SYMBOL INDEX (1425 symbols across 31 files)

FILE: 00_COURSE/00_mathematical_foundations/exercises/math_foundations_lab.py
  function restaurant_experience_simulation (line 58) | def restaurant_experience_simulation():
  class ContextComponent (line 104) | class ContextComponent:
    method __post_init__ (line 112) | def __post_init__(self):
  class ContextAssemblyFunction (line 117) | class ContextAssemblyFunction:
    method __init__ (line 130) | def __init__(self, max_tokens: int = 1000):
    method assemble_context (line 134) | def assemble_context(self, components: List[ContextComponent],
    method _linear_assembly (line 156) | def _linear_assembly(self, components: List[ContextComponent]) -> Dict:
    method _weighted_assembly (line 181) | def _weighted_assembly(self, components: List[ContextComponent]) -> Dict:
    method _hierarchical_assembly (line 212) | def _hierarchical_assembly(self, components: List[ContextComponent]) -...
  function demonstrate_context_formalization (line 257) | def demonstrate_context_formalization():
  function create_optimization_landscape (line 347) | def create_optimization_landscape():
  class ContextOptimizer (line 430) | class ContextOptimizer:
    method __init__ (line 437) | def __init__(self):
    method objective_function (line 440) | def objective_function(self, params: np.ndarray, components: List[Cont...
    method optimize_assembly_strategy (line 484) | def optimize_assembly_strategy(self, components: List[ContextComponent],
    method _scipy_optimization (line 506) | def _scipy_optimization(self, components: List[ContextComponent]) -> D...
    method _grid_search_optimization (line 549) | def _grid_search_optimization(self, components: List[ContextComponent]...
    method _gradient_descent_optimization (line 581) | def _gradient_descent_optimization(self, components: List[ContextCompo...
    method _numerical_gradient (line 624) | def _numerical_gradient(self, params: np.ndarray, components: List[Con...
  function demonstrate_optimization_methods (line 646) | def demonstrate_optimization_methods():
  function demonstrate_information_theory_basics (line 739) | def demonstrate_information_theory_basics():
  class InformationAnalyzer (line 830) | class InformationAnalyzer:
    method __init__ (line 833) | def __init__(self):
    method calculate_text_entropy (line 836) | def calculate_text_entropy(self, text: str, level: str = 'word') -> fl...
    method calculate_mutual_information (line 872) | def calculate_mutual_information(self, text1: str, text2: str) -> float:
    method analyze_context_information (line 916) | def analyze_context_information(self, components: List[ContextComponent],
  function demonstrate_context_information_analysis (line 992) | def demonstrate_context_information_analysis():
  function demonstrate_bayes_theorem (line 1119) | def demonstrate_bayes_theorem():
  class BayesianContextLearner (line 1214) | class BayesianContextLearner:
    method __init__ (line 1221) | def __init__(self, strategies: List[str]):
    method update_strategy_beliefs (line 1233) | def update_strategy_beliefs(self, strategy_used: str, feedback_score: ...
    method update_component_relevance (line 1292) | def update_component_relevance(self, component_id: str, relevance_evid...
    method select_best_strategy (line 1319) | def select_best_strategy(self) -> Tuple[str, float]:
    method get_strategy_uncertainty (line 1332) | def get_strategy_uncertainty(self) -> float:
    method get_component_relevance_estimate (line 1345) | def get_component_relevance_estimate(self, component_id: str) -> Tuple...
  function demonstrate_bayesian_learning (line 1369) | def demonstrate_bayesian_learning():
  class IntegratedContextEngineer (line 1516) | class IntegratedContextEngineer:
    method __init__ (line 1525) | def __init__(self, max_tokens: int = 1000):
    method engineer_context (line 1538) | def engineer_context(self, components: List[ContextComponent],
    method _calculate_integrated_quality (line 1625) | def _calculate_integrated_quality(self, assembly_result: Dict,
  function demonstrate_integrated_framework (line 1649) | def demonstrate_integrated_framework():
  function generate_lab_summary (line 1769) | def generate_lab_summary():

FILE: 00_COURSE/01_context_retrieval_generation/labs/dynamic_assembly_lab.py
  class ComponentType (line 56) | class ComponentType(Enum):
  class ContextComponent (line 66) | class ContextComponent:
    method __post_init__ (line 77) | def __post_init__(self):
  class AssemblyConstraints (line 83) | class AssemblyConstraints:
    method __post_init__ (line 90) | def __post_init__(self):
  class ContextAssembler (line 102) | class ContextAssembler:
    method __init__ (line 112) | def __init__(self, constraints: AssemblyConstraints = None):
    method add_component (line 117) | def add_component(self, component: ContextComponent) -> None:
    method add_components (line 121) | def add_components(self, components: List[ContextComponent]) -> None:
    method calculate_mutual_information (line 125) | def calculate_mutual_information(self, comp1: ContextComponent, comp2:...
    method calculate_component_utility (line 144) | def calculate_component_utility(self, component: ContextComponent,
    method greedy_assembly (line 163) | def greedy_assembly(self, target_query: str = "") -> Dict[str, Any]:
    method optimal_assembly_dp (line 211) | def optimal_assembly_dp(self, target_query: str = "") -> Dict[str, Any]:
  class AssemblyStrategy (line 270) | class AssemblyStrategy(Enum):
  class ContextOrchestrator (line 278) | class ContextOrchestrator:
    method __init__ (line 284) | def __init__(self):
    method _register_default_patterns (line 289) | def _register_default_patterns(self):
    method create_assembler (line 299) | def create_assembler(self, name: str, constraints: AssemblyConstraints...
    method _rag_pipeline_pattern (line 305) | def _rag_pipeline_pattern(self, query: str, knowledge_docs: List[str],
    method _agent_workflow_pattern (line 342) | def _agent_workflow_pattern(self, task: str, available_tools: List[Dict],
    method _research_assistant_pattern (line 404) | def _research_assistant_pattern(self, research_query: str, papers: Lis...
    method _code_generation_pattern (line 458) | def _code_generation_pattern(self, coding_request: str, existing_code:...
    method _multi_modal_pattern (line 517) | def _multi_modal_pattern(self, query: str, text_content: str = "",
    method assemble_with_pattern (line 580) | def assemble_with_pattern(self, pattern_name: str, strategy: AssemblyS...
  class AssemblyEvaluator (line 612) | class AssemblyEvaluator:
    method __init__ (line 615) | def __init__(self):
    method evaluate_coherence (line 618) | def evaluate_coherence(self, components: List[ContextComponent]) -> fl...
    method evaluate_coverage (line 642) | def evaluate_coverage(self, components: List[ContextComponent]) -> float:
    method evaluate_efficiency (line 648) | def evaluate_efficiency(self, result: Dict[str, Any]) -> float:
    method evaluate_diversity (line 654) | def evaluate_diversity(self, components: List[ContextComponent]) -> fl...
    method comprehensive_evaluation (line 683) | def comprehensive_evaluation(self, result: Dict[str, Any]) -> Dict[str...
  function create_sample_components (line 715) | def create_sample_components() -> List[ContextComponent]:
  function demonstrate_basic_assembly (line 783) | def demonstrate_basic_assembly():
  function demonstrate_assembly_patterns (line 843) | def demonstrate_assembly_patterns():
  function demonstrate_optimization_comparison (line 933) | def demonstrate_optimization_comparison():
  function performance_benchmark (line 975) | def performance_benchmark():
  function advanced_field_integration_demo (line 1023) | def advanced_field_integration_demo():
  function run_dynamic_assembly_lab (line 1111) | def run_dynamic_assembly_lab():

FILE: 00_COURSE/01_context_retrieval_generation/labs/knowledge_retrieval_lab.py
  function demonstrate_vector_similarity_concepts (line 70) | def demonstrate_vector_similarity_concepts():
  class Document (line 122) | class Document:
    method __post_init__ (line 130) | def __post_init__(self):
  class SimpleEmbeddingModel (line 134) | class SimpleEmbeddingModel:
    method __init__ (line 142) | def __init__(self, max_features: int = 1000):
    method fit (line 147) | def fit(self, documents: List[str]):
    method encode (line 163) | def encode(self, texts: List[str]) -> np.ndarray:
    method get_feature_names (line 171) | def get_feature_names(self) -> List[str]:
  class BasicVectorDatabase (line 182) | class BasicVectorDatabase:
    method __init__ (line 190) | def __init__(self, embedding_dim: int):
    method add_document (line 196) | def add_document(self, document: Document, embedding: np.ndarray):
    method search (line 213) | def search(self, query_embedding: np.ndarray, top_k: int = 5) -> List[...
    method _cosine_similarity_batch (line 232) | def _cosine_similarity_batch(self, query_vec: np.ndarray, doc_vecs: np...
    method get_statistics (line 242) | def get_statistics(self) -> Dict[str, Any]:
  function demonstrate_basic_vector_database (line 250) | def demonstrate_basic_vector_database():
  class AdvancedVectorDatabase (line 319) | class AdvancedVectorDatabase:
    method __init__ (line 330) | def __init__(self, embedding_dim: int, index_type: str = "Flat"):
    method _create_faiss_index (line 344) | def _create_faiss_index(self):
    method add_documents (line 356) | def add_documents(self, documents: List[Document], embeddings: np.ndar...
    method search (line 381) | def search(self, query_embedding: np.ndarray, top_k: int = 5,
    method _matches_filter (line 435) | def _matches_filter(self, doc: Document, filter_metadata: Dict[str, An...
    method save_index (line 442) | def save_index(self, filepath: str):
    method load_index (line 458) | def load_index(self, filepath: str):
  class ProfessionalEmbeddingModel (line 478) | class ProfessionalEmbeddingModel:
    method __init__ (line 485) | def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
    method encode (line 496) | def encode(self, texts: List[str], batch_size: int = 32, show_progress...
    method encode_single (line 506) | def encode_single(self, text: str) -> np.ndarray:
  function demonstrate_professional_embeddings (line 510) | def demonstrate_professional_embeddings():
  class RetrievalEvaluator (line 642) | class RetrievalEvaluator:
    method __init__ (line 650) | def __init__(self):
    method evaluate_retrieval (line 653) | def evaluate_retrieval(self, query: str, retrieved_docs: List[Document],
    method _calculate_average_precision (line 708) | def _calculate_average_precision(self, retrieved_ids: List[str], relev...
    method _calculate_reciprocal_rank (line 724) | def _calculate_reciprocal_rank(self, retrieved_ids: List[str], relevan...
    method _calculate_ndcg (line 731) | def _calculate_ndcg(self, retrieved_ids: List[str], relevant_set: set,...
    method get_summary_statistics (line 750) | def get_summary_statistics(self) -> Dict:
  function create_evaluation_dataset (line 780) | def create_evaluation_dataset():
  function demonstrate_retrieval_evaluation (line 820) | def demonstrate_retrieval_evaluation(vector_db, embedding_model):
  class PerformanceBenchmark (line 878) | class PerformanceBenchmark:
    method __init__ (line 885) | def __init__(self):
    method benchmark_search_performance (line 888) | def benchmark_search_performance(self, vector_db, embedding_model,
    method benchmark_scalability (line 941) | def benchmark_scalability(self, embedding_model, doc_counts: List[int]...
    method plot_performance_results (line 988) | def plot_performance_results(self, scalability_results):
  function demonstrate_performance_optimization (line 1031) | def demonstrate_performance_optimization(vector_db, embedding_model):
  function run_complete_lab (line 1098) | def run_complete_lab():
  function setup_lab_environment (line 1169) | def setup_lab_environment():
  function quick_demo (line 1198) | def quick_demo():
  function interactive_search_demo (line 1209) | def interactive_search_demo(vector_db, embedding_model):

FILE: 00_COURSE/01_context_retrieval_generation/labs/prompt_engineering_lab.py
  class PromptingTechnique (line 51) | class PromptingTechnique(Enum):
  class PromptExperiment (line 63) | class PromptExperiment:
  class ExperimentResult (line 75) | class ExperimentResult:
  class BasePromptFramework (line 83) | class BasePromptFramework(ABC):
    method __init__ (line 86) | def __init__(self, name: str, description: str):
    method generate_prompt (line 92) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method parse_response (line 97) | def parse_response(self, response: str) -> Dict[str, Any]:
    method log_experiment (line 101) | def log_experiment(self, result: ExperimentResult):
  class ChainOfThoughtFramework (line 106) | class ChainOfThoughtFramework(BasePromptFramework):
    method __init__ (line 113) | def __init__(self):
    method generate_prompt (line 121) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method _generate_standard_cot_template (line 149) | def _generate_standard_cot_template(self) -> str:
    method _generate_complex_cot_template (line 163) | def _generate_complex_cot_template(self) -> str:
    method _generate_simple_cot_template (line 193) | def _generate_simple_cot_template(self) -> str:
    method _get_domain_guidance (line 205) | def _get_domain_guidance(self, domain: str) -> str:
    method parse_response (line 217) | def parse_response(self, response: str) -> Dict[str, Any]:
  class TreeOfThoughtFramework (line 246) | class TreeOfThoughtFramework(BasePromptFramework):
    method __init__ (line 253) | def __init__(self):
    method generate_prompt (line 261) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method _get_approach_description (line 316) | def _get_approach_description(self, branch_number: int) -> str:
    method parse_response (line 326) | def parse_response(self, response: str) -> Dict[str, Any]:
  class ReActFramework (line 373) | class ReActFramework(BasePromptFramework):
    method __init__ (line 380) | def __init__(self):
    method generate_prompt (line 389) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method _format_tools_description (line 425) | def _format_tools_description(self, tools: List[str]) -> str:
    method parse_response (line 442) | def parse_response(self, response: str) -> Dict[str, Any]:
  class SelfConsistencyFramework (line 481) | class SelfConsistencyFramework(BasePromptFramework):
    method __init__ (line 488) | def __init__(self):
    method generate_prompt (line 496) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method parse_response (line 535) | def parse_response(self, response: str) -> Dict[str, Any]:
  class RoleBasedPromptingFramework (line 579) | class RoleBasedPromptingFramework(BasePromptFramework):
    method __init__ (line 586) | def __init__(self):
    method generate_prompt (line 614) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method _format_expertise (line 656) | def _format_expertise(self, expertise_list: List[str]) -> str:
    method parse_response (line 660) | def parse_response(self, response: str) -> Dict[str, Any]:
  class MetaCognitivePromptingFramework (line 691) | class MetaCognitivePromptingFramework(BasePromptFramework):
    method __init__ (line 698) | def __init__(self):
    method generate_prompt (line 707) | def generate_prompt(self, query: str, context: Optional[Dict] = None) ...
    method _generate_standard_metacognitive_prompt (line 717) | def _generate_standard_metacognitive_prompt(self, query: str) -> str:
    method _generate_deep_metacognitive_prompt (line 751) | def _generate_deep_metacognitive_prompt(self, query: str) -> str:
    method parse_response (line 785) | def parse_response(self, response: str) -> Dict[str, Any]:
  class PromptEngineeringLab (line 835) | class PromptEngineeringLab:
    method __init__ (line 843) | def __init__(self):
    method _initialize_test_cases (line 858) | def _initialize_test_cases(self) -> Dict[str, Dict]:
    method run_systematic_experiment (line 893) | def run_systematic_experiment(self,
    method _run_single_experiment (line 957) | def _run_single_experiment(self,
    method _simulate_llm_response (line 1004) | def _simulate_llm_response(self, technique: PromptingTechnique, prompt...
    method _simulate_cot_response (line 1026) | def _simulate_cot_response(self, query: str) -> str:
    method _simulate_tot_response (line 1048) | def _simulate_tot_response(self, query: str) -> str:
    method _simulate_react_response (line 1093) | def _simulate_react_response(self, query: str) -> str:
    method _simulate_self_consistency_response (line 1111) | def _simulate_self_consistency_response(self, query: str) -> str:
    method _simulate_role_based_response (line 1141) | def _simulate_role_based_response(self, query: str) -> str:
    method _simulate_metacognitive_response (line 1165) | def _simulate_metacognitive_response(self, query: str) -> str:
    method _calculate_performance_metrics (line 1193) | def _calculate_performance_metrics(self,
    method analyze_experiment_results (line 1252) | def analyze_experiment_results(self, results: Dict[str, ExperimentResu...
    method generate_experiment_report (line 1322) | def generate_experiment_report(self,
    method run_comparative_study (line 1388) | def run_comparative_study(self,
    method _perform_cross_case_analysis (line 1474) | def _perform_cross_case_analysis(self, test_case_results: Dict) -> Dic...
  function demo_individual_techniques (line 1534) | def demo_individual_techniques():
  function demo_systematic_experiment (line 1567) | def demo_systematic_experiment():
  function demo_comparative_study (line 1598) | def demo_comparative_study():
  class PromptOptimizer (line 1646) | class PromptOptimizer:
    method __init__ (line 1654) | def __init__(self, lab: PromptEngineeringLab):
    method optimize_prompt_iteratively (line 1658) | def optimize_prompt_iteratively(self,
    method _evaluate_prompt_performance (line 1770) | def _evaluate_prompt_performance(self, prompt: str, test_query: str) -...
    method _generate_optimization_candidates (line 1803) | def _generate_optimization_candidates(self,
    method _add_step_structure (line 1829) | def _add_step_structure(self, prompt: str) -> str:
    method _enhance_clarity (line 1833) | def _enhance_clarity(self, prompt: str) -> str:
    method _add_contextual_guidance (line 1838) | def _add_contextual_guidance(self, prompt: str, test_query: str) -> str:
    method _specify_output_format (line 1843) | def _specify_output_format(self, prompt: str) -> str:
    method _add_verification_step (line 1848) | def _add_verification_step(self, prompt: str) -> str:
  class ExperimentTracker (line 1853) | class ExperimentTracker:
    method __init__ (line 1861) | def __init__(self):
    method track_experiment (line 1865) | def track_experiment(self, experiment_data: Dict[str, Any]):
    method analyze_longitudinal_trends (line 1876) | def analyze_longitudinal_trends(self,
    method _generate_trend_summary (line 1929) | def _generate_trend_summary(self, trend_analysis: Dict) -> Dict[str, A...
    method export_experiment_data (line 1957) | def export_experiment_data(self, format_type: str = 'json') -> str:
  function quick_technique_comparison (line 2002) | def quick_technique_comparison(query: str,
  function benchmark_technique_performance (line 2026) | def benchmark_technique_performance() -> None:
  class ResearchUtilities (line 2094) | class ResearchUtilities:
    method calculate_statistical_significance (line 2103) | def calculate_statistical_significance(scores_a: List[float],
    method generate_research_report (line 2133) | def generate_research_report(study_results: Dict[str, Any],

FILE: 00_COURSE/01_context_retrieval_generation/templates/assembly_patterns.py
  class PatternType (line 42) | class PatternType(Enum):
  class ComponentType (line 56) | class ComponentType(Enum):
  class ContextComponent (line 66) | class ContextComponent:
    method __post_init__ (line 77) | def __post_init__(self):
  class AssemblyResult (line 83) | class AssemblyResult:
    method assembled_context (line 94) | def assembled_context(self) -> str:
  class PatternConfiguration (line 99) | class PatternConfiguration:
  class AssemblyPattern (line 114) | class AssemblyPattern(abc.ABC):
    method __init__ (line 122) | def __init__(self, config: PatternConfiguration = None):
    method assemble (line 138) | def assemble(self, query: str, components: List[ContextComponent],
    method get_pattern_type (line 154) | def get_pattern_type(self) -> PatternType:
    method optimize_components (line 158) | def optimize_components(self, components: List[ContextComponent],
    method _calculate_relevance (line 196) | def _calculate_relevance(self, component: ContextComponent, query: str...
    method _calculate_utility (line 210) | def _calculate_utility(self, component: ContextComponent,
    method _cache_key (line 220) | def _cache_key(self, query: str, components: List[ContextComponent]) -...
    method _get_cached_result (line 227) | def _get_cached_result(self, cache_key: str) -> Optional[AssemblyResult]:
    method _cache_result (line 243) | def _cache_result(self, cache_key: str, result: AssemblyResult):
    method _record_performance (line 266) | def _record_performance(self, metric_name: str, value: float):
    method get_performance_metrics (line 279) | def get_performance_metrics(self) -> Dict[str, Dict[str, float]]:
  class BasicRAGPattern (line 300) | class BasicRAGPattern(AssemblyPattern):
    method get_pattern_type (line 308) | def get_pattern_type(self) -> PatternType:
    method assemble (line 311) | def assemble(self, query: str, components: List[ContextComponent],
    method _calculate_quality_metrics (line 394) | def _calculate_quality_metrics(self, components: List[ContextComponent],
  class EnhancedRAGPattern (line 422) | class EnhancedRAGPattern(BasicRAGPattern):
    method get_pattern_type (line 429) | def get_pattern_type(self) -> PatternType:
    method assemble (line 432) | def assemble(self, query: str, components: List[ContextComponent],
    method _expand_query (line 451) | def _expand_query(self, query: str, **kwargs) -> List[str]:
    method _rerank_with_diversity (line 470) | def _rerank_with_diversity(self, components: List[ContextComponent],
    method _calculate_similarity (line 519) | def _calculate_similarity(self, comp1: ContextComponent,
  class AgentWorkflowPattern (line 537) | class AgentWorkflowPattern(AssemblyPattern):
    method get_pattern_type (line 544) | def get_pattern_type(self) -> PatternType:
    method assemble (line 547) | def assemble(self, query: str, components: List[ContextComponent],
    method _build_agent_instructions (line 640) | def _build_agent_instructions(self, query: str, available_tools: List[...
    method _format_tools (line 675) | def _format_tools(self, available_tools: List[Dict]) -> str:
    method _optimize_agent_components (line 696) | def _optimize_agent_components(self, components: List[ContextComponent...
    method _calculate_agent_quality_metrics (line 726) | def _calculate_agent_quality_metrics(self, components: List[ContextCom...
  class HierarchicalRAGPattern (line 756) | class HierarchicalRAGPattern(AssemblyPattern):
    method get_pattern_type (line 763) | def get_pattern_type(self) -> PatternType:
    method __init__ (line 766) | def __init__(self, config: PatternConfiguration = None):
    method assemble (line 771) | def assemble(self, query: str, components: List[ContextComponent],
    method _build_component_hierarchy (line 834) | def _build_component_hierarchy(self, components: List[ContextComponent...
    method _optimize_hierarchical_assembly (line 855) | def _optimize_hierarchical_assembly(self, components: List[ContextComp...
    method _calculate_hierarchical_quality (line 878) | def _calculate_hierarchical_quality(self, components: List[ContextComp...
  class GraphEnhancedRAGPattern (line 912) | class GraphEnhancedRAGPattern(AssemblyPattern):
    method get_pattern_type (line 919) | def get_pattern_type(self) -> PatternType:
    method assemble (line 922) | def assemble(self, query: str, components: List[ContextComponent],
    method _extract_entities (line 980) | def _extract_entities(self, query: str) -> List[str]:
    method _simulate_graph_traversal (line 997) | def _simulate_graph_traversal(self, entities: List[str],
    method _fuse_graph_vector_results (line 1033) | def _fuse_graph_vector_results(self, graph_components: List[ContextCom...
    method _calculate_graph_quality (line 1072) | def _calculate_graph_quality(self, components: List[ContextComponent],
  class FieldTheoreticPattern (line 1106) | class FieldTheoreticPattern(AssemblyPattern):
    method get_pattern_type (line 1113) | def get_pattern_type(self) -> PatternType:
    method __init__ (line 1116) | def __init__(self, config: PatternConfiguration = None):
    method assemble (line 1128) | def assemble(self, query: str, components: List[ContextComponent],
    method _identify_attractors (line 1198) | def _identify_attractors(self, text: str) -> List[str]:
    method _calculate_field_strength (line 1230) | def _calculate_field_strength(self, query_attractors: List[str],
    method _calculate_resonance (line 1249) | def _calculate_resonance(self, query_attractors: List[str],
    method _attractor_compatibility (line 1272) | def _attractor_compatibility(self, attractor1: str, attractor2: str) -...
    method _apply_cross_pollination (line 1286) | def _apply_cross_pollination(self, components: List[ContextComponent])...
    method _tune_boundaries (line 1315) | def _tune_boundaries(self, components: List[ContextComponent],
    method _assemble_field_components (line 1334) | def _assemble_field_components(self, components: List[ContextComponent],
    method _generate_field_instructions (line 1364) | def _generate_field_instructions(self, query_attractors: List[str]) ->...
    method _detect_emergence (line 1383) | def _detect_emergence(self, components: List[ContextComponent]) -> Dic...
    method _calculate_field_quality (line 1414) | def _calculate_field_quality(self, components: List[ContextComponent],
  class PatternRegistry (line 1448) | class PatternRegistry:
    method __init__ (line 1451) | def __init__(self):
    method _register_default_patterns (line 1455) | def _register_default_patterns(self):
    method register_pattern (line 1466) | def register_pattern(self, pattern_type: PatternType, pattern_class: t...
    method create_pattern (line 1473) | def create_pattern(self, pattern_type: PatternType,
    method list_patterns (line 1482) | def list_patterns(self) -> List[PatternType]:
  class PatternSelector (line 1490) | class PatternSelector:
    method __init__ (line 1493) | def __init__(self, registry: PatternRegistry):
    method _build_selection_rules (line 1497) | def _build_selection_rules(self) -> List[Dict]:
    method select_pattern (line 1532) | def select_pattern(self, query: str, components: List[ContextComponent],
    method _analyze_query (line 1566) | def _analyze_query(self, query: str, components: List[ContextComponent],
    method _estimate_complexity (line 1582) | def _estimate_complexity(self, query: str) -> float:
    method _identify_question_type (line 1592) | def _identify_question_type(self, query: str) -> str:
    method _count_entities (line 1607) | def _count_entities(self, query: str) -> int:
    method _has_temporal_indicators (line 1614) | def _has_temporal_indicators(self, query: str) -> bool:
    method _analyze_components (line 1619) | def _analyze_components(self, components: List[ContextComponent]) -> D...
    method _analyze_context (line 1651) | def _analyze_context(self, **kwargs) -> Dict[PatternType, float]:
    method _evaluate_rule (line 1673) | def _evaluate_rule(self, rule: Dict, query_features: Dict) -> float:
  class ProductionAssemblyOrchestrator (line 1690) | class ProductionAssemblyOrchestrator:
    method __init__ (line 1697) | def __init__(self, config: PatternConfiguration = None):
    method assemble_async (line 1710) | async def assemble_async(self, query: str, components: List[ContextCom...
    method assemble (line 1718) | def assemble(self, query: str, components: List[ContextComponent],
    method _get_pattern_instance (line 1778) | def _get_pattern_instance(self, pattern_type: PatternType) -> Assembly...
    method _create_fallback_result (line 1787) | def _create_fallback_result(self, query: str,
    method _record_metric (line 1813) | def _record_metric(self, metric_name: str, value: Any):
    method get_performance_report (line 1825) | def get_performance_report(self) -> Dict[str, Any]:
    method _calculate_pattern_usage (line 1838) | def _calculate_pattern_usage(self) -> Dict[str, int]:
    method _calculate_performance_trends (line 1845) | def _calculate_performance_trends(self) -> Dict[str, float]:
  function demo_assembly_patterns (line 1862) | def demo_assembly_patterns():

FILE: 00_COURSE/02_context_processing/benchmarks/long_context_evaluation.py
  class PerformanceMetrics (line 39) | class PerformanceMetrics:
    method __post_init__ (line 64) | def __post_init__(self):
  class BenchmarkResult (line 72) | class BenchmarkResult:
    method sequence_lengths (line 80) | def sequence_lengths(self) -> List[int]:
    method forward_times (line 84) | def forward_times(self) -> List[float]:
    method memory_peaks (line 88) | def memory_peaks(self) -> List[float]:
    method throughputs (line 92) | def throughputs(self) -> List[float]:
  class MemoryProfiler (line 99) | class MemoryProfiler:
    method __init__ (line 102) | def __init__(self):
    method start_profiling (line 107) | def start_profiling(self):
    method update_peak (line 113) | def update_peak(self):
    method get_metrics (line 118) | def get_metrics(self) -> Tuple[float, float]:
  class PerformanceBenchmark (line 128) | class PerformanceBenchmark:
    method __init__ (line 131) | def __init__(self, d_model: int = 512, num_heads: int = 8, warmup_runs...
    method evaluate_attention_mechanism (line 137) | def evaluate_attention_mechanism(self,
    method _benchmark_sequence_length (line 180) | def _benchmark_sequence_length(self,
    method evaluate_multiple_mechanisms (line 269) | def evaluate_multiple_mechanisms(self,
    method _should_skip_sequence (line 292) | def _should_skip_sequence(self, mechanism_name: str, seq_len: int) -> ...
    method _estimate_operations (line 301) | def _estimate_operations(self, mechanism_class: type, seq_len: int) ->...
    method _get_memory_complexity (line 318) | def _get_memory_complexity(self, mechanism_name: str) -> str:
    method _compute_attention_entropy (line 329) | def _compute_attention_entropy(self, attention_weights: np.ndarray) ->...
  class ScalabilityAnalyzer (line 340) | class ScalabilityAnalyzer:
    method __init__ (line 343) | def __init__(self):
    method analyze_scaling_behavior (line 352) | def analyze_scaling_behavior(self, results: Dict[str, BenchmarkResult]...
    method _fit_complexity_curve (line 374) | def _fit_complexity_curve(self, seq_lengths: np.ndarray, values: np.nd...
    method _analyze_efficiency (line 409) | def _analyze_efficiency(self, result: BenchmarkResult) -> Dict[str, fl...
    method _compute_scalability_score (line 428) | def _compute_scalability_score(self, result: BenchmarkResult) -> float:
    method _compute_trend (line 449) | def _compute_trend(self, values: List[float]) -> float:
  class QualityEvaluator (line 469) | class QualityEvaluator:
    method __init__ (line 472) | def __init__(self, d_model: int = 512):
    method compare_attention_quality (line 475) | def compare_attention_quality(self,
    method _compare_outputs (line 532) | def _compare_outputs(self,
  class ComparisonReport (line 578) | class ComparisonReport:
    method generate_performance_report (line 582) | def generate_performance_report(results: Dict[str, BenchmarkResult]) -...
    method generate_scalability_report (line 628) | def generate_scalability_report(analysis: Dict[str, Dict[str, Any]]) -...
    method generate_comparison_table (line 664) | def generate_comparison_table(results: Dict[str, BenchmarkResult]) -> ...
  function quick_benchmark (line 715) | def quick_benchmark(attention_classes: List[type],
  function save_benchmark_results (line 735) | def save_benchmark_results(results: Dict[str, BenchmarkResult], filename...
  function load_benchmark_results (line 770) | def load_benchmark_results(filename: str) -> Dict[str, BenchmarkResult]:

FILE: 00_COURSE/02_context_processing/benchmarks/processing_metrics.py
  class QualityScore (line 38) | class QualityScore:
    method overall (line 58) | def overall(self) -> float:
    method __str__ (line 65) | def __str__(self) -> str:
  class QualityMetric (line 68) | class QualityMetric(ABC):
    method evaluate (line 72) | def evaluate(self, context: np.ndarray, **kwargs) -> float:
    method name (line 78) | def name(self) -> str:
  class CoherenceEvaluator (line 86) | class CoherenceEvaluator(QualityMetric):
    method __init__ (line 89) | def __init__(self, window_size: int = 32, stride: int = 16):
    method name (line 94) | def name(self) -> str:
    method evaluate (line 97) | def evaluate(self, context: np.ndarray, **kwargs) -> float:
    method _local_coherence (line 110) | def _local_coherence(self, context: np.ndarray) -> float:
    method _global_coherence (line 125) | def _global_coherence(self, context: np.ndarray) -> float:
    method _structural_coherence (line 152) | def _structural_coherence(self, context: np.ndarray) -> float:
    method _pairwise_similarity (line 173) | def _pairwise_similarity(self, vectors: np.ndarray) -> float:
  class InformationPreservation (line 191) | class InformationPreservation(QualityMetric):
    method __init__ (line 194) | def __init__(self):
    method name (line 198) | def name(self) -> str:
    method evaluate (line 201) | def evaluate(self, original: np.ndarray, processed: np.ndarray, **kwar...
    method measure_preservation (line 212) | def measure_preservation(self, original: np.ndarray, processed: np.nda...
    method _mutual_information (line 222) | def _mutual_information(self, original: np.ndarray, processed: np.ndar...
    method _reconstruction_quality (line 248) | def _reconstruction_quality(self, original: np.ndarray, processed: np....
    method _distributional_similarity (line 283) | def _distributional_similarity(self, original: np.ndarray, processed: ...
  class AttentionAnalyzer (line 314) | class AttentionAnalyzer(QualityMetric):
    method __init__ (line 317) | def __init__(self):
    method name (line 321) | def name(self) -> str:
    method evaluate (line 324) | def evaluate(self, attention_weights: np.ndarray, **kwargs) -> float:
    method attention_focus (line 332) | def attention_focus(self, attention_weights: np.ndarray) -> float:
    method attention_diversity (line 360) | def attention_diversity(self, attention_weights: np.ndarray) -> float:
    method attention_meaningfulness (line 393) | def attention_meaningfulness(self, attention_weights: np.ndarray) -> f...
    method _measure_local_bias (line 408) | def _measure_local_bias(self, attention_weights: np.ndarray) -> float:
    method _measure_structure (line 433) | def _measure_structure(self, attention_weights: np.ndarray) -> float:
  class ComparativeEvaluator (line 465) | class ComparativeEvaluator:
    method __init__ (line 468) | def __init__(self):
    method compare_processing_quality (line 473) | def compare_processing_quality(self,
    method _compute_overall_assessment (line 533) | def _compute_overall_assessment(self, comparison: Dict[str, Any]) -> D...
  class QualityMonitor (line 576) | class QualityMonitor:
    method __init__ (line 579) | def __init__(self, alert_threshold: float = 0.7, degradation_threshold...
    method monitor_processing_quality (line 592) | def monitor_processing_quality(self,
    method _compute_confidence (line 639) | def _compute_confidence(self, quality_score: QualityScore) -> float:
    method _generate_alerts (line 647) | def _generate_alerts(self, quality_score: QualityScore) -> List[Dict[s...
    method _analyze_trends (line 695) | def _analyze_trends(self) -> Dict[str, Any]:
    method _generate_recommendations (line 725) | def _generate_recommendations(self, quality_score: QualityScore) -> Li...
  class MetricsReport (line 750) | class MetricsReport:
    method generate_quality_report (line 754) | def generate_quality_report(quality_scores: List[QualityScore],
    method generate_comparison_report (line 795) | def generate_comparison_report(comparison_result: Dict[str, Any]) -> str:
  function quick_quality_check (line 841) | def quick_quality_check(processed_context: np.ndarray,
  function batch_quality_evaluation (line 869) | def batch_quality_evaluation(contexts: List[np.ndarray],

FILE: 00_COURSE/02_context_processing/implementations/attention_mechanisms.py
  class Attention (line 27) | class Attention(ABC):
    method __init__ (line 30) | def __init__(self, d_model: int, num_heads: int = 8):
    method forward (line 41) | def forward(self, x: np.ndarray, mask: Optional[np.ndarray] = None) ->...
    method __call__ (line 45) | def __call__(self, x: np.ndarray, mask: Optional[np.ndarray] = None) -...
    method _project_qkv (line 48) | def _project_qkv(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray,...
    method _output_projection (line 55) | def _output_projection(self, attended: np.ndarray) -> np.ndarray:
    method _softmax (line 62) | def _softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
  class StandardAttention (line 72) | class StandardAttention(Attention):
    method forward (line 75) | def forward(self, x: np.ndarray, mask: Optional[np.ndarray] = None) ->...
  class SparseAttention (line 108) | class SparseAttention(Attention):
    method __init__ (line 111) | def __init__(self, d_model: int, num_heads: int = 8,
    method forward (line 118) | def forward(self, x: np.ndarray, mask: Optional[np.ndarray] = None) ->...
    method _create_sparse_mask (line 147) | def _create_sparse_mask(self, seq_len: int) -> np.ndarray:
  class StreamingAttention (line 172) | class StreamingAttention(Attention):
    method __init__ (line 175) | def __init__(self, d_model: int, num_heads: int = 8,
    method forward (line 186) | def forward(self, x: np.ndarray, mask: Optional[np.ndarray] = None) ->...
    method _init_cache (line 218) | def _init_cache(self, k: np.ndarray, v: np.ndarray):
    method _update_cache (line 224) | def _update_cache(self, k: np.ndarray, v: np.ndarray) -> Tuple[np.ndar...
    method reset_cache (line 253) | def reset_cache(self):
  class CrossModalAttention (line 263) | class CrossModalAttention(Attention):
    method __init__ (line 266) | def __init__(self, d_model: int, num_heads: int = 8, num_modalities: i...
    method forward (line 279) | def forward(self, modality_inputs: list,
    method _create_cross_modal_mask (line 358) | def _create_cross_modal_mask(self, num_modalities: int, seq_len: int,
  class FlashAttention (line 378) | class FlashAttention(Attention):
    method __init__ (line 381) | def __init__(self, d_model: int, num_heads: int = 8, block_size: int =...
    method forward (line 385) | def forward(self, x: np.ndarray, mask: Optional[np.ndarray] = None) ->...
  function create_causal_mask (line 439) | def create_causal_mask(seq_len: int) -> np.ndarray:
  function create_padding_mask (line 443) | def create_padding_mask(lengths: np.ndarray, max_len: int) -> np.ndarray:
  function attention_entropy (line 451) | def attention_entropy(attn_weights: np.ndarray, axis: int = -1) -> np.nd...
  function benchmark_attention (line 457) | def benchmark_attention(attention_class, seq_lengths: list = [128, 256, ...

FILE: 00_COURSE/02_context_processing/implementations/multimodal_processors.py
  class Modality (line 35) | class Modality(Enum):
  class ModalityData (line 43) | class ModalityData:
    method __post_init__ (line 50) | def __post_init__(self):
  class ModalityEncoder (line 54) | class ModalityEncoder(ABC):
    method __init__ (line 57) | def __init__(self, d_model: int = 512):
    method encode (line 61) | def encode(self, data: Any) -> np.ndarray:
    method output_dim (line 66) | def output_dim(self) -> int:
  class TextEncoder (line 73) | class TextEncoder(ModalityEncoder):
    method __init__ (line 76) | def __init__(self, d_model: int = 512, vocab_size: int = 32000, max_se...
    method encode (line 95) | def encode(self, tokens: np.ndarray) -> np.ndarray:
    method _create_positional_encoding (line 112) | def _create_positional_encoding(self) -> np.ndarray:
    method _self_attention (line 122) | def _self_attention(self, x: np.ndarray) -> np.ndarray:
    method _feed_forward (line 140) | def _feed_forward(self, x: np.ndarray) -> np.ndarray:
    method _softmax (line 146) | def _softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
  class ImageEncoder (line 151) | class ImageEncoder(ModalityEncoder):
    method __init__ (line 154) | def __init__(self, d_model: int = 512, patch_size: int = 16, image_siz...
    method encode (line 172) | def encode(self, image: np.ndarray) -> np.ndarray:
    method _extract_patches (line 211) | def _extract_patches(self, image: np.ndarray) -> np.ndarray:
    method _image_attention (line 237) | def _image_attention(self, x: np.ndarray) -> np.ndarray:
  class AudioEncoder (line 253) | class AudioEncoder(ModalityEncoder):
    method __init__ (line 256) | def __init__(self, d_model: int = 512, sample_rate: int = 16000, n_fft...
    method encode (line 273) | def encode(self, audio: np.ndarray) -> np.ndarray:
    method _compute_mel_spectrogram (line 295) | def _compute_mel_spectrogram(self, audio: np.ndarray) -> np.ndarray:
    method _create_mel_filterbank (line 323) | def _create_mel_filterbank(self) -> np.ndarray:
    method _temporal_modeling (line 357) | def _temporal_modeling(self, mel_spec: np.ndarray) -> np.ndarray:
    method _attention_pooling (line 369) | def _attention_pooling(self, temporal_features: np.ndarray) -> np.ndar...
  class CrossModalFusion (line 385) | class CrossModalFusion(ABC):
    method __init__ (line 388) | def __init__(self, d_model: int = 512):
    method fuse (line 392) | def fuse(self, modality_embeddings: List[np.ndarray],
  class AttentionFusion (line 397) | class AttentionFusion(CrossModalFusion):
    method __init__ (line 400) | def __init__(self, d_model: int = 512, num_heads: int = 8):
    method fuse (line 414) | def fuse(self, modality_embeddings: List[np.ndarray],
  class ConcatenationFusion (line 465) | class ConcatenationFusion(CrossModalFusion):
    method __init__ (line 468) | def __init__(self, d_model: int = 512, num_modalities: int = 3):
    method fuse (line 473) | def fuse(self, modality_embeddings: List[np.ndarray],
  class GatedFusion (line 493) | class GatedFusion(CrossModalFusion):
    method __init__ (line 496) | def __init__(self, d_model: int = 512, num_modalities: int = 3):
    method fuse (line 515) | def fuse(self, modality_embeddings: List[np.ndarray],
  function np_sigmoid (line 552) | def np_sigmoid(x):
  class MultimodalProcessor (line 562) | class MultimodalProcessor:
    method __init__ (line 565) | def __init__(self, d_model: int = 512, fusion_strategy: str = "attenti...
    method process (line 591) | def process(self, modality_data: Dict[Modality, Any],
  class ModalityAlignment (line 647) | class ModalityAlignment:
    method __init__ (line 650) | def __init__(self, d_model: int = 512):
    method align_embeddings (line 664) | def align_embeddings(self, embeddings: Dict[Modality, np.ndarray]) -> ...
    method compute_similarity_matrix (line 680) | def compute_similarity_matrix(self, aligned_embeddings: Dict[Modality,...
  class MultimodalRAG (line 700) | class MultimodalRAG:
    method __init__ (line 703) | def __init__(self, d_model: int = 512):
    method add_multimodal_document (line 712) | def add_multimodal_document(self, doc_id: str,
    method retrieve (line 745) | def retrieve(self, query_text: Optional[Any] = None,
  function create_sample_data (line 791) | def create_sample_data(d_model: int = 512) -> Dict[Modality, Any]:
  function benchmark_multimodal_processing (line 799) | def benchmark_multimodal_processing(processor_class, num_trials: int = 1...
  function visualize_modality_similarities (line 827) | def visualize_modality_similarities(similarity_matrix: np.ndarray,

FILE: 00_COURSE/02_context_processing/implementations/refinement_loops.py
  class QualityScore (line 32) | class QualityScore:
    method overall (line 41) | def overall(self) -> float:
    method __str__ (line 47) | def __str__(self) -> str:
  class RefinementStrategy (line 50) | class RefinementStrategy(Enum):
  class QualityAssessor (line 61) | class QualityAssessor(ABC):
    method assess (line 65) | def assess(self, context: np.ndarray, query: Optional[np.ndarray] = No...
  class EmbeddingQualityAssessor (line 69) | class EmbeddingQualityAssessor(QualityAssessor):
    method __init__ (line 72) | def __init__(self, d_model: int = 512, window_size: int = 32):
    method assess (line 83) | def assess(self, context: np.ndarray, query: Optional[np.ndarray] = No...
    method _assess_coherence (line 94) | def _assess_coherence(self, context: np.ndarray) -> float:
    method _assess_relevance (line 116) | def _assess_relevance(self, context: np.ndarray, query: np.ndarray) ->...
    method _assess_completeness (line 125) | def _assess_completeness(self, context: np.ndarray) -> float:
    method _assess_clarity (line 149) | def _assess_clarity(self, context: np.ndarray) -> float:
    method _assess_safety (line 159) | def _assess_safety(self, context: np.ndarray) -> float:
  function np_sigmoid (line 169) | def np_sigmoid(x):
  class ContextRefiner (line 179) | class ContextRefiner(ABC):
    method refine (line 183) | def refine(self, context: np.ndarray, quality_score: QualityScore,
  class AdaptiveRefiner (line 188) | class AdaptiveRefiner(ContextRefiner):
    method __init__ (line 191) | def __init__(self, d_model: int = 512, refinement_strength: float = 0.2):
    method refine (line 204) | def refine(self, context: np.ndarray, quality_score: QualityScore,
    method _improve_coherence (line 229) | def _improve_coherence(self, context: np.ndarray) -> np.ndarray:
    method _improve_relevance (line 237) | def _improve_relevance(self, context: np.ndarray, query: np.ndarray) -...
    method _improve_completeness (line 253) | def _improve_completeness(self, context: np.ndarray) -> np.ndarray:
    method _improve_clarity (line 275) | def _improve_clarity(self, context: np.ndarray) -> np.ndarray:
    method _apply_smoothing (line 287) | def _apply_smoothing(self, context: np.ndarray) -> np.ndarray:
    method _create_gaussian_kernel (line 312) | def _create_gaussian_kernel(self, size: int) -> np.ndarray:
  function np_softmax (line 317) | def np_softmax(x, axis=-1):
  class RefinementIteration (line 330) | class RefinementIteration:
    method improvement (line 339) | def improvement(self) -> float:
  class IterativeRefiner (line 342) | class IterativeRefiner:
    method __init__ (line 345) | def __init__(self, d_model: int = 512, max_iterations: int = 5,
    method refine (line 359) | def refine(self, context: np.ndarray, query: Optional[np.ndarray] = None,
  class MetaController (line 425) | class MetaController:
    method __init__ (line 428) | def __init__(self):
    method select_strategy (line 441) | def select_strategy(self, initial_quality: QualityScore,
    method get_refiner (line 453) | def get_refiner(self, strategy: RefinementStrategy) -> ContextRefiner:
    method update_performance (line 457) | def update_performance(self, strategy: RefinementStrategy,
  class ConstitutionalRefiner (line 467) | class ConstitutionalRefiner(ContextRefiner):
    method __init__ (line 470) | def __init__(self, d_model: int = 512):
    method refine (line 487) | def refine(self, context: np.ndarray, quality_score: QualityScore,
    method _detect_violations (line 509) | def _detect_violations(self, context: np.ndarray, quality_score: Quali...
  class RefinementPipeline (line 525) | class RefinementPipeline:
    method __init__ (line 528) | def __init__(self, d_model: int = 512, enable_caching: bool = True):
    method refine (line 541) | def refine(self, context: np.ndarray, query: Optional[np.ndarray] = None,
  function compare_quality_scores (line 622) | def compare_quality_scores(score1: QualityScore, score2: QualityScore) -...
  function benchmark_refinement (line 633) | def benchmark_refinement(refiner_class, num_trials: int = 10,
  function create_quality_report (line 684) | def create_quality_report(quality_score: QualityScore) -> str:

FILE: 00_COURSE/02_context_processing/labs/long_context_lab.py
  class AttentionMechanism (line 36) | class AttentionMechanism(ABC):
    method forward (line 40) | def forward(self, x: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
  class ProcessingStats (line 45) | class ProcessingStats:
    method throughput (line 53) | def throughput(self) -> float:
  function create_sample_embeddings (line 57) | def create_sample_embeddings(seq_len: int, d_model: int = 256, seed: int...
  function measure_performance (line 67) | def measure_performance(func, *args, **kwargs) -> Tuple[Any, ProcessingS...
  class StandardAttention (line 97) | class StandardAttention(AttentionMechanism):
    method __init__ (line 100) | def __init__(self, d_model: int, num_heads: int = 8):
    method forward (line 110) | def forward(self, x: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
    method _softmax (line 140) | def _softmax(self, x: np.ndarray, axis: int = -1) -> np.ndarray:
  class SparseAttention (line 146) | class SparseAttention(AttentionMechanism):
    method __init__ (line 149) | def __init__(self, d_model: int, num_heads: int = 8,
    method _create_sparse_mask (line 163) | def _create_sparse_mask(self, seq_len: int) -> np.ndarray:
    method forward (line 185) | def forward(self, x: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
  class StreamingAttention (line 216) | class StreamingAttention(AttentionMechanism):
    method __init__ (line 219) | def __init__(self, d_model: int, num_heads: int = 8,
    method _update_cache (line 237) | def _update_cache(self, k: np.ndarray, v: np.ndarray) -> Tuple[np.ndar...
    method forward (line 263) | def forward(self, x: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
  class HierarchicalMemory (line 303) | class HierarchicalMemory:
    method __init__ (line 306) | def __init__(self, d_model: int, short_term_size: int = 512,
    method add_context (line 322) | def add_context(self, context: np.ndarray) -> Dict[str, int]:
    method retrieve_relevant (line 347) | def retrieve_relevant(self, query: np.ndarray, max_tokens: int = 256) ...
    method _total_length (line 379) | def _total_length(self, memory_list: List[np.ndarray]) -> int:
    method _compress_context (line 383) | def _compress_context(self, context: np.ndarray, compression_matrix: n...
    method _compute_relevance (line 398) | def _compute_relevance(self, query: np.ndarray, memory: np.ndarray) ->...
  class ContextProcessor (line 413) | class ContextProcessor:
    method __init__ (line 416) | def __init__(self, d_model: int = 256, mechanism: str = 'sparse'):
    method process_chunk (line 435) | def process_chunk(self, chunk: np.ndarray, use_memory: bool = True) ->...
    method process_long_sequence (line 455) | def process_long_sequence(self, sequence: np.ndarray, chunk_size: int ...
  class PerformanceBenchmark (line 490) | class PerformanceBenchmark:
    method __init__ (line 493) | def __init__(self):
    method benchmark_mechanisms (line 496) | def benchmark_mechanisms(self, sequence_lengths: List[int] = [64, 128,...
    method benchmark_long_sequence_processing (line 549) | def benchmark_long_sequence_processing(self, max_length: int = 10000) ...
  function visualize_benchmark_results (line 580) | def visualize_benchmark_results(results: Dict):
  function main (line 700) | def main():

FILE: 00_COURSE/02_context_processing/labs/multimodal_lab.py
  class Modality (line 37) | class Modality(Enum):
  class ModalityData (line 45) | class ModalityData:
    method __post_init__ (line 51) | def __post_init__(self):
  class MultimodalContext (line 56) | class MultimodalContext:
    method __post_init__ (line 64) | def __post_init__(self):
    method available_modalities (line 69) | def available_modalities(self) -> List[Modality]:
    method get_modality_data (line 80) | def get_modality_data(self, modality: Modality) -> Optional[np.ndarray]:
  class ModalityEncoder (line 92) | class ModalityEncoder(ABC):
    method encode (line 96) | def encode(self, data: Any) -> np.ndarray:
    method output_dim (line 102) | def output_dim(self) -> int:
  class MultimodalFusion (line 106) | class MultimodalFusion(ABC):
    method fuse (line 110) | def fuse(self, multimodal_context: MultimodalContext) -> np.ndarray:
  class TextEncoder (line 118) | class TextEncoder(ModalityEncoder):
    method __init__ (line 121) | def __init__(self, d_model: int = 512, vocab_size: int = 50000, max_se...
    method encode (line 141) | def encode(self, text_tokens: np.ndarray) -> np.ndarray:
    method _create_positional_encoding (line 160) | def _create_positional_encoding(self) -> np.ndarray:
    method _self_attention (line 171) | def _self_attention(self, x: np.ndarray) -> np.ndarray:
    method _feed_forward (line 189) | def _feed_forward(self, x: np.ndarray) -> np.ndarray:
    method _softmax (line 194) | def _softmax(self, x: np.ndarray, axis: int = -1) -> np.ndarray:
    method output_dim (line 201) | def output_dim(self) -> int:
  class ImageEncoder (line 204) | class ImageEncoder(ModalityEncoder):
    method __init__ (line 207) | def __init__(self, d_model: int = 512, input_channels: int = 3, image_...
    method encode (line 231) | def encode(self, image_data: np.ndarray) -> np.ndarray:
    method _extract_patches (line 269) | def _extract_patches(self, image: np.ndarray) -> np.ndarray:
    method _simple_resize (line 299) | def _simple_resize(self, image: np.ndarray, target_size: Tuple[int, in...
    method _image_attention (line 315) | def _image_attention(self, x: np.ndarray) -> np.ndarray:
    method _softmax (line 333) | def _softmax(self, x: np.ndarray, axis: int = -1) -> np.ndarray:
    method output_dim (line 340) | def output_dim(self) -> int:
  class AudioEncoder (line 343) | class AudioEncoder(ModalityEncoder):
    method __init__ (line 346) | def __init__(self, d_model: int = 512, sample_rate: int = 16000, n_fft...
    method encode (line 365) | def encode(self, audio_data: np.ndarray) -> np.ndarray:
    method _compute_mel_spectrogram (line 388) | def _compute_mel_spectrogram(self, audio: np.ndarray) -> np.ndarray:
    method _create_mel_filterbank (line 418) | def _create_mel_filterbank(self, n_fft_bins: int, n_mels: int) -> np.n...
    method _temporal_modeling (line 459) | def _temporal_modeling(self, spectral_features: np.ndarray) -> np.ndar...
    method _temporal_attention_pooling (line 477) | def _temporal_attention_pooling(self, temporal_embeddings: np.ndarray)...
    method _softmax (line 488) | def _softmax(self, x: np.ndarray) -> np.ndarray:
    method output_dim (line 495) | def output_dim(self) -> int:
  class CrossModalAttentionFusion (line 502) | class CrossModalAttentionFusion(MultimodalFusion):
    method __init__ (line 505) | def __init__(self, d_model: int = 512, num_heads: int = 8):
    method fuse (line 525) | def fuse(self, multimodal_context: MultimodalContext) -> np.ndarray:
    method _cross_modal_attention (line 572) | def _cross_modal_attention(self, modality_embeddings: np.ndarray,
    method _sigmoid (line 605) | def _sigmoid(self, x: np.ndarray) -> np.ndarray:
    method _softmax (line 609) | def _softmax(self, x: np.ndarray, axis: int = -1) -> np.ndarray:
  class HierarchicalFusion (line 615) | class HierarchicalFusion(MultimodalFusion):
    method __init__ (line 618) | def __init__(self, d_model: int = 512):
    method fuse (line 632) | def fuse(self, multimodal_context: MultimodalContext) -> np.ndarray:
  class MultimodalProcessor (line 671) | class MultimodalProcessor:
    method __init__ (line 674) | def __init__(self, d_model: int = 512, fusion_strategy: str = 'cross_a...
    method process_multimodal_context (line 694) | def process_multimodal_context(self,
    method similarity_search (line 747) | def similarity_search(self, query_context: MultimodalContext,
  class MultimodalRAG (line 779) | class MultimodalRAG:
    method __init__ (line 782) | def __init__(self, d_model: int = 512):
    method add_to_knowledge_base (line 787) | def add_to_knowledge_base(self,
    method retrieve_relevant_context (line 805) | def retrieve_relevant_context(self,
  class MultimodalContentAnalyzer (line 837) | class MultimodalContentAnalyzer:
    method __init__ (line 840) | def __init__(self, d_model: int = 512):
    method analyze_content (line 848) | def analyze_content(self,
    method _compute_quality_metrics (line 892) | def _compute_quality_metrics(self, context: MultimodalContext) -> Dict...
    method _softmax (line 930) | def _softmax(self, x: np.ndarray) -> np.ndarray:
  function create_sample_data (line 940) | def create_sample_data():
  function benchmark_multimodal_processing (line 954) | def benchmark_multimodal_processing():
  function visualize_multimodal_results (line 1010) | def visualize_multimodal_results(benchmark_results: Dict):
  function main (line 1109) | def main():

FILE: 00_COURSE/02_context_processing/labs/self_refinement_lab.py
  class QualityMetric (line 36) | class QualityMetric(Enum):
  class QualityScore (line 45) | class QualityScore:
    method overall (line 54) | def overall(self) -> float:
    method __str__ (line 61) | def __str__(self) -> str:
  class RefinementIteration (line 65) | class RefinementIteration:
    method improvement (line 76) | def improvement(self) -> float:
  class ContextAssessor (line 80) | class ContextAssessor(ABC):
    method assess_quality (line 84) | def assess_quality(self, context: np.ndarray, query: Optional[np.ndarr...
  class ContextRefiner (line 88) | class ContextRefiner(ABC):
    method refine_context (line 92) | def refine_context(self, context: np.ndarray, quality_score: QualitySc...
  class SemanticCoherenceAssessor (line 101) | class SemanticCoherenceAssessor(ContextAssessor):
    method __init__ (line 104) | def __init__(self, d_model: int = 256, window_size: int = 32):
    method assess_quality (line 113) | def assess_quality(self, context: np.ndarray, query: Optional[np.ndarr...
    method _assess_coherence (line 139) | def _assess_coherence(self, context: np.ndarray) -> float:
    method _assess_relevance (line 162) | def _assess_relevance(self, context: np.ndarray, query: np.ndarray) ->...
    method _assess_completeness (line 173) | def _assess_completeness(self, context: np.ndarray) -> float:
    method _assess_clarity (line 194) | def _assess_clarity(self, context: np.ndarray) -> float:
    method _assess_factuality (line 214) | def _assess_factuality(self, context: np.ndarray) -> float:
  function np_sigmoid (line 230) | def np_sigmoid(x):
  class AdaptiveContextRefiner (line 241) | class AdaptiveContextRefiner(ContextRefiner):
    method __init__ (line 244) | def __init__(self, d_model: int = 256):
    method refine_context (line 256) | def refine_context(self, context: np.ndarray, quality_score: QualitySc...
    method _improve_coherence (line 282) | def _improve_coherence(self, context: np.ndarray) -> np.ndarray:
    method _improve_relevance (line 297) | def _improve_relevance(self, context: np.ndarray, query: np.ndarray) -...
    method _improve_completeness (line 315) | def _improve_completeness(self, context: np.ndarray) -> np.ndarray:
    method _improve_clarity (line 340) | def _improve_clarity(self, context: np.ndarray) -> np.ndarray:
    method _apply_smoothing (line 358) | def _apply_smoothing(self, context: np.ndarray) -> np.ndarray:
    method _create_smoothing_kernel (line 386) | def _create_smoothing_kernel(self, size: int) -> np.ndarray:
  function np_softmax (line 391) | def np_softmax(x, axis=-1):
  class SelfRefinementPipeline (line 404) | class SelfRefinementPipeline:
    method __init__ (line 407) | def __init__(self, d_model: int = 256, max_iterations: int = 5,
    method refine_context (line 421) | def refine_context(self, initial_context: np.ndarray,
    method get_refinement_analytics (line 501) | def get_refinement_analytics(self) -> Dict[str, Any]:
  class MetaRefinementController (line 532) | class MetaRefinementController:
    method __init__ (line 535) | def __init__(self, d_model: int = 256):
    method select_strategy (line 546) | def select_strategy(self, initial_quality: QualityScore,
    method update_strategy_performance (line 568) | def update_strategy_performance(self, strategy_name: str,
  class ConstitutionalRefinement (line 574) | class ConstitutionalRefinement:
    method __init__ (line 577) | def __init__(self, d_model: int = 256):
    method apply_constitutional_refinement (line 592) | def apply_constitutional_refinement(self, context: np.ndarray,
  class ProductionRefinementSystem (line 614) | class ProductionRefinementSystem:
    method __init__ (line 617) | def __init__(self, d_model: int = 256):
    method refine_context_production (line 627) | def refine_context_production(self, context: np.ndarray, query: Option...
  function create_sample_context (line 689) | def create_sample_context(seq_len: int, d_model: int = 256, quality_leve...
  function benchmark_refinement_pipeline (line 724) | def benchmark_refinement_pipeline():
  function visualize_refinement_results (line 774) | def visualize_refinement_results(results: Dict, refinement_history: List...
  function main (line 891) | def main():

FILE: 00_COURSE/02_context_processing/labs/structured_data_lab.py
  class EntityType (line 39) | class EntityType(Enum):
  class RelationType (line 49) | class RelationType(Enum):
  class Entity (line 61) | class Entity:
    method __hash__ (line 69) | def __hash__(self):
    method __eq__ (line 72) | def __eq__(self, other):
  class Relation (line 76) | class Relation:
    method __hash__ (line 84) | def __hash__(self):
  class Schema (line 88) | class Schema:
    method validate_entity (line 95) | def validate_entity(self, entity: Entity) -> bool:
    method validate_relation (line 99) | def validate_relation(self, relation: Relation) -> bool:
  class KnowledgeGraph (line 109) | class KnowledgeGraph:
    method __init__ (line 112) | def __init__(self, d_model: int = 256, schema: Optional[Schema] = None):
    method add_entity (line 136) | def add_entity(self, entity: Entity) -> bool:
    method add_relation (line 161) | def add_relation(self, relation: Relation) -> bool:
    method get_entity (line 179) | def get_entity(self, entity_id: str) -> Optional[Entity]:
    method get_neighbors (line 183) | def get_neighbors(self, entity_id: str, relation_type: Optional[Relati...
    method find_path (line 191) | def find_path(self, source_id: str, target_id: str, max_depth: int = 3...
    method query_entities (line 220) | def query_entities(self, entity_type: Optional[EntityType] = None,
    method get_subgraph (line 244) | def get_subgraph(self, center_entity_id: str, radius: int = 2) -> 'Kno...
    method compute_embeddings_gnn (line 265) | def compute_embeddings_gnn(self, num_iterations: int = 3) -> Dict[str,...
    method similarity_search (line 319) | def similarity_search(self, query_embedding: np.ndarray, top_k: int = ...
    method _generate_entity_embedding (line 338) | def _generate_entity_embedding(self, entity: Entity) -> np.ndarray:
    method get_statistics (line 354) | def get_statistics(self) -> Dict[str, Any]:
  class GraphRAG (line 378) | class GraphRAG:
    method __init__ (line 381) | def __init__(self, d_model: int = 256):
    method add_document (line 391) | def add_document(self, document_id: str, content: str,
    method retrieve_context (line 407) | def retrieve_context(self, query: str, query_embedding: np.ndarray,
    method _create_unified_context (line 467) | def _create_unified_context(self, query_embedding: np.ndarray,
  class StructuredDataProcessor (line 500) | class StructuredDataProcessor:
    method __init__ (line 503) | def __init__(self):
    method register_schema (line 507) | def register_schema(self, schema: Schema):
    method validate_data (line 512) | def validate_data(self, schema_name: str, data: Dict[str, Any]) -> Dic...
    method extract_entities_from_structured_data (line 531) | def extract_entities_from_structured_data(self, schema_name: str,
    method _create_validator (line 565) | def _create_validator(self, schema: Schema) -> callable:
  class GraphReasoner (line 604) | class GraphReasoner:
    method __init__ (line 607) | def __init__(self, knowledge_graph: KnowledgeGraph):
    method add_inference_rule (line 611) | def add_inference_rule(self, rule_name: str,
    method infer_new_relations (line 621) | def infer_new_relations(self) -> List[Relation]:
    method answer_query (line 653) | def answer_query(self, query: str, query_entities: List[str]) -> Dict[...
    method _find_pattern_matches (line 666) | def _find_pattern_matches(self, pattern: List[Tuple[str, RelationType,...
    method _handle_connectivity_query (line 685) | def _handle_connectivity_query(self, entity_ids: List[str]) -> Dict[st...
    method _handle_similarity_query (line 701) | def _handle_similarity_query(self, entity_ids: List[str]) -> Dict[str,...
    method _handle_path_query (line 727) | def _handle_path_query(self, entity_ids: List[str]) -> Dict[str, Any]:
    method _handle_general_query (line 761) | def _handle_general_query(self, query: str, entity_ids: List[str]) -> ...
  function create_sample_knowledge_graph (line 787) | def create_sample_knowledge_graph() -> KnowledgeGraph:
  function benchmark_graph_operations (line 830) | def benchmark_graph_operations():
  function visualize_graph_performance (line 928) | def visualize_graph_performance(results: Dict):
  function main (line 1032) | def main():

FILE: 00_COURSE/03_context_management/labs/memory_management_lab.py
  class MemoryEntry (line 39) | class MemoryEntry:
    method __post_init__ (line 49) | def __post_init__(self):
    method access (line 57) | def access(self) -> None:
    method decay_priority (line 62) | def decay_priority(self, decay_rate: float = 0.95) -> None:
    method compute_score (line 66) | def compute_score(self, query_embedding: Optional[List[float]] = None)...
  class MemoryInterface (line 78) | class MemoryInterface(ABC):
    method store (line 82) | def store(self, key: str, entry: MemoryEntry) -> bool:
    method retrieve (line 87) | def retrieve(self, key: str) -> Optional[MemoryEntry]:
    method search (line 92) | def search(self, query: str, limit: int = 10) -> List[Tuple[str, Memor...
    method size (line 97) | def size(self) -> int:
    method cleanup (line 102) | def cleanup(self) -> int:
  class WorkingMemory (line 111) | class WorkingMemory(MemoryInterface):
    method __init__ (line 117) | def __init__(self, max_size_bytes: int = 50000, max_entries: int = 100):
    method store (line 124) | def store(self, key: str, entry: MemoryEntry) -> bool:
    method retrieve (line 144) | def retrieve(self, key: str) -> Optional[MemoryEntry]:
    method search (line 154) | def search(self, query: str, limit: int = 10) -> List[Tuple[str, Memor...
    method size (line 169) | def size(self) -> int:
    method cleanup (line 172) | def cleanup(self) -> int:
    method _evict_lru (line 188) | def _evict_lru(self) -> None:
  class LongTermMemory (line 195) | class LongTermMemory(MemoryInterface):
    method __init__ (line 201) | def __init__(self, max_size_bytes: int = 10_000_000, persistence_file:...
    method store (line 214) | def store(self, key: str, entry: MemoryEntry) -> bool:
    method retrieve (line 243) | def retrieve(self, key: str) -> Optional[MemoryEntry]:
    method search (line 251) | def search(self, query: str, limit: int = 10) -> List[Tuple[str, Memor...
    method search_by_tags (line 280) | def search_by_tags(self, tags: List[str], limit: int = 10) -> List[Tup...
    method size (line 298) | def size(self) -> int:
    method cleanup (line 301) | def cleanup(self) -> int:
    method _evict_lowest_importance (line 326) | def _evict_lowest_importance(self) -> bool:
    method _remove_entry (line 335) | def _remove_entry(self, key: str) -> None:
    method _remove_from_indices (line 342) | def _remove_from_indices(self, key: str, entry: MemoryEntry) -> None:
    method _save_to_disk (line 350) | def _save_to_disk(self) -> None:
    method _load_from_disk (line 367) | def _load_from_disk(self) -> None:
  class HierarchicalMemorySystem (line 400) | class HierarchicalMemorySystem:
    method __init__ (line 406) | def __init__(self,
    method store (line 427) | def store(self, key: str, content: str, tags: List[str] = None, priori...
    method retrieve (line 448) | def retrieve(self, key: str) -> Optional[MemoryEntry]:
    method search (line 471) | def search(self, query: str, limit: int = 10, include_external: bool =...
    method optimize (line 512) | def optimize(self) -> Dict[str, int]:
    method get_statistics (line 549) | def get_statistics(self) -> Dict[str, Any]:
  class ContextWindowManager (line 565) | class ContextWindowManager:
    method __init__ (line 571) | def __init__(self,
    method assemble_context (line 592) | def assemble_context(self,
    method _truncate_to_tokens (line 693) | def _truncate_to_tokens(self, text: str, max_tokens: int) -> str:
    method _assemble_final_context (line 711) | def _assemble_final_context(self, components: Dict[str, str]) -> str:
    method optimize_allocations (line 726) | def optimize_allocations(self, history_window: int = 100) -> Dict[str,...
    method get_performance_metrics (line 771) | def get_performance_metrics(self) -> Dict[str, Any]:
  class MemoryPerformanceMonitor (line 792) | class MemoryPerformanceMonitor:
    method __init__ (line 795) | def __init__(self):
    method measure_operation (line 806) | def measure_operation(self, operation_name: str):
    method start_monitoring (line 830) | def start_monitoring(self, memory_system: HierarchicalMemorySystem,
    method stop_monitoring (line 863) | def stop_monitoring(self):
    method _get_memory_usage (line 869) | def _get_memory_usage(self) -> int:
    method _check_alerts (line 875) | def _check_alerts(self, stats: Dict[str, Any]):
    method generate_report (line 898) | def generate_report(self) -> str:
  function demo_basic_memory_hierarchy (line 962) | def demo_basic_memory_hierarchy():
  function demo_context_window_management (line 994) | def demo_context_window_management():
  function benchmark_memory_performance (line 1031) | def benchmark_memory_performance():
  function quick_start_memory_system (line 1093) | def quick_start_memory_system():
  function quick_start_context_manager (line 1101) | def quick_start_context_manager(memory_system=None):

FILE: 10_guides_zero_to_hero/01_min_prompt.py
  class SimpleLLM (line 42) | class SimpleLLM:
    method __init__ (line 45) | def __init__(self, model_name: str = "dummy-model"):
    method count_tokens (line 51) | def count_tokens(self, text: str) -> int:
    method generate (line 59) | def generate(self, prompt: str) -> str:
    method get_stats (line 78) | def get_stats(self) -> Dict[str, Any]:
  function measure_consistency (line 182) | def measure_consistency(prompt: str, n_samples: int = 3) -> Dict[str, Any]:

FILE: 10_guides_zero_to_hero/02_expand_context.py
  function _select_provider_and_model (line 88) | def _select_provider_and_model() -> Tuple[str, str]:
  function _build_client (line 114) | def _build_client() -> Optional[Any]:
  function _build_tokenizer (line 137) | def _build_tokenizer(model_name: str) -> Optional[Any]:
  function count_tokens (line 157) | def count_tokens(text: str) -> int:
  function measure_latency (line 168) | def measure_latency(func, *args, **kwargs) -> Tuple[Any, float]:
  function calculate_metrics (line 190) | def calculate_metrics(prompt: str, response: str, latency: float) -> Dic...
  function generate_response (line 207) | def generate_response(prompt: str, temperature: float = 0.7, max_tokens:...
  function run_experiments (line 246) | def run_experiments() -> Tuple[Dict[str, Dict[str, float]], Dict[str, st...
  function plot_results (line 331) | def plot_results(results: Dict[str, Dict[str, float]]) -> None:
  function print_responses (line 382) | def print_responses(responses: Dict[str, str]) -> None:
  function create_expanded_context (line 402) | def create_expanded_context(
  function demo_template (line 455) | def demo_template() -> None:
  function main (line 503) | def main() -> None:

FILE: 10_guides_zero_to_hero/03_control_loops.py
  function setup_client (line 77) | def setup_client(api_key=None, model=DEFAULT_MODEL):
  function count_tokens (line 101) | def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
  function generate_response (line 122) | def generate_response(
  function format_metrics (line 193) | def format_metrics(metrics: Dict[str, Any]) -> str:
  function display_response (line 215) | def display_response(
  class ControlLoop (line 244) | class ControlLoop:
    method __init__ (line 250) | def __init__(
    method _log (line 286) | def _log(self, message: str) -> None:
    method _call_llm (line 296) | def _call_llm(
    method get_summary_metrics (line 340) | def get_summary_metrics(self) -> Dict[str, Any]:
    method visualize_metrics (line 360) | def visualize_metrics(self) -> None:
  class SequentialChain (line 416) | class SequentialChain(ControlLoop):
    method __init__ (line 422) | def __init__(self, steps: List[Dict[str, Any]], **kwargs):
    method _validate_steps (line 437) | def _validate_steps(self) -> None:
    method run (line 447) | def run(self, initial_input: str) -> Tuple[str, Dict[str, Any]]:
    method display_chain_results (line 483) | def display_chain_results(self, all_outputs: Dict[str, Any]) -> None:
  class IterativeRefiner (line 528) | class IterativeRefiner(ControlLoop):
    method __init__ (line 534) | def __init__(
    method generate_feedback (line 559) | def generate_feedback(self, response: str) -> Tuple[str, Dict[str, Any]]:
    method refine_response (line 572) | def refine_response(
    method run (line 593) | def run(
    method display_refinement_history (line 668) | def display_refinement_history(self, refinement_history: Dict[str, Any...
  class ConditionalBrancher (line 714) | class ConditionalBrancher(ControlLoop):
    method __init__ (line 720) | def __init__(
    method _validate_branches (line 741) | def _validate_branches(self) -> None:
    method classify_input (line 750) | def classify_input(self, input_text: str) -> Tuple[str, Dict[str, Any]]:
    method execute_branch (line 801) | def execute_branch(
    method run (line 825) | def run(
    method display_branching_results (line 864) | def display_branching_results(self, run_details: Dict[str, Any]) -> None:
  class SelfCritique (line 901) | class SelfCritique(ControlLoop):
    method __init__ (line 907) | def __init__(
    method run (line 925) | def run(self, input_text: str) -> Tuple[str, Dict[str, Any]]:
    method display_results (line 973) | def display_results(self, run_details: Dict[str, Any]) -> None:
  class ExternalValidation (line 1013) | class ExternalValidation(ControlLoop):
    method __init__ (line 1019) | def __init__(
    method run (line 1041) | def run(self, input_text: str) -> Tuple[str, Dict[str, Any]]:
    method display_results (line 1125) | def display_results(self, run_details: Dict[str, Any]) -> None:
  function example_sequential_chain (line 1179) | def example_sequential_chain():
  function example_iterative_refiner (line 1222) | def example_iterative_refiner():
  function example_conditional_brancher (line 1250) | def example_conditional_brancher():
  function example_self_critique (line 1289) | def example_self_critique():
  function example_external_validation (line 1323) | def example_external_validation():

FILE: 10_guides_zero_to_hero/04_rag_recipes.py
  class Document (line 97) | class Document:
    method __post_init__ (line 104) | def __post_init__(self):
  function setup_client (line 118) | def setup_client(api_key=None, model=DEFAULT_MODEL):
  function count_tokens (line 142) | def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
  function generate_embedding (line 163) | def generate_embedding(
  function generate_response (line 197) | def generate_response(
  function format_metrics (line 268) | def format_metrics(metrics: Dict[str, Any]) -> str:
  function display_response (line 290) | def display_response(
  function text_to_chunks (line 328) | def text_to_chunks(
  function _approximate_text_to_chunks (line 386) | def _approximate_text_to_chunks(
  function extract_document_batch_embeddings (line 456) | def extract_document_batch_embeddings(
  function similarity_search (line 507) | def similarity_search(
  function create_faiss_index (line 556) | def create_faiss_index(documents: List[Document]) -> Any:
  function faiss_similarity_search (line 590) | def faiss_similarity_search(
  class RAGSystem (line 645) | class RAGSystem:
    method __init__ (line 651) | def __init__(
    method _log (line 694) | def _log(self, message: str) -> None:
    method add_documents (line 704) | def add_documents(self, documents: List[Document]) -> None:
    method add_texts (line 713) | def add_texts(
    method _retrieve (line 736) | def _retrieve(
    method _format_context (line 754) | def _format_context(
    method _create_prompt (line 782) | def _create_prompt(
    method query (line 806) | def query(
    method get_summary_metrics (line 881) | def get_summary_metrics(self) -> Dict[str, Any]:
    method display_query_results (line 902) | def display_query_results(self, details: Dict[str, Any], show_context:...
    method visualize_metrics (line 950) | def visualize_metrics(self) -> None:
  class SimpleRAG (line 1015) | class SimpleRAG(RAGSystem):
    method __init__ (line 1020) | def __init__(self, **kwargs):
    method add_documents (line 1027) | def add_documents(self, documents: List[Document]) -> None:
    method _ensure_documents_embedded (line 1037) | def _ensure_documents_embedded(self) -> None:
    method _retrieve (line 1054) | def _retrieve(
  class ChunkedRAG (line 1093) | class ChunkedRAG(SimpleRAG):
    method __init__ (line 1098) | def __init__(
    method add_documents (line 1123) | def add_documents(self, documents: List[Document]) -> None:
    method _ensure_documents_embedded (line 1159) | def _ensure_documents_embedded(self) -> None:
    method _retrieve (line 1168) | def _retrieve(
  class HybridRAG (line 1216) | class HybridRAG(ChunkedRAG):
    method __init__ (line 1221) | def __init__(
    method _keyword_search (line 1237) | def _keyword_search(
    method _retrieve (line 1271) | def _retrieve(

FILE: 10_guides_zero_to_hero/05_prompt_programs.py
  function setup_client (line 71) | def setup_client(api_key=None, model=DEFAULT_MODEL):
  function count_tokens (line 95) | def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
  function generate_response (line 116) | def generate_response(
  function format_metrics (line 187) | def format_metrics(metrics: Dict[str, Any]) -> str:
  function display_program_output (line 209) | def display_program_output(
  class PromptTemplate (line 274) | class PromptTemplate:
    method __post_init__ (line 281) | def __post_init__(self):
    method format (line 288) | def format(self, **kwargs) -> str:
  class PromptProgram (line 307) | class PromptProgram:
    method __init__ (line 313) | def __init__(
    method _log (line 358) | def _log(self, message: str) -> None:
    method _generate_prompt (line 368) | def _generate_prompt(self, **kwargs) -> str:
    method _call_llm (line 381) | def _call_llm(
    method _process_response (line 416) | def _process_response(self, response: str) -> Any:
    method _update_state (line 429) | def _update_state(
    method execute (line 467) | def execute(self, input_data: Any) -> Any:
    method get_summary_metrics (line 497) | def get_summary_metrics(self) -> Dict[str, Any]:
    method display_execution (line 517) | def display_execution(self) -> None:
    method visualize_metrics (line 527) | def visualize_metrics(self) -> None:
  class MultiStepProgram (line 583) | class MultiStepProgram(PromptProgram):
    method __init__ (line 588) | def __init__(
    method add_operation (line 603) | def add_operation(
    method execute (line 628) | def execute(self, input_data: Any) -> Any:
    method _generate_prompt (line 675) | def _generate_prompt(self, **kwargs) -> str:
  class ReasoningProtocol (line 683) | class ReasoningProtocol(MultiStepProgram):
    method __init__ (line 689) | def __init__(
    method _setup_operations (line 721) | def _setup_operations(self) -> None:
    method _create_reasoning_template (line 754) | def _create_reasoning_template(self) -> str:
    method _create_verification_template (line 769) | def _create_verification_template(self) -> str:
    method _create_correction_template (line 782) | def _create_correction_template(self) -> str:
    method execute (line 797) | def execute(self, problem: str) -> Dict[str, Any]:
  class StepByStepReasoning (line 821) | class StepByStepReasoning(ReasoningProtocol):
    method __init__ (line 827) | def __init__(self, **kwargs):
    method _create_reasoning_template (line 847) | def _create_reasoning_template(self) -> str:
  class ComparativeAnalysis (line 867) | class ComparativeAnalysis(ReasoningProtocol):
    method __init__ (line 873) | def __init__(self, criteria: List[str] = None, **kwargs):
    method _create_reasoning_template (line 902) | def _create_reasoning_template(self) -> str:
  class FieldShell (line 933) | class FieldShell(PromptProgram):
    method __init__ (line 939) | def __init__(
    method _generate_shell_template (line 981) | def _generate_shell_template(self) -> str:
    method _format_input_section (line 1023) | def _format_input_section(self, input_data: Any) -> str:
    method _format_output_section (line 1036) | def _format_output_section(self) -> str:
    method _generate_prompt (line 1046) | def _generate_prompt(self, **kwargs) -> str:
    method _process_response (line 1076) | def _process_response(self, response: str) -> Dict[str, Any]:
  class RecursiveFieldShell (line 1108) | class RecursiveFieldShell(FieldShell):
    method __init__ (line 1114) | def __init__(
    method _add_recursive_capabilities (line 1145) | def _add_recursive_capabilities(self) -> None:
    method _generate_prompt (line 1189) | def _generate_prompt(self, **kwargs) -> str:
  function create_reasoning_shell (line 1214) | def create_reasoning_shell() -> RecursiveFieldShell:
  function create_analysis_shell (line 1270) | def create_analysis_shell() -> RecursiveFieldShell:
  function create_emergence_shell (line 1337) | def create_emergence_shell() -> RecursiveFieldShell:
  function example_step_by_step_reasoning (line 1410) | def example_step_by_step_reasoning():
  function example_comparative_analysis (line 1436) | def example_comparative_analysis():
  function example_field_shell (line 1476) | def example_field_shell():
  function example_emergence_shell (line 1497) | def example_emergence_shell():

FILE: 10_guides_zero_to_hero/06_schema_design.py
  function setup_client (line 79) | def setup_client(api_key=None, model=DEFAULT_MODEL):
  function count_tokens (line 103) | def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
  function generate_response (line 124) | def generate_response(
  function format_metrics (line 195) | def format_metrics(metrics: Dict[str, Any]) -> str:
  function display_schema_example (line 217) | def display_schema_example(
  class JSONSchema (line 251) | class JSONSchema:
    method __init__ (line 257) | def __init__(
    method validate (line 286) | def validate(self, instance: Dict[str, Any]) -> Tuple[bool, Optional[s...
    method generate_example (line 320) | def generate_example(
    method generate_prompt_with_schema (line 388) | def generate_prompt_with_schema(
    method get_validation_stats (line 421) | def get_validation_stats(self) -> Dict[str, Any]:
    method visualize_validation_stats (line 438) | def visualize_validation_stats(self) -> None:
  class SchemaContext (line 482) | class SchemaContext:
    method __init__ (line 488) | def __init__(
    method _log (line 529) | def _log(self, message: str) -> None:
    method query (line 539) | def query(
    method get_summary_metrics (line 674) | def get_summary_metrics(self) -> Dict[str, Any]:
    method display_query_results (line 698) | def display_query_results(self, details: Dict[str, Any], show_prompt: ...
    method visualize_metrics (line 751) | def visualize_metrics(self) -> None:
  class FractalSchema (line 814) | class FractalSchema(JSONSchema):
    method __init__ (line 820) | def __init__(
    method validate (line 848) | def validate(self, instance: Dict[str, Any]) -> Tuple[bool, Optional[s...
    method _analyze_recursion_depth (line 867) | def _analyze_recursion_depth(self, instance: Dict[str, Any], path: str...
    method generate_example (line 919) | def generate_example(
    method get_recursion_metrics (line 1000) | def get_recursion_metrics(self) -> Dict[str, Any]:
    method visualize_recursion_metrics (line 1009) | def visualize_recursion_metrics(self) -> None:
  function example_basic_schema (line 1477) | def example_basic_schema():
  function example_recursive_schema (line 1522) | def example_recursive_schema():
  function example_schema_context (line 1588) | def example_schema_context():
  function example_fractal_repo_schema (line 1641) | def example_fractal_repo_schema():
  function example_protocol_shell_schema (line 1672) | def example_protocol_shell_schema():

FILE: 10_guides_zero_to_hero/07_recursive_patterns.py
  function setup_client (line 72) | def setup_client(api_key=None, model=DEFAULT_MODEL):
  function count_tokens (line 96) | def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
  function generate_response (line 117) | def generate_response(
  function format_metrics (line 188) | def format_metrics(metrics: Dict[str, Any]) -> str:
  function display_recursive_pattern (line 210) | def display_recursive_pattern(
  class RecursivePattern (line 281) | class RecursivePattern:
    method __init__ (line 287) | def __init__(
    method _log (line 335) | def _log(self, message: str) -> None:
    method _generate_recursive_prompt (line 345) | def _generate_recursive_prompt(self, iteration: int, **kwargs) -> str:
    method _call_llm (line 359) | def _call_llm(
    method _process_response (line 394) | def _process_response(self, response: str, iteration: int) -> Any:
    method _update_state (line 408) | def _update_state(
    method _should_continue (line 446) | def _should_continue(self, iteration: int, current_output: Any) -> bool:
    method run (line 460) | def run(self, input_data: Any) -> Tuple[Any, List[Dict[str, Any]]]:
    method get_summary_metrics (line 512) | def get_summary_metrics(self) -> Dict[str, Any]:
    method display_execution (line 532) | def display_execution(self) -> None:
    method visualize_metrics (line 542) | def visualize_metrics(self) -> None:
  class SelfReflection (line 601) | class SelfReflection(RecursivePattern):
    method __init__ (line 607) | def __init__(
    method _generate_recursive_prompt (line 632) | def _generate_recursive_prompt(self, iteration: int, **kwargs) -> str:
    method _process_response (line 659) | def _process_response(self, response: str, iteration: int) -> Dict[str...
    method _should_continue (line 697) | def _should_continue(self, iteration: int, current_output: Any) -> bool:
  class RecursiveBootstrapping (line 727) | class RecursiveBootstrapping(RecursivePattern):
    method __init__ (line 733) | def __init__(
    method _generate_recursive_prompt (line 760) | def _generate_recursive_prompt(self, iteration: int, **kwargs) -> str:
    method _process_response (line 805) | def _process_response(self, response: str, iteration: int) -> Dict[str...
  class SymbolicResidue (line 833) | class SymbolicResidue(RecursivePattern):
    method __init__ (line 839) | def __init__(
    method _generate_recursive_prompt (line 863) | def _generate_recursive_prompt(self, iteration: int, **kwargs) -> str:
    method _process_response (line 915) | def _process_response(self, response: str, iteration: int) -> Dict[str...
    method _should_continue (line 972) | def _should_continue(self, iteration: int, current_output: Any) -> bool:

FILE: 20_templates/control_loop.py
  class ModelInterface (line 39) | class ModelInterface(ABC):
    method generate (line 43) | def generate(self, context: str, max_tokens: int = 1000) -> str:
  class OpenAIInterface (line 47) | class OpenAIInterface(ModelInterface):
    method __init__ (line 50) | def __init__(self, model_name: str, api_key: Optional[str] = None):
    method generate (line 67) | def generate(self, context: str, max_tokens: int = 1000) -> str:
  class AnthropicInterface (line 82) | class AnthropicInterface(ModelInterface):
    method __init__ (line 85) | def __init__(self, model_name: str, api_key: Optional[str] = None):
    method generate (line 101) | def generate(self, context: str, max_tokens: int = 1000) -> str:
  class ContextManager (line 119) | class ContextManager:
    method __init__ (line 122) | def __init__(self,
    method update (line 139) | def update(self, key: str, value: Any) -> None:
    method get_context_str (line 143) | def get_context_str(self, template: Optional[str] = None) -> str:
    method _prune_if_needed (line 193) | def _prune_if_needed(self, context_str: str) -> str:
    method add_to_history (line 219) | def add_to_history(self, entry: Any) -> None:
    method clear_history (line 227) | def clear_history(self) -> None:
  class EvaluationFunction (line 236) | class EvaluationFunction(ABC):
    method evaluate (line 240) | def evaluate(self, response: str, context: Dict[str, Any]) -> Tuple[bo...
  class SimpleKeywordEvaluator (line 253) | class SimpleKeywordEvaluator(EvaluationFunction):
    method __init__ (line 256) | def __init__(self, required_keywords: List[str], forbidden_keywords: L...
    method evaluate (line 267) | def evaluate(self, response: str, context: Dict[str, Any]) -> Tuple[bo...
  class PatternMatchEvaluator (line 309) | class PatternMatchEvaluator(EvaluationFunction):
    method __init__ (line 312) | def __init__(self, required_patterns: List[str], forbidden_patterns: L...
    method evaluate (line 325) | def evaluate(self, response: str, context: Dict[str, Any]) -> Tuple[bo...
  class ModelEvaluator (line 365) | class ModelEvaluator(EvaluationFunction):
    method __init__ (line 368) | def __init__(self, model_interface: ModelInterface, evaluation_prompt_...
    method evaluate (line 379) | def evaluate(self, response: str, context: Dict[str, Any]) -> Tuple[bo...
  class ControlLoop (line 440) | class ControlLoop:
    method __init__ (line 446) | def __init__(self,
    method add_evaluator (line 491) | def add_evaluator(self, evaluator: EvaluationFunction) -> None:
    method run (line 495) | def run(self, input_data: Any = None) -> Dict[str, Any]:
    method reset (line 594) | def reset(self) -> None:
  class NeuralField (line 604) | class NeuralField:
    method __init__ (line 610) | def __init__(self,
    method inject (line 634) | def inject(self, pattern: str, strength: float = 1.0) -> 'NeuralField':
    method _form_attractor (line 679) | def _form_attractor(self, pattern: str) -> str:
    method _process_resonance (line 698) | def _process_resonance(self, trigger_pattern: str) -> 'NeuralField':
    method decay (line 722) | def decay(self) -> 'NeuralField':
    method _calculate_resonance (line 750) | def _calculate_resonance(self, pattern1: str, pattern2: str) -> float:
    method _blend_patterns (line 776) | def _blend_patterns(self, pattern1: str, pattern2: str, blend_ratio: f...
    method measure_field_stability (line 791) | def measure_field_stability(self) -> float:
    method get_context_representation (line 823) | def get_context_representation(self) -> str:
  class NeuralFieldControlLoop (line 853) | class NeuralFieldControlLoop(ControlLoop):
    method __init__ (line 856) | def __init__(self,
    method run (line 897) | def run(self, input_data: Any = None) -> Dict[str, Any]:
    method reset (line 1011) | def reset(self) -> None:
  class ProtocolShell (line 1027) | class ProtocolShell:
    method __init__ (line 1033) | def __init__(self,
    method format (line 1067) | def format(self) -> str:
    method execute (line 1131) | def execute(self, context: Dict[str, Any] = None) -> Dict[str, Any]:
  class ProtocolShellControlLoop (line 1201) | class ProtocolShellControlLoop(ControlLoop):
    method __init__ (line 1204) | def __init__(self,
    method run (line 1246) | def run(self, input_data: Any = None) -> Dict[str, Any]:
    method _extract_output_from_response (line 1383) | def _extract_output_from_response(self, response: str) -> Dict[str, Any]:
    method reset (line 1423) | def reset(self) -> None:
  class RecursiveFieldControlLoop (line 1442) | class RecursiveFieldControlLoop:
    method __init__ (line 1448) | def __init__(self,
    method run (line 1530) | def run(self, input_data: Any = None) -> Dict[str, Any]:
    method _generate_protocol (line 1688) | def _generate_protocol(self) -> ProtocolShell:
    method _evaluate_response (line 1720) | def _evaluate_response(self, response: str) -> List[Dict[str, Any]]:
    method _recursive_improve (line 1756) | def _recursive_improve(self, response: str, evaluations: List[Dict[str...
    method _extract_output_from_response (line 1830) | def _extract_output_from_response(self, response: str) -> Dict[str, Any]:
    method reset (line 1870) | def reset(self) -> None:
  class SymbolicResidue (line 1889) | class SymbolicResidue:
    method __init__ (line 1892) | def __init__(self,
    method interact (line 1914) | def interact(self, target: str, interaction_type: str, strength_delta:...
    method to_dict (line 1926) | def to_dict(self) -> Dict[str, Any]:
    method from_dict (line 1939) | def from_dict(cls, data: Dict[str, Any]) -> 'SymbolicResidue':
  class SymbolicResidueTracker (line 1952) | class SymbolicResidueTracker:
    method __init__ (line 1955) | def __init__(self):
    method surface (line 1960) | def surface(self, content: str, source: str, strength: float = 1.0) ->...
    method integrate (line 1983) | def integrate(self, residue_id: str, target: str, strength_delta: floa...
    method echo (line 2006) | def echo(self, residue_id: str, target: str, strength_delta: float = -...
    method get_active_residues (line 2029) | def get_active_residues(self, min_strength: float = 0.5) -> List[Symbo...
    method get_residues_by_state (line 2033) | def get_residues_by_state(self, state: str) -> List[SymbolicResidue]:
    method to_dict (line 2037) | def to_dict(self) -> Dict[str, Any]:
    method from_dict (line 2045) | def from_dict(cls, data: Dict[str, Any]) -> 'SymbolicResidueTracker':
  class ResidueEnhancedNeuralField (line 2055) | class ResidueEnhancedNeuralField(NeuralField):
    method __init__ (line 2058) | def __init__(self,
    method inject (line 2067) | def inject(self, pattern: str, strength: float = 1.0, source: str = "m...
    method _form_attractor (line 2094) | def _form_attractor(self, pattern: str) -> str:
    method decay (line 2106) | def decay(self) -> 'ResidueEnhancedNeuralField':
    method get_context_representation (line 2120) | def get_context_representation(self) -> str:
  function basic_control_loop_example (line 2158) | def basic_control_loop_example():
  function neural_field_example (line 2191) | def neural_field_example():
  function protocol_shell_example (line 2232) | def protocol_shell_example():
  function recursive_field_example (line 2303) | def recursive_field_example():

FILE: 20_templates/field_protocol_shells.py
  class ProtocolParser (line 56) | class ProtocolParser:
    method parse_shell (line 60) | def parse_shell(shell_content: str) -> Dict[str, Any]:
    method _parse_object_section (line 107) | def _parse_object_section(section_content: str) -> Dict[str, Any]:
    method _parse_process_section (line 120) | def _parse_process_section(section_content: str) -> List[str]:
    method serialize_shell (line 129) | def serialize_shell(protocol_dict: Dict[str, Any]) -> str:
  class ProtocolValidator (line 185) | class ProtocolValidator:
    method validate (line 189) | def validate(protocol_dict: Dict[str, Any], schema_path: str) -> bool:
  class ProtocolShell (line 210) | class ProtocolShell:
    method __init__ (line 213) | def __init__(self, protocol_dict: Dict[str, Any]):
    method from_file (line 232) | def from_file(cls, file_path: str) -> 'ProtocolShell':
    method from_string (line 249) | def from_string(cls, shell_content: str) -> 'ProtocolShell':
    method _init_operation_registry (line 262) | def _init_operation_registry(self):
    method _extract_operation_names (line 272) | def _extract_operation_names(self) -> List[str]:
    method _operation_to_method_name (line 282) | def _operation_to_method_name(self, operation_name: str) -> str:
    method _extract_operation_params (line 287) | def _extract_operation_params(self, operation: str) -> Dict[str, str]:
    method execute (line 319) | def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
    method _validate_input (line 364) | def _validate_input(self, input_data: Dict[str, Any]) -> None:
    method _prepare_output (line 383) | def _prepare_output(self, execution_state: Dict[str, Any]) -> Dict[str...
  class AttractorCoEmergeProtocol (line 406) | class AttractorCoEmergeProtocol(ProtocolShell):
    method attractor_scan (line 409) | def attractor_scan(self, state: Dict[str, Any], detect: str = 'attract...
    method residue_surface (line 438) | def residue_surface(self, state: Dict[str, Any], mode: str = 'recursive',
    method co_emergence_algorithms (line 470) | def co_emergence_algorithms(self, state: Dict[str, Any],
    method field_audit (line 501) | def field_audit(self, state: Dict[str, Any],
    method agency_self_prompt (line 533) | def agency_self_prompt(self, state: Dict[str, Any],
    method integration_protocol (line 568) | def integration_protocol(self, state: Dict[str, Any],
    method boundary_collapse (line 602) | def boundary_collapse(self, state: Dict[str, Any],
    method _detect_attractors (line 634) | def _detect_attractors(self, field: Field, detect_type: str) -> List[A...
    method _filter_attractors (line 639) | def _filter_attractors(self, attractors: List[Attractor], filter_by: s...
    method _detect_residue (line 644) | def _detect_residue(self, field: Field, mode: str) -> List[Residue]:
    method _integrate_residue (line 649) | def _integrate_residue(self, field: Field, residues: List[Residue]) ->...
    method _apply_harmonic_integration (line 654) | def _apply_harmonic_integration(self, field: Field, attractors: List[A...
    method _apply_boundary_dissolution (line 659) | def _apply_boundary_dissolution(self, field: Field, attractors: List[A...
    method _apply_resonance_amplification (line 664) | def _apply_resonance_amplification(self, field: Field, attractors: Lis...
    method _identify_attractor_basins (line 669) | def _identify_attractor_basins(self, field: Field) -> List[Dict[str, A...
    method _calculate_field_coherence (line 674) | def _calculate_field_coherence(self, field: Field) -> float:
    method _detect_emergent_patterns (line 679) | def _detect_emergent_patterns(self, field: Field) -> List[Dict[str, An...
    method _generate_cycle_prompt (line 684) | def _generate_cycle_prompt(self, field: Field, audit_results: Dict[str...
    method _generate_pattern_prompt (line 689) | def _generate_pattern_prompt(self, patterns: List[Dict[str, Any]]) -> ...
    method _generate_coherence_prompt (line 694) | def _generate_coherence_prompt(self, coherence: float) -> str:
    method _detect_co_emergent_attractors (line 699) | def _detect_co_emergent_attractors(self, field: Field) -> List[Attract...
    method _integrate_attractors (line 704) | def _integrate_attractors(self, field: Field, attractors: List[Attract...
    method _collapse_all_boundaries (line 709) | def _collapse_all_boundaries(self, field: Field) -> Field:
    method _collapse_selected_boundaries (line 714) | def _collapse_selected_boundaries(self, field: Field) -> Field:
    method _create_gradient_boundaries (line 719) | def _create_gradient_boundaries(self, field: Field) -> Field:
  class RecursiveEmergenceProtocol (line 725) | class RecursiveEmergenceProtocol(ProtocolShell):
    method self_prompt_loop (line 728) | def self_prompt_loop(self, state: Dict[str, Any],
    method agency_activate (line 756) | def agency_activate(self, state: Dict[str, Any],
    method residue_compress (line 786) | def residue_compress(self, state: Dict[str, Any],
    method boundary_collapse (line 817) | def boundary_collapse(self, state: Dict[str, Any],
    method emergence_detect (line 847) | def emergence_detect(self, state: Dict[str, Any],
    method field_evolution (line 875) | def field_evolution(self, state: Dict[str, Any],
    method halt_check (line 903) | def halt_check(self, state: Dict[str, Any],
    method _create_trigger (line 941) | def _create_trigger(self, trigger_condition: str) -> Dict[str, Any]:
    method _create_self_prompt_mechanism (line 946) | def _create_self_prompt_mechanism(self, trigger: Dict[str, Any]) -> Di...
    method _integrate_mechanism (line 951) | def _integrate_mechanism(self, field: Field, mechanism: Dict[str, Any]...
    method _create_agency_mechanisms (line 956) | def _create_agency_mechanisms(self, agency_level: float) -> List[Dict[...
    method _integrate_agency (line 965) | def _integrate_agency(self, field: Field, mechanisms: List[Dict[str, A...
    method _detect_residue (line 971) | def _detect_residue(self, field: Field) -> List[Residue]:
    method _compress_residue (line 976) | def _compress_residue(self, residue: List[Residue]) -> List[Residue]:
    method _integrate_residue (line 981) | def _integrate_residue(self, field: Field, residue: List[Residue]) -> ...
    method _monitor_field (line 986) | def _monitor_field(self, field: Field, monitor: str) -> Dict[str, Any]:
    method _should_collapse_boundaries (line 996) | def _should_collapse_boundaries(self, monitoring_results: Dict[str, An...
    method _identify_collapse_boundaries (line 1001) | def _identify_collapse_boundaries(self, field: Field,
    method _collapse_boundaries (line 1007) | def _collapse_boundaries(self, field: Field,
    method _create_pattern_detector (line 1013) | def _create_pattern_detector(self, pattern: str) -> Dict[str, Any]:
    method _scan_for_patterns (line 1018) | def _scan_for_patterns(self, field: Field,
    method _analyze_patterns (line 1024) | def _analyze_patterns(self, patterns: List[Dict[str, Any]]) -> Dict[st...
    method _create_evolution_strategy (line 1033) | def _create_evolution_strategy(self, strategy: str) -> Dict[str, Any]:
    method _apply_evolution_strategy (line 1038) | def _apply_evolution_strategy(self, field: Field,
    method _measure_evolution (line 1044) | def _measure_evolution(self, field: Field) -> Dict[str, Any]:
    method _measure_convergence (line 1053) | def _measure_convergence(self, field: Field) -> float:
    method _determine_halt_reason (line 1058) | def _determine_halt_reason(self, should_halt: bool, cycle_count: int,

FILE: 20_templates/field_resonance_measure.py
  class ResonanceMeasurer (line 42) | class ResonanceMeasurer:
    method __init__ (line 45) | def __init__(self, method: str = "cosine", threshold: float = 0.2, amp...
    method _initialize_embedding_model (line 67) | def _initialize_embedding_model(self):
    method measure (line 77) | def measure(self, pattern1: str, pattern2: str) -> float:
    method _cosine_similarity (line 101) | def _cosine_similarity(self, pattern1: str, pattern2: str) -> float:
    method _word_overlap (line 130) | def _word_overlap(self, pattern1: str, pattern2: str) -> float:
    method _embedding_similarity (line 152) | def _embedding_similarity(self, pattern1: str, pattern2: str) -> float:
    method _get_word_freq (line 173) | def _get_word_freq(self, text: str) -> Dict[str, int]:
  class CoherenceMeasurer (line 185) | class CoherenceMeasurer:
    method __init__ (line 188) | def __init__(self, method: str = "attractor_alignment", sampling: str ...
    method measure (line 202) | def measure(self, field: Any) -> float:
    method _pairwise_coherence (line 222) | def _pairwise_coherence(self, field: Any) -> float:
    method _attractor_alignment (line 250) | def _attractor_alignment(self, field: Any) -> float:
    method _entropy_coherence (line 283) | def _entropy_coherence(self, field: Any) -> float:
    method _sample_patterns (line 316) | def _sample_patterns(self, field: Any) -> List[Tuple[str, float]]:
    method _get_attractors (line 348) | def _get_attractors(self, field: Any) -> List[Tuple[str, float]]:
  class StabilityMeasurer (line 367) | class StabilityMeasurer:
    method __init__ (line 370) | def __init__(self, attractor_weight: float = 0.6, organization_weight:...
    method measure (line 382) | def measure(self, field: Any) -> float:
    method _get_attractors (line 409) | def _get_attractors(self, field: Any) -> List[Tuple[str, float]]:
  class FieldResonanceMeasurer (line 428) | class FieldResonanceMeasurer:
    method __init__ (line 434) | def __init__(self, config_path: Optional[str] = None):
    method _load_config (line 461) | def _load_config(self, config_path: Optional[str]) -> Dict[str, Any]:
    method measure_resonance (line 489) | def measure_resonance(self, pattern1: str, pattern2: str) -> float:
    method measure_coherence (line 502) | def measure_coherence(self, field: Any) -> float:
    method measure_stability (line 514) | def measure_stability(self, field: Any) -> float:
    method get_field_metrics (line 526) | def get_field_metrics(self, field: Any) -> Dict[str, float]:
    method _get_attractors (line 575) | def _get_attractors(self, field: Any) -> List[Tuple[str, float]]:
    method _get_patterns (line 590) | def _get_patterns(self, field: Any) -> List[Tuple[str, float]]:
    method _calculate_entropy (line 604) | def _calculate_entropy(self, field: Any) -> float:
    method visualize_field (line 642) | def visualize_field(self, field: Any, format: str = "ascii") -> str:
    method _visualize_ascii (line 660) | def _visualize_ascii(self, field: Any) -> str:
    method _visualize_text (line 713) | def _visualize_text(self, field: Any) -> str:
    method _visualize_json (line 749) | def _visualize_json(self, field: Any) -> str:
  class FieldAnalyzer (line 800) | class FieldAnalyzer:
    method __init__ (line 803) | def __init__(self, measurer: Optional[FieldResonanceMeasurer] = None):
    method analyze_field (line 812) | def analyze_field(self, field: Any) -> Dict[str, Any]:
    method _get_attractors (line 849) | def _get_attractors(self, field: Any) -> List[Tuple[str, float]]:
    method _get_patterns (line 864) | def _get_patterns(self, field: Any) -> List[Tuple[str, float]]:
    method _analyze_attractors (line 878) | def _analyze_attractors(self, attractors: List[Tuple[str, float]]) -> ...
    method _analyze_patterns (line 941) | def _analyze_patterns(self, patterns: List[Tuple[str, float]],
    method _analyze_evolution_potential (line 1026) | def _analyze_evolution_potential(self, field: Any, metrics: Dict[str, ...
    method _generate_recommendations (line 1079) | def _generate_recommendations(self, metrics: Dict[str, float],
  function measure_field_resonance_example (line 1140) | def measure_field_resonance_example():

FILE: 20_templates/prompt_program_template.py
  class StepType (line 50) | class StepType(Enum):
  class ProgramStep (line 59) | class ProgramStep:
    method __init__ (line 62) | def __init__(self,
    method add_substep (line 79) | def add_substep(self, substep: 'ProgramStep') -> None:
    method format (line 83) | def format(self, index: Optional[int] = None, indent: int = 0) -> str:
  class PromptProgram (line 125) | class PromptProgram:
    method __init__ (line 131) | def __init__(self,
    method add_step (line 157) | def add_step(self, content: str, step_type: StepType = StepType.INSTRU...
    method add_condition (line 174) | def add_condition(self, condition: str, true_step: str,
    method add_loop (line 202) | def add_loop(self, variable: str, iterable: str,
    method add_variable (line 225) | def add_variable(self, name: str, value: str) -> ProgramStep:
    method add_function (line 238) | def add_function(self, name: str, params: str) -> ProgramStep:
    method add_error_handler (line 251) | def add_error_handler(self, handler: str) -> ProgramStep:
    method format (line 265) | def format(self) -> str:
    method execute (line 298) | def execute(self, input_data: str, max_tokens: int = 1000) -> str:
    method _create_standard_prompt (line 399) | def _create_standard_prompt(self, program_str: str, input_data: str) -...
    method execute_with_trace (line 418) | def execute_with_trace(self, input_data: str, max_tokens: int = 1000) ...
    method _parse_execution_trace (line 441) | def _parse_execution_trace(self, result: str) -> List[Dict[str, Any]]:
  class NeuralFieldProgram (line 479) | class NeuralFieldProgram(PromptProgram):
    method __init__ (line 482) | def __init__(self,
    method _create_basic_neural_field (line 520) | def _create_basic_neural_field(self, params: Dict[str, Any]) -> Any:
    method add_resonance_step (line 572) | def add_resonance_step(self, description: str, patterns: List[str]) ->...
    method add_attractor (line 595) | def add_attractor(self, pattern: str, strength: float = 1.0) -> None:
    method execute (line 622) | def execute(self, input_data: str, max_tokens: int = 1000) -> str:
    method _measure_field_metrics (line 660) | def _measure_field_metrics(self) -> Dict[str, float]:
  class ProtocolShellProgram (line 698) | class ProtocolShellProgram(PromptProgram):
    method __init__ (line 701) | def __init__(self,
    method _generate_steps_from_protocol (line 725) | def _generate_steps_from_protocol(self) -> None:
    method format (line 762) | def format(self) -> str:
    method _format_protocol (line 772) | def _format_protocol(self) -> str:
    method execute (line 850) | def execute(self, input_data: str, max_tokens: int = 1000) -> str:
    method extract_output (line 871) | def extract_output(self, response: str) -> Dict[str, Any]:
  function basic_program_example (line 939) | def basic_program_example():
  function neural_field_program_example (line 992) | def neural_field_program_example():
  function protocol_shell_program_example (line 1068) | def protocol_shell_program_example():

FILE: 20_templates/recursive_context.py
  class ContextResult (line 25) | class ContextResult:
    method __post_init__ (line 32) | def __post_init__(self):
  class ModelProvider (line 40) | class ModelProvider(Protocol):
    method generate (line 43) | def generate(self, prompt: str, max_tokens: int = 1000) -> str:
  class SecurityValidator (line 48) | class SecurityValidator:
    method validate_input (line 68) | def validate_input(cls, text: str, max_length: int = 10000) -> str:
    method sanitize_output (line 86) | def sanitize_output(cls, text: str) -> str:
  class RateLimiter (line 98) | class RateLimiter:
    method __init__ (line 101) | def __init__(self, requests_per_minute: int = 60):
    method allow_request (line 106) | def allow_request(self) -> bool:
  function rate_limited (line 124) | def rate_limited(limiter: RateLimiter):
  class RecursiveContextFramework (line 136) | class RecursiveContextFramework:
    method __init__ (line 143) | def __init__(self, model_provider: Optional[ModelProvider] = None):
    method _create_default_provider (line 163) | def _create_default_provider(self) -> ModelProvider:
    method _calculate_improvement_score (line 174) | def _calculate_improvement_score(self, original: str, improved: str) -...
    method improve (line 190) | def improve(self,
    method batch_improve (line 254) | def batch_improve(self, contents: List[str], **kwargs) -> List[Context...
  class SecureAnthropicProvider (line 277) | class SecureAnthropicProvider:
    method __init__ (line 280) | def __init__(self, api_key: str):
    method generate (line 285) | def generate(self, prompt: str, max_tokens: int = 1000) -> str:

FILE: 20_templates/scoring_functions.py
  function tokenize (line 46) | def tokenize(text: str) -> List[str]:
  function count_tokens (line 62) | def count_tokens(text: str) -> int:
  function extract_sentences (line 77) | def extract_sentences(text: str) -> List[str]:
  function jaccard_similarity (line 93) | def jaccard_similarity(set1: Set[str], set2: Set[str]) -> float:
  function cosine_similarity (line 112) | def cosine_similarity(vec1: Dict[str, int], vec2: Dict[str, int]) -> float:
  function get_word_frequency (line 142) | def get_word_frequency(text: str) -> Dict[str, int]:
  function score_relevance (line 159) | def score_relevance(response: str, query: str, method: str = "cosine") -...
  function score_coherence (line 188) | def score_coherence(text: str) -> float:
  function score_comprehensiveness (line 241) | def score_comprehensiveness(response: str, reference: Optional[str] = No...
  function score_conciseness (line 291) | def score_conciseness(response: str, reference: Optional[str] = None, ke...
  function score_accuracy (line 373) | def score_accuracy(response: str, reference: Optional[str] = None, facts...
  function score_token_efficiency (line 437) | def score_token_efficiency(response: str, max_tokens: int = 500) -> float:
  function score_field_resonance (line 483) | def score_field_resonance(response: str, field: Any) -> float:
  function score_field_coherence (line 531) | def score_field_coherence(response: str, field: Any) -> float:
  function score_field_stability_impact (line 584) | def score_field_stability_impact(response: str, field: Any, before_state...
  function _get_field_attractors (line 620) | def _get_field_attractors(field: Any) -> List[Tuple[str, float]]:
  function _get_field_patterns (line 633) | def _get_field_patterns(field: Any) -> List[Tuple[str, float]]:
  function score_protocol_adherence (line 649) | def score_protocol_adherence(response: str, protocol: Any) -> float:
  function _extract_protocol_steps (line 718) | def _extract_protocol_steps(protocol: Any) -> List[Dict[str, Any]]:
  function _extract_step_keywords (line 733) | def _extract_step_keywords(step: Dict[str, Any]) -> List[str]:
  function score_protocol_output_match (line 748) | def score_protocol_output_match(response: str, protocol: Any) -> float:
  function _extract_protocol_output (line 810) | def _extract_protocol_output(protocol: Any) -> Dict[str, Any]:
  function _extract_structured_output (line 825) | def _extract_structured_output(response: str) -> Dict[str, Any]:
  function score_response (line 859) | def score_response(response: str, query: str, context: Optional[Dict[str...
  function basic_scoring_example (line 931) | def basic_scoring_example():

FILE: cognitive-tools/cognitive-architectures/architecture-examples.py
  function generate_id (line 23) | def generate_id() -> str:
  function get_current_timestamp (line 27) | def get_current_timestamp() -> str:
  function llm_executor (line 32) | def llm_executor(prompt: str) -> str:
  function execute_protocol (line 79) | def execute_protocol(protocol: str) -> Dict[str, Any]:
  class ProtocolShell (line 114) | class ProtocolShell:
    method __init__ (line 117) | def __init__(self, intent: str, input_params: Dict[str, Any],
    method to_prompt (line 134) | def to_prompt(self) -> str:
    method _format_value (line 171) | def _format_value(self, v: Any) -> str:
    method execute (line 184) | def execute(self) -> Dict[str, Any]:
  class SemanticField (line 209) | class SemanticField:
    method __init__ (line 212) | def __init__(self, dimensions: int = 128, name: str = "generic_field"):
    method add_attractor (line 228) | def add_attractor(self, concept: str, position: np.ndarray = None,
    method _update_field_state (line 268) | def _update_field_state(self):
    method calculate_trajectory (line 295) | def calculate_trajectory(self, start_state: np.ndarray, steps: int = 1...
    method detect_basins (line 348) | def detect_basins(self) -> List[Dict[str, Any]]:
    method visualize (line 372) | def visualize(self, show_attractors: bool = True, show_trajectories: b...
  class CognitiveToolsLibrary (line 452) | class CognitiveToolsLibrary:
    method understand_question (line 456) | def understand_question(question: str, domain: str = None) -> Dict[str...
    method decompose_problem (line 494) | def decompose_problem(problem: Dict[str, Any]) -> Dict[str, Any]:
    method step_by_step (line 526) | def step_by_step(problem: Dict[str, Any], approach: str) -> Dict[str, ...
    method verify_solution (line 562) | def verify_solution(problem: Dict[str, Any], solution: Dict[str, Any])...
  class MetaCognitiveController (line 598) | class MetaCognitiveController:
    method __init__ (line 601) | def __init__(self):
    method monitor (line 611) | def monitor(self, phase_results: Dict[str, Any]) -> Dict[str, Any]:
    method regulate (line 653) | def regulate(self, monitoring_assessment: Dict[str, Any]) -> Dict[str,...
    method reflect (line 694) | def reflect(self, complete_process: Dict[str, Any]) -> Dict[str, Any]:
  class SolverArchitecture (line 735) | class SolverArchitecture:
    method __init__ (line 738) | def __init__(self):
    method solve (line 745) | def solve(self, problem: str, domain: str = None) -> Dict[str, Any]:
    method update_field_from_solution (line 846) | def update_field_from_solution(self, understanding: Dict[str, Any], so...
    method visualize_solution_process (line 871) | def visualize_solution_process(self, session_index: int = -1) -> plt.F...
  function solver_example_math_problem (line 1043) | def solver_example_math_problem():
  function solver_example_algorithmic_design (line 1083) | def solver_example_algorithmic_design():
  function solver_example_with_field_theory (line 1122) | def solver_example_with_field_theory():
  class StudentKnowledgeModel (line 1198) | class StudentKnowledgeModel:
    method __init__ (line 1201) | def __init__(self, dimensions: int = 64):
    method update_knowledge_state (line 1220) | def update_knowledge_state(self, assessment_results: Dict[str, Any]) -...
    method get_knowledge_state (line 1324) | def get_knowledge_state(self, concept: str = None) -> Dict[str, Any]:
    method get_metacognitive_level (line 1356) | def get_metacognitive_level(self) -> Dict[str, Any]:
    method update_metacognitive_profile (line 1373) | def update_metacognitive_profile(self, meta_analysis: Dict[str, Any]):
  class ContentModel (line 1386) | class ContentModel:
    method __init__ (line 1389) | def __init__(self, domain: str):
    method add_concept (line 1406) | def add_concept(self, concept_id: str, concept_data: Dict[str, Any]) -...
    method get_concept (line 1475) | def get_concept(self, concept_id: str) -> Dict[str, Any]:
    method get_related_concepts (line 1490) | def get_related_concepts(self, concept_id: str) -> List[str]:
    method get_learning_sequence (line 1510) | def get_learning_sequence(self, concepts: List[str], student_model: St...
  class PedagogicalModel (line 1576) | class PedagogicalModel:
    method __init__ (line 1579) | def __init__(self):
    method _initialize_tools (line 1586) | def _initialize_tools(self) -> Dict[str, callable]:
    method _explanation_tool (line 1599) | def _explanation_tool(self, concept: str, student_model: StudentKnowle...
    method _practice_tool (line 1630) | def _practice_tool(self, concept: str, student_model: StudentKnowledge...
    method _assessment_tool (line 1671) | def _assessment_tool(self, concept: str, student_model: StudentKnowled...
    method _feedback_tool (line 1709) | def _feedback_tool(self, performance: Dict[str, Any], student_model: S...
    method _scaffolding_tool (line 1739) | def _scaffolding_tool(self, task: Dict[str, Any], student_model: Stude...
    method _misconception_detector (line 1769) | def _misconception_detector(self, responses: Dict[str, Any], content_m...
    method _goal_assessment (line 1797) | def _goal_assessment(self, learning_goal: str, student_model: StudentK...
    method _reflection_prompt (line 1830) | def _reflection_prompt(self, learning_experience: Dict[str, Any], stud...
    method select_strategy (line 1860) | def select_strategy(self, learning_goal: str, student_model: StudentKn...
    method execute_strategy (line 1948) | def execute_strategy(self, strategy: Dict[str, Any], student_model: St...
    method recommend_next_steps (line 1999) | def recommend_next_steps(self, student_model: StudentKnowledgeModel, c...
    method modulate_field (line 2008) | def modulate_field(self, current_field: Dict[str, Any], target_state: ...
  class TutorArchitecture (line 2045) | class TutorArchitecture:
    method __init__ (line 2048) | def __init__(self, domain: str = "general"):
    method initialize_content (line 2061) | def initialize_content(self):
    method teach_concept (line 2102) | def teach_concept(self, concept_id: str, learning_goal: str = "mastery...
    method update_field_from_learning (line 2163) | def update_field_from_learning(self, concept_id: str, learning_experie...
    method visualize_learning_process (line 2200) | def visualize_learning_process(self, session_index: int = -1) -> plt.F...
  function tutor_example_math_concept (line 2366) | def tutor_example_math_concept():
  function tutor_example_adaptive_scaffolding (line 2404) | def tutor_example_adaptive_scaffolding():
  function tutor_example_misconception_remediation (line 2467) | def tutor_example_misconception_remediation():
  class ResearchKnowledgeField (line 2513) | class ResearchKnowledgeField(SemanticField):
    method __init__ (line 2516) | def __init__(self, domain: str, dimensions: int = 128):
    method add_literature (line 2532) | def add_literature(self, papers: List[Dict[str, Any]]) -> Dict[str, Any]:
    method identify_research_opportunities (line 2614) | def identify_research_opportunities(self, research_interests: List[str],
    method detect_contradictions (line 2673) | def detect_contradictions(self) -> List[Dict[str, Any]]:
    method visualize_research_landscape (line 2682) | def visualize_research_landscape(self, focus: str = "literature", incl...
  class ResearchInquiryModel (line 2725) | class ResearchInquiryModel:
    method __init__ (line 2728) | def __init__(self):
    method develop_research_question (line 2735) | def develop_research_question(self, knowledge_field: ResearchKnowledge...
    method develop_hypothesis (line 2794) | def develop_hypothesis(self, knowledge_field: ResearchKnowledgeField,
    method refine_hypothesis (line 2867) | def refine_hypothesis(self, hypothesis_id: str, refinement_data: Dict[...
  class ResearchSynthesisModel (line 2959) | class ResearchSynthesisModel:
    method __init__ (line 2962) | def __init__(self):
    method synthesize_findings (line 2970) | def synthesize_findings(self, knowledge_field: ResearchKnowledgeField,...
    method develop_theoretical_model (line 3047) | def develop_theoretical_model(self, knowledge_field: ResearchKnowledge...
  class ResearchCommunicationModel (line 3118) | class ResearchCommunicationModel:
    method __init__ (line 3121) | def __init__(self):
    method develop_research_narrative (line 3128) | def develop_research_narrative(self, knowledge_field: ResearchKnowledg...
    method create_research_visualization (line 3196) | def create_research_visualization(self, knowledge_field: ResearchKnowl...
  class ResearchArchitecture (line 3257) | class ResearchArchitecture:
    method __init__ (line 3260) | def __init__(self, domain: str = "general"):
    method initialize_literature (line 3277) | def initialize_literature(self, papers: List[Dict[str, Any]]):
    method conduct_literature_review (line 3286) | def conduct_literature_review(self, research_question: str, depth: str...
    method develop_research_idea (line 3431) | def develop_research_idea(self, research_interest: str,
    method analyze_interdisciplinary_potential (line 3531) | def analyze_interdisciplinary_potential(self, primary_domain: str,
    method visualize_research_process (line 3645) | def visualize_research_process(self, session_index: int = -1) -> plt.F...
  function research_example_literature_review (line 3954) | def research_example_literature_review():
  function research_example_hypothesis_development (line 4025) | def research_example_hypothesis_development():
  function research_example_interdisciplinary_research (line 4130) | def research_example_interdisciplinary_research():
  function cross_architecture_integration_example (line 4223) | def cross_architecture_integration_example():
  function main (line 4357) | def main():

FILE: cognitive-tools/cognitive-programs/program-examples.py
  class ExampleConfig (line 43) | class ExampleConfig:
  function print_header (line 63) | def print_header(title: str, color: str = 'header'):
  function print_example (line 72) | def print_example(title: str, program_output: str, description: str = ""):
  function simulate_llm_execution (line 82) | def simulate_llm_execution(prompt: str, complexity_penalty: float = 0.1)...
  function run_cognitive_tools_demo (line 106) | def run_cognitive_tools_demo():
  function run_symbolic_processing_demo (line 215) | def run_symbolic_processing_demo():
  function run_quantum_semantic_demo (line 309) | def run_quantum_semantic_demo():
  function run_memory_reasoning_demo (line 428) | def run_memory_reasoning_demo():
  function run_field_dynamics_demo (line 548) | def run_field_dynamics_demo():
  function run_progressive_complexity_demo (line 693) | def run_progressive_complexity_demo():
  function run_unified_architecture_demo (line 826) | def run_unified_architecture_demo():
  function run_practical_applications_demo (line 989) | def run_practical_applications_demo():
  function create_interactive_examples (line 1108) | def create_interactive_examples():
  function visualize_performance_metrics (line 1160) | def visualize_performance_metrics():
  function run_all_examples (line 1241) | def run_all_examples():
  function quick_start (line 1303) | def quick_start():
  function get_example_by_research_stream (line 1343) | def get_example_by_research_stream(stream: str):

FILE: cognitive-tools/cognitive-programs/program-library.py
  class ComplexityLevel (line 28) | class ComplexityLevel(Enum):
  class ProcessingStage (line 38) | class ProcessingStage(Enum):
  class CognitiveContext (line 46) | class CognitiveContext:
  class ProgramResult (line 57) | class ProgramResult:
  class CognitiveToolsEngine (line 69) | class CognitiveToolsEngine:
    method cognitive_tool_template (line 76) | def cognitive_tool_template(
    method problem_analyzer_tool (line 118) | def problem_analyzer_tool(problem: str, domain: str = "general") -> str:
    method solution_validator_tool (line 125) | def solution_validator_tool(solution: str, problem: str) -> str:
  class SymbolicProcessingEngine (line 154) | class SymbolicProcessingEngine:
    method three_stage_processor (line 161) | def three_stage_processor(
    method symbolic_abstractor (line 219) | def symbolic_abstractor(content: str, abstraction_level: str = "high")...
  class QuantumSemanticEngine (line 248) | class QuantumSemanticEngine:
    method meaning_generator (line 255) | def meaning_generator(
    method observer_dependent_interpreter (line 306) | def observer_dependent_interpreter(
  class MemoryReasoningEngine (line 359) | class MemoryReasoningEngine:
    method mem1_consolidator (line 366) | def mem1_consolidator(
    method long_horizon_reasoner (line 422) | def long_horizon_reasoner(
  class FieldDynamicsEngine (line 465) | class FieldDynamicsEngine:
    method field_generator (line 472) | def field_generator(
    method attractor_detector (line 526) | def attractor_detector(
  class ProgressiveComplexityEngine (line 580) | class ProgressiveComplexityEngine:
    method complexity_orchestrator (line 587) | def complexity_orchestrator(
    method adaptive_complexity_manager (line 664) | def adaptive_complexity_manager(
  class UnifiedCognitivePrograms (line 721) | class UnifiedCognitivePrograms:
    method __init__ (line 727) | def __init__(self):
    method integrated_reasoning_program (line 735) | def integrated_reasoning_program(
    method meta_cognitive_program (line 843) | def meta_cognitive_program(
  class ProgramFactory (line 893) | class ProgramFactory:
    method __init__ (line 896) | def __init__(self):
    method create_program (line 899) | def create_program(
    method _create_problem_solver (line 920) | def _create_problem_solver(self, complexity: ComplexityLevel, **kwargs...
    method _create_research_assistant (line 938) | def _create_research_assistant(self, complexity: ComplexityLevel, **kw...
    method _create_creative_generator (line 967) | def _create_creative_generator(self, complexity: ComplexityLevel, **kw...
    method _create_analytical_reasoner (line 1000) | def _create_analytical_reasoner(self, complexity: ComplexityLevel, **k...
    method _create_collaborative_agent (line 1033) | def _create_collaborative_agent(self, complexity: ComplexityLevel, **k...
    method _create_meta_learner (line 1067) | def _create_meta_learner(self, complexity: ComplexityLevel, **kwargs) ...
  function demonstrate_program_library (line 1098) | def demonstrate_program_library():