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# BeastBullet Performance Configuration
# Optimized for Sonnet-like reasoning quality

import torch
from typing import Dict, List, Optional

class BeastBulletConfig:
    """
    Configuration for achieving high-quality reasoning performance
    comparable to Claude Sonnet.
    """
    
    # ============================================================================
    # EXPERT SELECTION STRATEGY
    # ============================================================================
    
    # ISL Router Configuration
    ISL_CONFIDENCE_THRESHOLD = 0.85  # High confidence threshold for early termination
    ISL_MAX_EXPERTS_PER_QUERY = 3    # Maximum experts to invoke per query
    ISL_CACHE_SIZE = 10000           # Cache size for deterministic routing
    ISL_CACHE_TTL = 3600             # Cache TTL in seconds
    
    # Expert Selection Strategy
    EXPERT_SELECTION_MODE = "confidence_weighted"  # Options: "top_k", "threshold", "confidence_weighted"
    MIN_EXPERT_CONFIDENCE = 0.6      # Minimum confidence to invoke expert
    PARALLEL_EXPERT_EXECUTION = True # Execute experts in parallel
    
    # ============================================================================
    # REASONING QUALITY SETTINGS
    # ============================================================================
    
    # Grok Logic Configuration
    GROK_MAX_REASONING_DEPTH = 5     # Maximum reasoning chain depth
    GROK_ENABLE_CHAIN_OF_THOUGHT = True
    GROK_REQUIRE_JUSTIFICATION = True # Always require justification for answers
    GROK_MIN_EVIDENCE_SOURCES = 2    # Minimum evidence sources for high-confidence answers
    
    # Blackboard Configuration
    BLACKBOARD_CONFLICT_RESOLUTION = "validator_arbitration"  # How to resolve conflicts
    BLACKBOARD_EVIDENCE_DEDUPLICATION = True
    BLACKBOARD_MIN_CONFIDENCE_TO_WRITE = 0.5
    
    # Validator Configuration
    VALIDATOR_ENABLED = True
    VALIDATOR_STRICT_MODE = True     # Reject uncertain answers
    VALIDATOR_CROSS_CHECK_THRESHOLD = 0.9  # Threshold for cross-validation
    VALIDATOR_ADVERSARIAL_TESTING = True   # Test with adversarial examples
    
    # ============================================================================
    # SYNTHESIS QUALITY
    # ============================================================================
    
    # TinyLlama/OLMoE Synthesis Settings
    SYNTHESIS_MODEL = "tinyllama"    # Options: "tinyllama", "olmoe"
    SYNTHESIS_TEMPERATURE = 0.3      # Lower = more deterministic
    SYNTHESIS_TOP_P = 0.9
    SYNTHESIS_MAX_TOKENS = 512
    SYNTHESIS_REPETITION_PENALTY = 1.1
    
    # Answer Quality Requirements
    REQUIRE_CITATIONS = True         # Always cite evidence sources
    REQUIRE_CONFIDENCE_SCORE = True  # Include confidence in answer
    REQUIRE_REASONING_TRACE = True   # Include reasoning steps
    MIN_ANSWER_CONFIDENCE = 0.7      # Minimum confidence to return answer
    
    # ============================================================================
    # PERFORMANCE OPTIMIZATION
    # ============================================================================
    
    # Inference Settings
    DEVICE = "cpu"                   # Options: "cpu", "cuda"
    BATCH_SIZE = 1                   # Batch size for expert inference
    NUM_THREADS = 4                  # CPU threads for inference
    ENABLE_ONNX_OPTIMIZATION = True  # Use ONNX for faster inference
    
    # Memory Management
    MAX_MEMORY_MB = 4096            # Maximum memory usage
    ENABLE_GRADIENT_CHECKPOINTING = False  # Not needed for inference
    CLEAR_CACHE_AFTER_QUERY = True
    
    # ============================================================================
    # EXPERT-SPECIFIC SETTINGS
    # ============================================================================
    
    EXPERT_CONFIGS = {
        "math_expert": {
            "confidence_threshold": 0.85,
            "max_computation_steps": 10,
            "require_step_by_step": True,
        },
        "logic_expert": {
            "confidence_threshold": 0.80,
            "enable_symbolic_reasoning": True,
            "max_inference_depth": 5,
        },
        "validator_expert": {
            "confidence_threshold": 0.90,  # Very high for validator
            "false_positive_tolerance": 0.0,  # Zero tolerance
            "adversarial_mode": True,
        },
        "code_generation_expert": {
            "confidence_threshold": 0.75,
            "enable_syntax_validation": True,
            "require_documentation": True,
        },
        "question_answering_expert": {
            "confidence_threshold": 0.70,
            "require_source_citation": True,
            "max_context_length": 2048,
        },
    }
    
    # ============================================================================
    # QUALITY ASSURANCE
    # ============================================================================
    
    # Answer Validation
    ENABLE_ANSWER_VALIDATION = True
    VALIDATION_CHECKS = [
        "confidence_threshold",
        "evidence_consistency",
        "logical_coherence",
        "factual_accuracy",
        "citation_completeness",
    ]
    
    # Fallback Strategy
    FALLBACK_ON_LOW_CONFIDENCE = True
    FALLBACK_STRATEGY = "request_clarification"  # Options: "request_clarification", "return_uncertain", "escalate"
    
    # Logging and Monitoring
    ENABLE_DETAILED_LOGGING = True
    LOG_REASONING_TRACE = True
    LOG_EXPERT_DECISIONS = True
    LOG_PERFORMANCE_METRICS = True
    
    # ============================================================================
    # ADVANCED FEATURES
    # ============================================================================
    
    # Multi-turn Conversation
    ENABLE_CONVERSATION_MEMORY = True
    MAX_CONVERSATION_HISTORY = 10
    CONVERSATION_CONTEXT_WINDOW = 5
    
    # Self-correction
    ENABLE_SELF_CORRECTION = True
    MAX_CORRECTION_ITERATIONS = 2
    CORRECTION_CONFIDENCE_DELTA = 0.1  # Minimum improvement to accept correction
    
    # Uncertainty Handling
    EXPLICIT_UNCERTAINTY_REPORTING = True
    UNCERTAINTY_THRESHOLD = 0.6       # Below this, report uncertainty
    PROVIDE_ALTERNATIVE_ANSWERS = True  # Provide alternatives when uncertain
    
    @classmethod
    def get_config_dict(cls) -> Dict:
        """Return configuration as dictionary."""
        return {
            key: value for key, value in cls.__dict__.items()
            if not key.startswith('_') and not callable(value)
        }
    
    @classmethod
    def load_from_file(cls, config_path: str):
        """Load configuration from JSON file."""
        import json
        with open(config_path, 'r') as f:
            config = json.load(f)
        for key, value in config.items():
            if hasattr(cls, key):
                setattr(cls, key, value)
    
    @classmethod
    def save_to_file(cls, config_path: str):
        """Save configuration to JSON file."""
        import json
        with open(config_path, 'w') as f:
            json.dump(cls.get_config_dict(), f, indent=2)


# ============================================================================
# USAGE EXAMPLE
# ============================================================================

def create_high_quality_system():
    """
    Create BeastBullet system optimized for Sonnet-like performance.
    """
    from code.isl_router import ISLRouter
    from code.grok_logic import GrokEngine
    from code.shared_blackboard import Blackboard
    
    # Initialize with high-quality config
    config = BeastBulletConfig()
    
    # Create components
    router = ISLRouter(
        experts_dir="experts/",
        confidence_threshold=config.ISL_CONFIDENCE_THRESHOLD,
        max_experts=config.ISL_MAX_EXPERTS_PER_QUERY,
        cache_size=config.ISL_CACHE_SIZE,
    )
    
    blackboard = Blackboard(
        conflict_resolution=config.BLACKBOARD_CONFLICT_RESOLUTION,
        enable_deduplication=config.BLACKBOARD_EVIDENCE_DEDUPLICATION,
    )
    
    grok = GrokEngine(
        router=router,
        blackboard=blackboard,
        max_depth=config.GROK_MAX_REASONING_DEPTH,
        require_justification=config.GROK_REQUIRE_JUSTIFICATION,
        enable_chain_of_thought=config.GROK_ENABLE_CHAIN_OF_THOUGHT,
    )
    
    return grok, config


def query_with_quality_assurance(grok, query: str, config: BeastBulletConfig):
    """
    Query with full quality assurance pipeline.
    """
    # Execute query
    result = grok.reason(query)
    
    # Quality checks
    if config.ENABLE_ANSWER_VALIDATION:
        # Check confidence
        if result.get("confidence", 0) < config.MIN_ANSWER_CONFIDENCE:
            if config.FALLBACK_ON_LOW_CONFIDENCE:
                return {
                    "answer": "I'm not confident enough to provide a definitive answer.",
                    "confidence": result.get("confidence", 0),
                    "uncertainty_reason": "Below confidence threshold",
                    "alternative_answers": result.get("alternatives", []),
                    "reasoning_trace": result.get("reasoning_trace", []),
                }
        
        # Check evidence consistency
        if config.GROK_MIN_EVIDENCE_SOURCES > 1:
            evidence_sources = len(result.get("evidence", []))
            if evidence_sources < config.GROK_MIN_EVIDENCE_SOURCES:
                result["warning"] = f"Only {evidence_sources} evidence sources (minimum: {config.GROK_MIN_EVIDENCE_SOURCES})"
    
    # Add quality metadata
    result["quality_score"] = calculate_quality_score(result, config)
    result["validation_passed"] = True
    
    return result


def calculate_quality_score(result: Dict, config: BeastBulletConfig) -> float:
    """
    Calculate overall quality score for answer.
    """
    scores = []
    
    # Confidence score
    scores.append(result.get("confidence", 0))
    
    # Evidence completeness
    evidence_count = len(result.get("evidence", []))
    evidence_score = min(1.0, evidence_count / config.GROK_MIN_EVIDENCE_SOURCES)
    scores.append(evidence_score)
    
    # Citation completeness
    if config.REQUIRE_CITATIONS:
        has_citations = "citations" in result and len(result["citations"]) > 0
        scores.append(1.0 if has_citations else 0.5)
    
    # Reasoning trace completeness
    if config.REQUIRE_REASONING_TRACE:
        has_trace = "reasoning_trace" in result and len(result["reasoning_trace"]) > 0
        scores.append(1.0 if has_trace else 0.5)
    
    return sum(scores) / len(scores)


# ============================================================================
# EXAMPLE USAGE
# ============================================================================

if __name__ == "__main__":
    # Create high-quality system
    grok, config = create_high_quality_system()
    
    # Example query
    query = "What is 15% of 240? Explain your reasoning."
    
    # Query with quality assurance
    result = query_with_quality_assurance(grok, query, config)
    
    print("=" * 70)
    print("BeastBullet High-Quality Response")
    print("=" * 70)
    print(f"\nQuery: {query}")
    print(f"\nAnswer: {result['answer']}")
    print(f"Confidence: {result['confidence']:.2%}")
    print(f"Quality Score: {result.get('quality_score', 0):.2%}")
    print(f"\nExperts Used: {', '.join(result.get('experts_used', []))}")
    print(f"\nReasoning Trace:")
    for i, step in enumerate(result.get('reasoning_trace', []), 1):
        print(f"  {i}. {step}")
    print(f"\nEvidence Sources: {len(result.get('evidence', []))}")
    print("=" * 70)