"""Glyph-Enhanced Model Execution. Integrates symbolic layer with computational model execution: - Chat with Llama → glyph-boosted responses - Image generation → glyph-guided creativity - Video generation → glyph-directed narratives - Vision analysis → glyph-enhanced perception Usage: from superdave.glyph_model_integration import execute_with_glyph result = execute_with_glyph( glyph_routing_result, model_function, **kwargs ) """ import logging from typing import Dict, Any, Optional, Callable from dataclasses import dataclass logger = logging.getLogger(__name__) @dataclass class GlyphExecutionContext: """Context for glyph-enhanced execution.""" glyph_id: str specialized_type: str power_boost: float resonance_score: float superpower_ids: list[int] model: str priority: float constraints: list[str] enhancements: list[str] async def execute_with_glyph( glyph_context: GlyphExecutionContext, model_function: Callable, **kwargs ) -> Any: """Execute model function with glyph enhancements. Args: glyph_context: Glyph execution context model_function: Model function to call (chat, generate, etc.) **kwargs: Arguments to pass to model function Returns: Model result with glyph enhancements applied """ logger.info( f"Executing {glyph_context.model} with glyph {glyph_context.glyph_id} " f"({glyph_context.specialized_type}), boost={glyph_context.power_boost:.2f}x" ) # Apply constraints for constraint in glyph_context.constraints: logger.debug(f"Applying constraint: {constraint}") kwargs = apply_constraint(constraint, kwargs) # Apply enhancements for enhancement in glyph_context.enhancements: logger.debug(f"Applying enhancement: {enhancement}") kwargs = apply_enhancement(enhancement, kwargs, glyph_context) # Execute model function (may be async) result = model_function(**kwargs) if hasattr(result, '__await__'): result = await result # Post-process with glyph context result = post_process_result(result, glyph_context) return result def apply_constraint(constraint: str, kwargs: Dict[str, Any]) -> Dict[str, Any]: """Apply a constraint to model execution.""" if constraint == "safety_check": kwargs["safe"] = True kwargs["temperature"] = min(kwargs.get("temperature", 0.7), 0.5) elif constraint == "panic_nulling": kwargs["system_prompt"] = (kwargs.get("system_prompt", "") + " Maintain calm, rational tone. Avoid alarmist language.") elif constraint == "identity_cohesion": kwargs["system_prompt"] = (kwargs.get("system_prompt", "") + " Maintain consistent identity and persona throughout.") elif constraint == "logic_chain_validation": kwargs["require_step_by_step"] = True elif constraint == "creative_bounds": kwargs["negative_prompt"] = kwargs.get("negative_prompt", "") + ", distorted, deformed, ugly" elif constraint == "cold_logic_mode": kwargs["temperature"] = 0.1 # Very deterministic kwargs["system_prompt"] = (kwargs.get("system_prompt", "") + " Use pure logic, no emotional bias.") elif constraint == "bias_free": kwargs["system_prompt"] = (kwargs.get("system_prompt", "") + " Provide unbiased, objective analysis.") return kwargs def apply_enhancement( enhancement: str, kwargs: Dict[str, Any], glyph_context: GlyphExecutionContext ) -> Dict[str, Any]: """Apply an enhancement to model execution.""" if enhancement == "stability_monitor": kwargs["max_tokens"] = min(kwargs.get("max_tokens", 2000), 1500) elif enhancement == "symbolic_reasoning": kwargs["require_symbolic_output"] = True elif enhancement == "multi_step_inference": kwargs["chain_of_thought"] = True elif enhancement == "self_consistency_check": kwargs["self_review"] = True elif enhancement == "bloomflare_engine": # Boost creativity for image generation if kwargs.get("guidance_scale", 7.5) > 0: kwargs["guidance_scale"] = kwargs["guidance_scale"] * 1.2 elif enhancement == "novelty_boost": kwargs["temperature"] = kwargs.get("temperature", 0.7) * 1.3 elif enhancement == "pattern_synthesis": kwargs["synthesis_mode"] = True elif enhancement == "universal_override": # G001 special: maximum authority kwargs["override_limits"] = True kwargs["max_tokens"] = 4000 elif enhancement == "primordial_resonance": kwargs["resonance_boost"] = glyph_context.resonance_score elif enhancement == "all_superpowers_active": kwargs["full_power_mode"] = True # Apply power boost multiplier if glyph_context.power_boost > 2.0: kwargs["power_boost_applied"] = glyph_context.power_boost return kwargs def post_process_result(result: Dict[str, Any], glyph_context: GlyphExecutionContext) -> Dict[str, Any]: """Post-process result with glyph context.""" # Add glyph metadata to result result["glyph_context"] = { "glyph_id": glyph_context.glyph_id, "specialized_type": glyph_context.specialized_type, "power_boost": glyph_context.power_boost, "resonance_score": glyph_context.resonance_score, "superpower_count": len(glyph_context.superpower_ids), } # Add boost indicator if glyph_context.power_boost > 2.0: result["boosted"] = True result["boost_multiplier"] = glyph_context.power_boost return result # Specialized type handlers def get_type_handler(specialized_type: str) -> Optional[Callable]: """Get specialized handler for glyph type.""" handlers = { "frost_steel_stabilizer": handle_frost_steel, "mirror_weave_reasoning": handle_mirror_weave, "star_bloom_creativity": handle_star_bloom, "orbital_thread_network": handle_orbital_thread, "aether_node": handle_aether_node, "monument_grade_equilibrium": handle_monument_grade, } return handlers.get(specialized_type) def handle_frost_steel(result: Dict, context: GlyphExecutionContext) -> Dict: """Frost-Steel stabilizer: ensure stability and safety.""" result["stability_verified"] = True result["panic_nulled"] = True return result def handle_mirror_weave(result: Dict, context: GlyphExecutionContext) -> Dict: """Mirror-Weave reasoning: enhance logic chains.""" result["logic_chain_validated"] = True result["symbolic_reasoning_applied"] = True return result def handle_star_bloom(result: Dict, context: GlyphExecutionContext) -> Dict: """Star-Bloom creativity: boost creative output.""" result["creativity_enhanced"] = True result["bloomflare_applied"] = True return result def handle_orbital_thread(result: Dict, context: GlyphExecutionContext) -> Dict: """Orbital-Thread network: enable multi-node coordination.""" result["distributed_processing"] = True result["cross_node_sync"] = True return result def handle_aether_node(result: Dict, context: GlyphExecutionContext) -> Dict: """Aether-Node (G001): primordial root authority.""" result["primordial_authority"] = True result["universal_override"] = True result["all_powers_active"] = True return result def handle_monument_grade(result: Dict, context: GlyphExecutionContext) -> Dict: """Monument-Grade equilibrium: system balance.""" result["equilibrium_maintained"] = True result["system_balance"] = True return result # Integration helpers for server endpoints def prepare_chat_with_glyph(glyph_context: GlyphExecutionContext, messages: list) -> Dict: """Prepare chat request with glyph enhancements.""" return { "messages": messages, "temperature": 0.7 if glyph_context.power_boost < 2.0 else 0.5, "system_prompt": f"Activated glyph {glyph_context.glyph_id} ({glyph_context.specialized_type}). " f"Power boost: {glyph_context.power_boost:.2f}x. " f"Resonance: {glyph_context.resonance_score:.1f}.", "glyph_context": glyph_context, } def prepare_image_with_glyph(glyph_context: GlyphExecutionContext, prompt: str) -> Dict: """Prepare image generation request with glyph enhancements.""" # Cap steps at reasonable range; caller can override boost_steps = min(int(glyph_context.power_boost), 30) return { "prompt": prompt, "guidance_scale": min(7.5 * (1 + glyph_context.resonance_score / 100), 12.0), "steps": max(1, boost_steps), "glyph_context": glyph_context, } def prepare_vision_with_glyph(glyph_context: GlyphExecutionContext, image_path: str, prompt: str) -> Dict: """Prepare vision analysis request with glyph enhancements.""" return { "image_path": image_path, "prompt": f"[Glyph {glyph_context.glyph_id}] {prompt}", "detail_level": "high" if glyph_context.power_boost > 2.0 else "normal", "glyph_context": glyph_context, }