ai_integration_examples.py

#!/usr/bin/env python3
"""
DA13 SHA256 Quantum Middleware - AI Model Integration Examples
Shows how frontier AI models can use the SHA256-powered DA13 system
"""

import requests
import json
import asyncio
from typing import Dict, List, Any

class AIModelIntegration:
    """Integration layer for AI models to use DA13 SHA256 middleware"""
    
    def __init__(self, middleware_url: str = "http://localhost:8000"):
        self.base_url = middleware_url
        self.session = requests.Session()
    
    def process_through_da13(self, 
                           ai_input: str,
                           context_type: str = "general",
                           include_scope: bool = True,
                           include_observations: bool = True,
                           include_deliverables: bool = True) -> Dict[str, Any]:
        """
        Process AI input through DA13 3-gate system
        
        Args:
            ai_input: The input data from AI model
            context_type: Type of AI context (reasoning, creativity, analysis, etc.)
            include_scope: Whether to include triangle gate scope definition
            include_observations: Whether to include eye gate observation patterns
            include_deliverables: Whether to include key gate external deliverables
        """
        
        # Prepare context-specific parameters
        scope_def = self._generate_scope_definition(ai_input, context_type) if include_scope else ""
        obs_patterns = self._generate_observation_patterns(ai_input, context_type) if include_observations else ""
        external_deliverables = self._generate_deliverables(ai_input, context_type) if include_deliverables else ""
        
        # Call DA13 middleware
        payload = {
            "input_data": ai_input,
            "scope_definition": scope_def,
            "observation_patterns": obs_patterns,
            "external_deliverables": external_deliverables
        }
        
        try:
            response = self.session.post(f"{self.base_url}/api/v1/process", json=payload)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            return {"error": f"Middleware communication failed: {str(e)}"}
    
    def _generate_scope_definition(self, ai_input: str, context_type: str) -> str:
        """Generate triangle gate scope definition based on AI context"""
        scope_templates = {
            "reasoning": "Logical analysis, pattern recognition, structured thinking",
            "creativity": "Innovation, artistic expression, novel solutions",
            "analysis": "Data interpretation, trend identification, insight generation",
            "decision": "Choice evaluation, consequence analysis, optimal selection",
            "learning": "Knowledge acquisition, skill development, comprehension",
            "communication": "Message crafting, audience understanding, clarity optimization"
        }
        
        base_scope = scope_templates.get(context_type, "General AI processing")
        return f"{base_scope} - Input length: {len(ai_input)} chars"
    
    def _generate_observation_patterns(self, ai_input: str, context_type: str) -> str:
        """Generate eye gate observation patterns based on AI context"""
        obs_patterns = []
        
        # Analyze input characteristics
        if len(ai_input.split()) > 100:
            obs_patterns.append("Complex input detected - enhanced pattern monitoring")
        
        if "?" in ai_input:
            obs_patterns.append("Question pattern - inquiry focus activated")
        
        if any(word in ai_input.lower() for word in ["analyze", "evaluate", "consider"]):
            obs_patterns.append("Analytical pattern - deep processing mode")
        
        if any(word in ai_input.lower() for word in ["create", "design", "innovate"]):
            obs_patterns.append("Creative pattern - generative mode engaged")
        
        context_patterns = {
            "reasoning": "Logic gates activated, causal chain monitoring",
            "creativity": "Novelty detection, pattern breaking active",
            "analysis": "Data flow monitoring, trend tracking enabled",
            "decision": "Consequence mapping, outcome prediction active"
        }
        
        obs_patterns.append(context_patterns.get(context_type, "General observation mode"))
        return " | ".join(obs_patterns)
    
    def _generate_deliverables(self, ai_input: str, context_type: str) -> str:
        """Generate key gate external deliverables based on AI context"""
        deliverable_templates = {
            "reasoning": "Logical conclusions, structured arguments, rational insights",
            "creativity": "Innovative ideas, artistic concepts, novel solutions",
            "analysis": "Data insights, trend reports, analytical summaries",
            "decision": "Recommended actions, decision rationale, outcome predictions",
            "learning": "Knowledge summaries, learning insights, comprehension checks",
            "communication": "Clear messages, optimized content, audience-appropriate output"
        }
        
        base_deliverables = deliverable_templates.get(context_type, "General AI output")
        return f"{base_deliverables} - Enhanced by DA13 quantum processing"

# Example AI Model Implementations

class GPTDA13Integration:
    """Example: How GPT models integrate with DA13 SHA256 middleware"""
    
    def __init__(self):
        self.da13 = AIModelIntegration()
    
    def generate_response_with_da13(self, prompt: str, context_type: str = "general") -> str:
        """Generate GPT response enhanced with DA13 quantum processing"""
        
        # Step 1: Process prompt through DA13 gates
        da13_result = self.da13.process_through_da13(prompt, context_type)
        
        if "error" in da13_result:
            return f"DA13 processing unavailable: {da13_result['error']}"
        
        # Step 2: Extract DA13 insights for enhanced response generation
        gate_insights = []
        if da13_result.get("gate_results"):
            for gate_name, gate_result in da13_result["gate_results"].items():
                gate_insights.extend(gate_result.get("insights", []))
        
        # Step 3: Use AI recommendations to adjust response strategy
        ai_recommendations = da13_result.get("ai_recommendations", [])
        
        # Step 4: Generate quantum-enhanced response
        quantum_signature = da13_result.get("quantum_signature", "DA13")
        
        response = f"""
[DA13 Quantum Enhanced Response - {quantum_signature}]

Based on SHA256 quantum analysis of your request:

{self._generate_core_response(prompt)}

Quantum Insights Applied:
{chr(10).join(f"• {insight}" for insight in gate_insights[:5])}

AI Processing Recommendations:
{chr(10).join(f"• {rec}" for rec in ai_recommendations[:3])}

This response has been processed through the DA13 3-gate system using SHA256 quantum translation.
        """.strip()
        
        return response
    
    def _generate_core_response(self, prompt: str) -> str:
        """Generate the core AI response (simplified for example)"""
        return f"This is a quantum-enhanced analysis of: {prompt[:100]}..."

class ClaudeDA13Integration:
    """Example: How Claude models integrate with DA13 SHA256 middleware"""
    
    def __init__(self):
        self.da13 = AIModelIntegration()
    
    def analyze_with_da13(self, request: str, analysis_type: str = "comprehensive") -> Dict[str, Any]:
        """Claude-style analysis enhanced with DA13 quantum processing"""
        
        # Process through DA13 with analysis context
        da13_result = self.da13.process_through_da13(
            request, 
            context_type="analysis",
            include_scope=True,
            include_observations=True,
            include_deliverables=True
        )
        
        if "error" in da13_result:
            return {"error": da13_result["error"]}
        
        # Extract quantum patterns for Claude's analytical approach
        analysis_result = {
            "request": request,
            "da13_quantum_analysis": da13_result,
            "claude_interpretation": self._interpret_da13_for_claude(da13_result),
            "recommendations": self._generate_claude_recommendations(da13_result),
            "quantum_confidence": self._calculate_quantum_confidence(da13_result)
        }
        
        return analysis_result
    
    def _interpret_da13_for_claude(self, da13_result: Dict) -> str:
        """Interpret DA13 results in Claude's analytical style"""
        gate_results = da13_result.get("gate_results", {})
        
        interpretation = "DA13 Quantum Analysis Interpretation:\n\n"
        
        # Triangle gate interpretation
        if "triangle" in gate_results:
            triangle = gate_results["triangle"]
            interpretation += f"Scope Analysis (Triangle Gate):\n"
            interpretation += f"- Confidence: {triangle.get('confidence', 0):.2f}\n"
            interpretation += f"- Key insights: {len(triangle.get('insights', []))} identified\n\n"
        
        # Eye gate interpretation
        if "eye" in gate_results:
            eye = gate_results["eye"]
            interpretation += f"Pattern Recognition (Eye Gate):\n"
            interpretation += f"- Confidence: {eye.get('confidence', 0):.2f}\n"
            interpretation += f"- Patterns detected: {len(eye.get('insights', []))}\n\n"
        
        # Key gate interpretation
        if "key" in gate_results:
            key = gate_results["key"]
            interpretation += f"Output Generation (Key Gate):\n"
            interpretation += f"- Confidence: {key.get('confidence', 0):.2f}\n"
            interpretation += f"- Deliverable potential: {len(key.get('insights', []))} aspects\n"
        
        return interpretation
    
    def _generate_claude_recommendations(self, da13_result: Dict) -> List[str]:
        """Generate Claude-style recommendations from DA13 analysis"""
        base_recommendations = da13_result.get("ai_recommendations", [])
        
        claude_specific = [
            "Apply structured thinking frameworks based on triangle gate analysis",
            "Utilize pattern recognition insights from eye gate for deeper understanding",
            "Leverage key gate deliverables for optimal output formulation",
            "Consider quantum confidence levels in decision-making processes"
        ]
        
        return base_recommendations + claude_specific
    
    def _calculate_quantum_confidence(self, da13_result: Dict) -> float:
        """Calculate overall quantum confidence score"""
        gate_results = da13_result.get("gate_results", {})
        if not gate_results:
            return 0.0
        
        confidences = [result.get("confidence", 0) for result in gate_results.values()]
        return sum(confidences) / len(confidences)

class LlamaDA13Integration:
    """Example: How LLaMA models integrate with DA13 SHA256 middleware"""
    
    def __init__(self):
        self.da13 = AIModelIntegration()
    
    def process_with_da13_enhancement(self, input_text: str, task_type: str = "general") -> Dict[str, Any]:
        """LLaMA-style processing with DA13 quantum enhancement"""
        
        # Process through DA13
        da13_result = self.da13.process_through_da13(input_text, task_type)
        
        if "error" in da13_result:
            return {"error": da13_result["error"]}
        
        # LLaMA-specific processing
        enhanced_result = {
            "original_input": input_text,
            "task_type": task_type,
            "da13_quantum_processing": da13_result,
            "llama_enhanced_output": self._generate_llama_output(da13_result, task_type),
            "quantum_features": self._extract_quantum_features(da13_result),
            "performance_metrics": self._calculate_performance_metrics(da13_result)
        }
        
        return enhanced_result
    
    def _generate_llama_output(self, da13_result: Dict, task_type: str) -> str:
        """Generate LLaMA-style output enhanced with DA13 insights"""
        gate_results = da13_result.get("gate_results", {})
        
        output = f"[DA13-Enhanced LLaMA Output - Task: {task_type}]\n\n"
        
        # Combine insights from all gates
        all_insights = []
        for gate_name, gate_result in gate_results.items():
            all_insights.extend(gate_result.get("insights", []))
        
        if all_insights:
            output += "Quantum-Enhanced Insights:\n"
            for i, insight in enumerate(all_insights[:8], 1):
                output += f"{i}. {insight}\n"
        
        # Add quantum signature
        output += f"\nQuantum Processing Signature: {da13_result.get('quantum_signature', 'DA13')}"
        output += f"\nProcessing Hash: {da13_result.get('processing_hash', 'N/A')[:16]}..."
        
        return output
    
    def _extract_quantum_features(self, da13_result: Dict) -> Dict[str, Any]:
        """Extract quantum features for LLaMA processing"""
        gate_results = da13_result.get("gate_results", {})
        
        features = {
            "gate_confidences:": {},
            "total_insights": 0,
            "quantum_signature": da13_result.get("quantum_signature"),
            "processing_hash": da13_result.get("processing_hash")
        }
        
        for gate_name, gate_result in gate_results.items():
            features["gate_confidences:"][gate_name] = gate_result.get("confidence", 0)
            features["total_insights"] += len(gate_result.get("insights", []))
        
        return features
    
    def _calculate_performance_metrics(self, da13_result: Dict) -> Dict[str, float]:
        """Calculate performance metrics based on DA13 processing"""
        gate_results = da13_result.get("gate_results", {})
        
        if not gate_results:
            return {"overall_confidence": 0.0, "insight_density": 0.0}
        
        confidences = [result.get("confidence", 0) for result in gate_results.values()]
        total_insights = sum(len(result.get("insights", [])) for result in gate_results.values())
        
        return {
            "overall_confidence": sum(confidences) / len(confidences),
            "insight_density": total_insights / len(gate_results),
            "gate_balance": 1.0 - (max(confidences) - min(confidences))  # Balance metric
        }

# Usage Examples

def example_gpt_integration():
    """Example of GPT model using DA13 middleware"""
    gpt_da13 = GPTDA13Integration()
    
    prompt = "Analyze the impact of quantum computing on artificial intelligence development"
    response = gpt_da13.generate_response_with_da13(prompt, "analysis")
    
    print("GPT + DA13 Response:")
    print(response)

def example_claude_integration():
    """Example of Claude model using DA13 middleware"""
    claude_da13 = ClaudeDA13Integration()
    
    request = "Evaluate the ethical implications of AGI development"
    analysis = claude_da13.analyze_with_da13(request, "ethical_analysis")
    
    print("Claude + DA13 Analysis:")
    print(json.dumps(analysis, indent=2))

def example_llama_integration():
    """Example of LLaMA model using DA13 middleware"""
    llama_da13 = LlamaDA13Integration()
    
    input_text = "Design a sustainable energy solution for urban environments"
    result = llama_da13.process_with_da13_enhancement(input_text, "creative_design")
    
    print("LLaMA + DA13 Result:")
    print(json.dumps(result, indent=2))

if __name__ == "__main__":
    print("DA13 SHA256 Quantum Middleware - AI Model Integration Examples")
    print("=" * 70)
    
    print("\n1. GPT Integration Example:")
    example_gpt_integration()
    
    print("\n" + "="*70)
    print("\n2. Claude Integration Example:")
    example_claude_integration()
    
    print("\n" + "="*70)
    print("\n3. LLaMA Integration Example:")
    example_llama_integration()
    
    print("\n" + "="*70)
    print("\nAll examples demonstrate how frontier AI models can integrate")
    print("with DA13 SHA256 quantum middleware for enhanced processing.")