show an example implementation with a ConsensusBuildingConversation, 3 swarm agents with openaimodels that use gpt-4-0125-preview, and a prompttemplate that is used to start their consensus. the prompttemplate should trigger the first agent to make a proposal to solve a task.
To create a ConsensusBuildingConversation scenario with 3 Swarm Agents utilizing OpenAI models based on GPT-4 and initiating the conversation with a PromptTemplate to solve a task, let's structure it step-by-step. We'll start by defining the necessary components:

1. **ConsensusBuildingConversation** - Manages the flow of messages and consensus building among agents.
2. **Swarm Agents** - Three agents equipped with GPT-4-based models, OpenAIToolModel, to generate proposals and participate in consensus.
3. **PromptTemplate** - Used to kickstart the conversation with a specific task.

Here's an example implementation in Python:

```python
# File: OpenAIToolModel.py
from swarmauri.standard.models.base.ModelBase import ModelBase
from swarmauri.core.models.IPredict import IPredict
from openai import OpenAI

class OpenAIToolModel(ModelBase, IPredict):
    def __init__(self, api_key: str, model_name: str = "gpt-4-0125-preview"):
        super().__init__(model_name)
        self.client = OpenAI(api_key=api_key)

    def predict(self, prompt: str) -> str:
        response = self.client.completions.create(
            model=self.model_name,
            prompt=prompt,
            max_tokens=1024,
            temperature=0.7
        )
        return response.choices[0].text.strip()

# File: swarmauri/standard/prompts/concrete/PromptTemplate.py (already provided in the defined implementation)
from ..base.PromptBase import PromptBase

class PromptTemplate(PromptBase):
    def __init__(self, template: str = "", variables: Dict[str, str] = {}):
        super().__init__(template, variables)

    def __call__(self, variables: Dict[str, str] = {}):
        formatted_prompt = self.template.format(**variables)
        return formatted_prompt

# File: ConsensusBuildingConversation.py (provided in the example)
# Implementation of `ConsensusBuildingConversation` with method `initiate_consensus` to start the conversation

# Define Swarm Agents with OpenAIToolModel
class SwarmAgent:
    def __init__(self, model):
        self.model = model

    def contribute_to_conversation(self, prompt):
        proposal = self.model.predict(prompt)
        return proposal

# Set up PromptTemplate for initiating the consensus
task_description = "Solve the problem of efficiently distributing vaccines in a fair manner."
prompt_template = PromptTemplate(template="How should we approach: {task}?", variables={"task": task_description})

# Initialize OpenAI Models for each Agent (fake API key for demonstration)
api_key = "your_openai_api_key"
agent1 = SwarmAgent(OpenAIToolModel(api_key))
agent2 = SwarmAgent(OpenAIToolModel(api_key, "gpt-4-0125-preview-variant1"))
agent3 = SwarmAgent(OpenAIToolModel(api_key, "gpt-4-0125-preview-variant2"))

# Start the conversation with a task
initiating_prompt = prompt_template()
print(f"Initiating Prompt: {initiating_prompt}")

# Agent 1 makes the first proposal
first_proposal = agent1.contribute_to_conversation(initiating_prompt)
print(f"Agent 1 proposes: {first_proposal}")

# From here, you could continue the flow where Agent 2 and 3 provide feedback, additional proposals,
# and the conversation is steered towards building consensus as per `ConsensusBuildingConversation` logic.

```

In this example:
- We initialize three agents with the `OpenAIToolModel`, configured to use a GPT-4 model (assuming the `gpt-4-0125-preview` identifier and variants are placeholders for actual GPT-4 model identifiers).
- A `PromptTemplate` is defined and used to kickstart the conversation by generating an initiating prompt.
- Agent 1 reads the initiating prompt and makes the first proposal towards solving the task.
- Next steps (not fully implemented) involve other agents reacting to the proposal, contributing their ideas, and the group collectively moving towards a consensus.

This example provides a starting point. The actual consensus logic, including how proposals are voted on and a consensus is reached, would need to be implemented within the `ConsensusBuildingConversation` logic using methods such as `add_proposal`, `add_comment`, and `check_agreement`.

?  show the rest of the consensusbuildingconversation
Building upon the initial setup, we'll now flesh out the rest of the `ConsensusBuildingConversation` to demonstrate how agents might engage in a consensus-driven discussion, leveraging their unique capabilities or viewpoints generated by their respective GPT-4 models. This extension will include methods for agents to add proposals, comments, and votes, along with the mechanism to check for and reach consensus.

### Step 1: Expansion of ConsensusBuildingMessage and Conversation Classes

We'll start with expanding the `ConsensusBuildingMessage` type for various message kinds, and then work on the conversation logic to incorporate consensus mechanisms.

```python
# Considering ConsensusBuildingMessage and SharedConversation classes are defined,
# let's elaborate on different message types: Proposal and Comment.

class ConsensusBuildingMessage:
    def __init__(self, sender_id: str, content: str, message_type: str = "proposal"):
        self.sender_id = sender_id
        self.content = content
        self.type = message_type  # can be 'proposal', 'comment', or 'vote'
    
    def __str__(self):
        return f"[{self.type.upper()} by {self.sender_id}]: {self.content}"
```

### Step 2: Adding Proposals, Comments, and Votes

Now, let's implement functionalities for agents to add proposals, comments, and how the conversation tracks votes to reach a consensus.

```python
class ConsensusBuildingConversation:
    def __init__(self):
        self.messages = []
        self.votes = {}

    def add_message(self, message: ConsensusBuildingMessage):
        print(str(message))
        self.messages.append(message)
        if message.type == "vote":
            self.votes[message.content] = self.votes.get(message.content, 0) + 1

    def initiate_consensus(self, task_description):
        initiate_message = ConsensusBuildingMessage("admin", task_description, "task")
        self.add_message(initiate_message)

    def add_proposal(self, agent_id, proposal):
        proposal_message = ConsensusBuildingMessage(agent_id, proposal, "proposal")
        self.add_message(proposal_message)

    def add_comment(self, agent_id, comment):
        comment_message = ConsensusBuildingMessage(agent_id, comment, "comment")
        self.add_message(comment_message)

    def vote(self, agent_id, proposal):
        vote_message = ConsensusBuildingMessage(agent_id, proposal, "vote")
        self.add_message(vote_message)

    def check_agreement(self):
        consensus_threshold = 2  # For simplicity, consensus is reached if at least two votes match
        for proposal, count in self.votes.items():
            if count >= consensus_threshold:
                return True, proposal
        return False, ""
```

### Step 3: Running the Consensus-Building Process

Finally, you can simulate agents engaging in the conversation, proposing ideas, commenting, and eventually voting on proposals.

```python
# Initialize the conversation process
conversation = ConsensusBuildingConversation()
task = "Enhancing the efficiency of urban transportation systems."
conversation.initiate_consensus(task)

# Simulate agents contributing to the conversation
conversation.add_proposal("Agent1", "Proposal 1: Implement a dynamic routing system for public transportation.")
conversation.add_comment("Agent2", "Comment: Support Proposal 1 with smart traffic management.")
conversation.add_proposal("Agent3", "Proposal 2: Increase investment in bike-sharing infrastructure.")

# Voting on the proposals
conversation.vote("Agent1", "Proposal 1")
conversation.vote("Agent2", "Proposal 1")
conversation.vote("Agent3", "Proposal 2")

# Check and declare consensus
consensus_reached, consensus_proposal = conversation.check_agreement()
if consensus_reached:
    print(f"Consensus Reached on: {consensus_proposal}")
else:
    print("No Consensus Reached.")
```

This code simulates the process where three agents discuss a task, propose strategies, comment on proposals, and vote to decide on the best approach. A consensus is reported if at least two agents agree on a specific proposal.

This illustrative example focuses on the mechanics of the conversation and consensus process among agents using pre-defined roles and behaviors. In a real-world application, the agents’ contributions would be dynamically generated responses from the GPT-4 AI model based on the prompt provided, accounting for more complex interactions and decision-making processes.

?  show an example implementation with a ConsensusBuildingConversation, 3 swarm agents with openaimodels that use gpt-4-0125-preview, and a prompttemplate that is used to start their consensus. the prompttemplate should trigger the first agent to make a proposal to solve a task. show the complete conversation flow from first proposal to voting to reaching consensus.
To illustrate a complete flow from proposal generation to consensus within a simulated environment using 3 swarm agents, each equipped with an OpenAI GPT-4 model, and a PromptTemplate to kick-start their conversation, we'll set up a structured example. This example will encompass initiating discussions, generating proposals based on the first prompt, allowing agents to respond, and finally voting to reach consensus on how to proceed. 

Let's begin:

### 1. Defining the Base Setup

First, we define our setup, assuming the `OpenAIToolModel`, `PromptTemplate`, `ConsensusBuildingConversation`, and `ConsensusBuildingMessage` are implemented as previously described.

### 2. Initializing the Environment

```python
api_key = "your_openai_api_key"

# Defining three agents with their respective OpenAI models focused on a "gpt-4-0125-preview" and its variants
agent1 = SwarmAgent(OpenAIToolModel(api_key, "gpt-4-0125-preview"))
agent2 = SwarmAgent(OpenAIToolModel(api_key, "gpt-4-0125-preview-variant1"))
agent3 = SwarmAgent(OpenAIToolModel(api_key, "gpt-4-0125-preview-variant2"))

# The task to be solved, initiating the conversation
task_description = "Devise an innovative strategy to improve global literacy rates."
prompt_template = PromptTemplate(template="Initial approach to address: {task}?", variables={"task": task_description})

# Creating the conversation object
conversation = ConsensusBuildingConversation(topic="Global Literacy Strategy", participants=["agent1", "agent2", "agent3"])
```

### 3. Conducting the Conversation

Agent 1 kicks off the conversation:

```python
initiating_prompt = prompt_template()
print(f"Agent 1 Initiating: {initiating_prompt}")

# Agent 1 makes the first proposal based on received prompt
first_proposal = agent1.contribute_to_conversation(initiating_prompt)
conversation.initiate_consensus(initiating_prompt)
conversation.add_proposal("agent1", first_proposal)

# Assume Agent 2 and 3 generate their responses based on the initial proposal
response_agent2 = agent2.contribute_to_conversation("Commenting on the first proposal: " + first_proposal)
conversation.add_comment("agent2", response_agent2)

response_agent3 = agent3.contribute_to_conversation("Suggest an alternative strategy to " + first_proposal)
conversation.add_proposal("agent3", response_agent3)
```

### 4. Voting Phase and Checking for Consensus

After discussion, it's time to vote on the proposed strategies. For simplicity, let's simulate the voting:

```python
# Voting - let's say Agent 2 supports Agent 1's initial proposal, and Agent 3 remains with its own.
conversation.vote("agent2", "Proposal from agent1")
conversation.vote("agent3", "Proposal from agent3")

# Agent 1 votes for its own proposal to push the example towards consensus
conversation.vote("agent1", "Proposal from agent1")
```

### 5. Reaching Consensus

We check whether the consensus has been reached following the votes.

```python
is_agreement, agreement_message = conversation.check_agreement()

if is_agreement:
    print(f"Consensus Reached: {agreement_message}")
else:
    print("The agents could not reach a consensus.")
```

### Notes:

- The actual text for proposals and comments generated will be based on GPT-4 responses to prompts crafted from the conversation context, which can include the initial task and responses from other agents.
- To fully implement this scenario, you'd need access to the OpenAI API with the specified GPT-4 model versions or adjust the model identifiers according to available versions.
- Voting logic in this example is simplified for clarity. In practice, each agent's decision-making process to vote could also be informed by additional GPT-4 generated insights or predefined logic.

This example outlines how swarm intelligence could function in a collaborative task-solving context, demonstrating the concept of consensus-building in multi-agent systems using AI-driven decision-making.

?  we need a mechanism for talking to the agent's exec function so that we can get the agent's vote. additionally we need a function so that we can parse the agent's vote and determine which proposal the agent is voting for
To facilitate the agents' ability to vote and parse these votes effectively, we will enhance our agent setup with a mechanism to communicate votes and a function to parse these votes to understand which proposal an agent is voting for. This involves two key enhancements:

1. **Adding Voting Capability to the Agent's `exec` Function**: The `exec` function will be extended to process input for making a proposal, commenting, or voting. Based on the context or the content of the input, the agent will decide whether to propose a new solution, comment on existing proposals, or vote for the best proposal.

2. **Vote Parsing Function**: A function to parse the agent's response to extract the vote. This function will analyze the agent's text response to determine which proposal the agent supports.

Here's how these enhancements can be implemented:

### Updating the Swarm Agent

The `SwarmAgent` class is extended to include a decision-making capability for voting. This example does not directly integrate OpenAI's GPT-4 because of the complexity and access requirements. However, it demonstrates how you might structure these capabilities.

```python
class SwarmAgent:
    def __init__(self, model, agent_id):
        self.model = model
        self.agent_id = agent_id

    def exec(self, context):
        """
        Process the given context and decide on making a proposal, comment, or casting a vote.
        """
        # Example: Decide based on context (This should be replaced with actual model invocation)
        if "proposal" in context:
            response = self.model.predict(context)
            return response
        elif "vote" in context:
            # Simulate decision making for voting
            vote = self.decide_vote(context)
            return vote
        else:
            return "I need more information."

    def decide_vote(self, context):
        """
        Simulate an analysis to decide on voting - in practice, this could involve analyzing proposals with the model.
        """
        # Dummy logic for demonstration - replace with genuine analysis based on model prediction or explicit logic
        if "Proposal from agent1" in context:
            return "Proposal from agent1"
        else:
            return "Undecided"

    def contribute_to_conversation(self, prompt):
        proposal = self.exec(prompt)
        return proposal
```

### Implementing Vote Parsing

A function is created to parse the explicit vote from an agent's response. This function identifies which proposal the agent supports based on its text response.

```python
def parse_agent_vote(agent_response):
    """
    Parses the agent's text response to extract the vote.

    Parameters:
    agent_response (str): The agent's response text from which to extract the vote.

    Returns:
    str: The extracted vote indicating the supported proposal.
    """
    # Example parsing logic
    if "Proposal from agent1" in agent_response:
        return "Proposal from agent1"
    elif "Proposal from agent2" in agent_response:
        return "Proposal from agent2"
    elif "Proposal from agent3" in agent_response:
        return "Proposal from agent3"
    return "Undecided"
```

### Example Usage

Assuming the conversation and proposals have been initiated and it’s time to vote:

```python
# Assuming the context for voting includes all proposals
voting_context = "Please vote on the proposals: Proposal from agent1, Proposal from agent2, Proposal from agent3"

# Each agent votes
for agent in [agent1, agent2, agent3]:
    agent_vote = agent.exec(voting_context)
    parsed_vote = parse_agent_vote(agent_vote)
    # Add the parsed vote to the conversation
    conversation.vote(agent.agent_id, parsed_vote)

# Check for consensus
is_agreement, agreement_message = conversation.check_agreement()
print(f"Consensus Reached: {is_agreement}, with agreement on: {agreement_message}" if is_agreement else "No consensus reached.")
```

This structure and mechanisms allow agents to make informed decisions based on the context provided, such as proposals made by other agents, and provide a manageable way to parse these decisions to understand voting preferences better. In a real-world scenario, the decision-making process (`decide_vote`) and vote parsing should be enhanced to intelligently analyze the conversation context and proposals to determine the agent's stance accurately.