          If you are the team leader, you should aggregate your team's total chocolate count to cooperate.
                                          Once the team leader know their team's tally, they can suggest another team leader for them to find their team tally, because we need all three team tallys to succeed.
                                          Use NEXT: to suggest the next speaker, e.g., NEXT: A0.

                                          Once we have the total tally from all nine players, sum up all three teams' tally, then terminate the discussion using TERMINATE.

                                          """,
                llm_config=config_list_gpt4,
            )
        )
        speaker_transitions_dict[agents[-1]] = []

    # Add edges between nodes with the same prefix using a nested for loop
    for source_node in range(3):
        source_id = f"{prefix}{source_node}"
        for target_node in range(3):
            target_id = f"{prefix}{target_node}"
            if source_node != target_node:  # To avoid self-loops
                speaker_transitions_dict[get_agent_of_name(agents, source_id)].append(
                    get_agent_of_name(agents, name=target_id)
                )

# Adding edges between teams
speaker_transitions_dict[get_agent_of_name(agents, "A0")].append(get_agent_of_name(agents, name="B0"))
speaker_transitions_dict[get_agent_of_name(agents, "A0")].append(get_agent_of_name(agents, name="C0"))
speaker_transitions_dict[get_agent_of_name(agents, "B0")].append(get_agent_of_name(agents, name="A0"))
speaker_transitions_dict[get_agent_of_name(agents, "B0")].append(get_agent_of_name(agents, name="C0"))
speaker_transitions_dict[get_agent_of_name(agents, "C0")].append(get_agent_of_name(agents, name="A0"))
speaker_transitions_dict[get_agent_of_name(agents, "C0")].append(get_agent_of_name(agents, name="B0"))

# Visualization only
graph = nx.DiGraph()

# Add nodes
graph.add_nodes_from([agent.name for agent in agents])

# Add edges
for key, value in speaker_transitions_dict.items():
    for agent in value:
        graph.add_edge(key.name, agent.name)

# Visualize
# Draw the graph with secret values annotated
plt.figure(figsize=(12, 10))
pos = nx.spring_layout(graph)  # positions for all nodes

# Draw nodes with their colors
nx.draw(graph, pos, with_labels=True, font_weight="bold")

# Annotate secret values
for node, (x, y) in pos.items():
    secret_value = secret_values[node]
    plt.text(x, y + 0.1, s=f"Secret: {secret_value}", horizontalalignment="center")

plt.show()

# Termination message detection

def is_termination_msg(content) -> bool:
    have_content = content.get("content", None) is not None
    return have_content and "TERMINATE" in content["content"]

# Terminates the conversation when TERMINATE is detected.
user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    system_message="Terminator admin.",
    code_execution_config=False,
    is_termination_msg=is_termination_msg,
    human_input_mode="NEVER",
)

agents.append(user_proxy)

group_chat = GroupChat(
    agents=agents,
    messages=[],
    max_round=20,
    allowed_or_disallowed_speaker_transitions=speaker_transitions_dict,
    speaker_transitions_type="allowed",
)

# Create the manager
manager = autogen.GroupChatManager(
    groupchat=group_chat,
    llm_config=config_list_gpt4,
    code_execution_config=False,
    is_termination_msg=is_termination_msg,
)

# Initiates the chat with Alice
agents[0].initiate_chat(
    manager,
    message="""
                        There are 9 players in this game, split equally into Teams A, B, C. Therefore each team has 3 players, including the team leader.
                        The task is to find out the sum of chocolate counts from all nine players. I will now start with my team.
                        NEXT: A1""",
)