# 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)