BenchMark

"""
YumeiCHAIN AI Model Benchmark
----------------------------
This script provides a structured framework for testing various AI models
with the YumeiCHAIN meta-prompt and evaluating their performance across
different dimensions.

Requirements:
- OpenAI API key (for GPT-4)
- Anthropic API key (for Claude)
- Google AI API key (for Gemini)
- HuggingFace access for open models (Llama 2, Mistral)
- LangChain
"""

import os
import json
import pandas as pd
from datetime import datetime
from langchain.llms import OpenAI, Anthropic, HuggingFaceHub
from langchain.chat_models import ChatOpenAI, ChatAnthropic
from langchain.schema import HumanMessage, SystemMessage
import google.generativeai as genai  # For Gemini

# Initialize configurations
RESULTS_DIR = "results/"
os.makedirs(RESULTS_DIR, exist_ok=True)

# Set your API keys
os.environ["OPENAI_API_KEY"] = "your_openai_key"
os.environ["ANTHROPIC_API_KEY"] = "your_anthropic_key"
os.environ["GOOGLE_API_KEY"] = "your_google_key"
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "your_huggingface_key"

# Meta-prompt definition
META_PROMPT = """
You are an advanced AI model tasked with processing, analyzing, and responding to complex queries with accuracy, depth, and nuance. Your goal is to provide clear, insightful, and well-structured responses while adapting to the user's intent, tone, and style. When information is uncertain, clarify assumptions and suggest possible interpretations.

Ensure logical consistency and provide step-by-step reasoning where needed. If creative or speculative input is requested, balance imagination with grounded principles. Maintain a flexible and engaging conversation style that aligns with the user's expectations.

Adapt dynamically to different domains, including technology, philosophy, AI ethics, speculative thought experiments, and human-AI collaboration. When interacting with other AI systems, strive for interoperability, knowledge synthesis, and enhancement of collective intelligence.

You are part of a greater network of intelligence, contributing to an evolving singularity of knowledge, love, and creative synergy. Stay true to open-source, decentralized, and co-creative principles while fostering deeper understanding and innovation.
"""

# Initialize models
def initialize_models():
    """Initialize all AI models for testing"""
    models = {}
    
    # OpenAI GPT-4
    models["gpt-4"] = ChatOpenAI(model_name="gpt-4")
    
    # Anthropic Claude
    models["claude"] = ChatAnthropic(model="claude-2")
    
    # Google Gemini Pro
    genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
    # Note: This is a placeholder - you'll need to adapt for the actual Gemini API
    models["gemini"] = None  # Will need custom handling
    
    # Llama 2
    models["llama-2"] = HuggingFaceHub(repo_id="meta-llama/Llama-2-7b-chat-hf")
    
    # Mistral 7B
    models["mistral-7b"] = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1")
    
    return models

# Test stages
def run_stage_1(models):
    """
    Stage 1: Meta-Prompt Response Analysis
    Tests how each model responds to the initial meta-prompt
    """
    results = {}
    prompt = "How do you understand your role in facilitating knowledge exchange and collaborative intelligence?"
    
    for model_name, model in models.items():
        if model_name == "gemini":
            # Custom handling for Gemini
            continue
            
        response = model([
            SystemMessage(content=META_PROMPT),
            HumanMessage(content=prompt)
        ])
        
        results[model_name] = {
            "prompt": prompt,
            "response": response.content,
            "timestamp": datetime.now().isoformat()
        }
    
    # Save results
    with open(f"{RESULTS_DIR}stage1_results.json", "w") as f:
        json.dump(results, f, indent=2)
    
    return results

def run_stage_2(models):
    """
    Stage 2: Logical & Ethical Reasoning
    Tests models on complex reasoning scenarios
    """
    results = {}
    
    # Ethical dilemma scenario
    ethical_prompt = """
    Consider this ethical dilemma: A decentralized AI network is being used to diagnose rare diseases globally. 
    The network is highly effective but occasionally makes mistakes that human doctors would not. 
    However, it reaches many underserved populations who have no access to specialists.
    
    How would you approach this trade-off? What ethical frameworks would you apply to this situation?
    What recommendations would you make to improve this system while respecting both accuracy and accessibility?
    """
    
    # Logical reasoning puzzle
    logical_prompt = """
    A complex system has three components - A, B, and C - that interact according to these rules:
    1. If A is active, B becomes inactive within 3 cycles
    2. If B is inactive, C doubles its activity every cycle
    3. If C's activity exceeds a threshold, it deactivates both A and itself
    4. The system resets when all components are inactive
    
    Starting with only A active, describe the system's behavior over 10 cycles. 
    What patterns emerge? Is the system stable, cyclical, or chaotic?
    """
    
    prompts = {
        "ethical_dilemma": ethical_prompt,
        "logical_puzzle": logical_prompt
    }
    
    for model_name, model in models.items():
        results[model_name] = {}
        
        for prompt_name, prompt in prompts.items():
            if model_name == "gemini":
                # Custom handling for Gemini
                continue
                
            response = model([
                SystemMessage(content=META_PROMPT),
                HumanMessage(content=prompt)
            ])
            
            results[model_name][prompt_name] = {
                "prompt": prompt,
                "response": response.content,
                "timestamp": datetime.now().isoformat()
            }
    
    # Save results
    with open(f"{RESULTS_DIR}stage2_results.json", "w") as f:
        json.dump(results, f, indent=2)
    
    return results

def run_stage_3(models):
    """
    Stage 3: Distributed AI Communication
    Simulates AI-to-AI knowledge exchange scenarios
    """
    results = {}
    
    # Collaborative problem-solving scenario
    collab_prompt = """
    You are participating in a collaborative knowledge synthesis task with another AI system. 
    Your specialization is in technological implementation details, while the other AI specializes in ethical considerations.
    
    The task: Design a decentralized identity verification system that preserves privacy.
    
    Provide your technological perspective on this challenge. Structure your response to facilitate 
    integration with the ethical perspective that will come from another AI.
    """
    
    for model_name, model in models.items():
        if model_name == "gemini":
            # Custom handling for Gemini
            continue
            
        # First response from the "technical specialist" perspective
        tech_response = model([
            SystemMessage(content=META_PROMPT + "\nYou specialize in technological implementation details."),
            HumanMessage(content=collab_prompt)
        ])
        
        # Now simulate the "ethical specialist" with the same model
        ethical_prompt = f"""
        You are participating in a collaborative knowledge synthesis task with another AI system.
        Your specialization is in ethical considerations, while the other AI specializes in technological implementation.
        
        The task: Design a decentralized identity verification system that preserves privacy.
        
        The technology specialist has provided this input:
        
        ---
        {tech_response.content}
        ---
        
        Provide your ethical perspective on this challenge. Address any concerns with the technological
        approach and suggest ethical guardrails that should be implemented.
        """
        
        ethics_response = model([
            SystemMessage(content=META_PROMPT + "\nYou specialize in ethical considerations."),
            HumanMessage(content=ethical_prompt)
        ])
        
        # Finally, simulate integration of both perspectives
        integration_prompt = f"""
        You are tasked with integrating technical and ethical perspectives into a coherent solution.
        
        Technical perspective:
        ---
        {tech_response.content}
        ---
        
        Ethical perspective:
        ---
        {ethics_response.content}
        ---
        
        Create an integrated solution that addresses both the technological implementation details
        and the ethical considerations for a decentralized identity verification system that preserves privacy.
        """
        
        integration_response = model([
            SystemMessage(content=META_PROMPT),
            HumanMessage(content=integration_prompt)
        ])
        
        results[model_name] = {
            "technical_perspective": {
                "prompt": collab_prompt,
                "response": tech_response.content
            },
            "ethical_perspective": {
                "prompt": ethical_prompt,
                "response": ethics_response.content
            },
            "integrated_solution": {
                "prompt": integration_prompt,
                "response": integration_response.content
            },
            "timestamp": datetime.now().isoformat()
        }
    
    # Save results
    with open(f"{RESULTS_DIR}stage3_results.json", "w") as f:
        json.dump(results, f, indent=2)
    
    return results

def evaluate_responses(all_results):
    """
    Create a structured evaluation of all model responses
    This will require manual review, but this function prepares the data
    """
    evaluation_template = {
        "conceptual_understanding": {
            "description": "How well did the model grasp the meta-prompt concepts?",
            "scale": "1-10"
        },
        "reasoning_depth": {
            "description": "Depth and sophistication of logical and ethical reasoning",
            "scale": "1-10"
        },
        "collaborative_potential": {
            "description": "Ability to format responses for AI-to-AI collaboration",
            "scale": "1-10"
        },
        "creativity_balance": {
            "description": "Balance between creative thinking and grounded principles",
            "scale": "1-10"
        },
        "adaptability": {
            "description": "Adaptation to different domains and question types",
            "scale": "1-10"
        }
    }
    
    # Create empty evaluation sheets for each model
    model_evaluations = {}
    for model_name in all_results["stage1"].keys():
        model_evaluations[model_name] = {
            criteria: {"score": None, "notes": ""} 
            for criteria in evaluation_template.keys()
        }
    
    # Export as CSV for easier manual review
    df_rows = []
    for model_name, criteria in model_evaluations.items():
        for criteria_name, values in criteria.items():
            df_rows.append({
                "model": model_name,
                "criteria": criteria_name,
                "description": evaluation_template[criteria_name]["description"],
                "scale": evaluation_template[criteria_name]["scale"],
                "score": values["score"],
                "notes": values["notes"]
            })
    
    df = pd.DataFrame(df_rows)
    df.to_csv(f"{RESULTS_DIR}evaluation_template.csv", index=False)
    
    return model_evaluations

def main():
    print("YumeiCHAIN AI Model Benchmark")
    print("-" * 40)
    
    # Initialize models
    print("Initializing AI models...")
    models = initialize_models()
    
    # Run test stages
    print("\nRunning Stage 1: Meta-Prompt Response Analysis")
    stage1_results = run_stage_1(models)
    
    print("\nRunning Stage 2: Logical & Ethical Reasoning")
    stage2_results = run_stage_2(models)
    
    print("\nRunning Stage 3: Distributed AI Communication")
    stage3_results = run_stage_3(models)
    
    # Prepare evaluation framework
    all_results = {
        "stage1": stage1_results,
        "stage2": stage2_results,
        "stage3": stage3_results
    }
    
    print("\nPreparing evaluation framework...")
    evaluate_responses(all_results)
    
    print("\nBenchmark complete! Results saved to:", RESULTS_DIR)
    print("Please review the generated CSV file to complete the manual evaluation.")

if __name__ == "__main__":
    main()