# LV Framework Configuration
# =========================
# Central configuration file for Lotka-Volterra Ecosystem Intelligence Framework
# Database Configuration
[database.neo4j]
uri = "bolt://localhost:7687"
username = "neo4j"
password = "lv_password_2024"
database = "neo4j"
max_connection_lifetime = 3600
max_connection_pool_size = 50
connection_acquisition_timeout = 60
[database.qdrant]
host = "localhost"
port = 6333
grpc_port = 6334
prefer_grpc = false
timeout = 60
# Optional API key for production
# api_key = "your_qdrant_api_key"
# LV Ecosystem Parameters
[lv_ecosystem]
# Mathematical parameters
max_iterations = 10
damping_factor = 0.15
convergence_threshold = 1e-6
# Entropy thresholds for behavioral adaptation
entropy_low_threshold = 0.3
entropy_high_threshold = 0.6
# Default embedding model
embedder_model = "all-MiniLM-L6-v2"
# Alternative models:
# embedder_model = "all-mpnet-base-v2" # Higher quality, slower
# embedder_model = "all-distilroberta-v1" # Balanced performance
# Growth Rate Weights by Entropy Level
[lv_ecosystem.weights.low_entropy]
# Precision mode: High confidence, factual optimization
quality = 0.9
novelty = 0.0
bias = 0.05
cost = 0.05
[lv_ecosystem.weights.medium_entropy]
# Balanced mode: Exploration + exploitation
quality = 0.6
novelty = 0.3
bias = 0.05
cost = 0.05
[lv_ecosystem.weights.high_entropy]
# Creativity mode: Maximum diversity
quality = 0.2
novelty = 0.7
bias = 0.05
cost = 0.05
# Alpha Matrix Weights (Interaction Matrix)
[lv_ecosystem.alpha_weights]
# Relative importance of different competition factors
semantic = 0.6 # Semantic similarity competition
niche = 0.3 # Niche-based inhibition
task = 0.1 # Task-specific penalties
# Niche Inhibition Values
[lv_ecosystem.niche_inhibition]
same_niche_high_similarity = -1.5 # Strong competition
same_niche_low_similarity = -0.7 # Moderate competition
different_niches = 0.0 # No competition
preferred_to_nonpreferred = -1.2 # Dominance effect
nonpreferred_to_preferred = 0.0 # Asymmetric exclusion
# Quality Assessment Configuration
[quality_assessment]
# Grammar and coherence weights
grammar_weight = 0.4
coherence_weight = 0.3
factual_weight = 0.3
# Quality thresholds
minimum_quality_threshold = 0.3
high_quality_threshold = 0.8
# Novelty Assessment Configuration
[novelty_assessment]
# Similarity search parameters
similarity_search_limit = 5
novelty_threshold = 0.7
historical_window_size = 1000
# Bias Mitigation Configuration
[bias_mitigation]
# Bias detection sensitivity
bias_detection_threshold = 0.6
fairness_classifier_enabled = true
# Protected attributes for bias detection
protected_attributes = [
"gender",
"race",
"age",
"religion",
"political_affiliation"
]
# Performance Configuration
[performance]
# Caching settings
enable_embedding_cache = true
cache_size_limit = 10000
cache_ttl_seconds = 3600
# Parallel processing
max_concurrent_candidates = 20
enable_gpu_acceleration = true
gpu_batch_size = 32
# Memory management
max_memory_usage_mb = 2048
garbage_collection_threshold = 0.8
# Logging Configuration
[logging]
level = "INFO"
# Levels: DEBUG, INFO, WARNING, ERROR, CRITICAL
# Log format
format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
# File logging
enable_file_logging = true
log_file_path = "logs/lv_framework.log"
max_log_file_size_mb = 100
log_file_backup_count = 5
# Specific logger levels
[logging.loggers]
"mcp_neocoder.lv_ecosystem" = "INFO"
"mcp_neocoder.lv_templates" = "INFO"
"mcp_neocoder.lv_integration" = "INFO"
"neo4j" = "WARNING"
"qdrant_client" = "WARNING"
"sentence_transformers" = "WARNING"
# Monitoring and Metrics
[monitoring]
# Enable metrics collection
enable_metrics = true
metrics_export_interval = 60 # seconds
# Metrics to track
track_diversity_metrics = true
track_convergence_metrics = true
track_performance_metrics = true
track_quality_metrics = true
# Prometheus integration (if available)
prometheus_enabled = false
prometheus_port = 8000
# Development Configuration
[development]
# Development mode settings
debug_mode = false
enable_profiling = false
save_intermediate_results = false
# Testing configuration
mock_databases = false
test_data_size = 100
enable_benchmarking = false
# Advanced Features
[advanced]
# Experimental features (use with caution)
enable_adaptive_weights = false
enable_meta_learning = false
enable_ensemble_selection = false
# Hardware optimization
auto_detect_hardware = true
prefer_cpu = false
nvidia_gpu_memory_fraction = 0.8
# Custom Extensions
[extensions]
# Custom entropy estimators
custom_entropy_estimator = null
# Custom quality assessors
custom_quality_assessor = null
# Custom novelty calculators
custom_novelty_calculator = null
# Plugin system
enable_plugins = false
plugin_directory = "plugins/"
# Integration Settings
[integration.neocoder]
# NeoCoder-specific settings
auto_enhance_templates = true
enhancement_threshold = 0.4 # Entropy threshold for auto-enhancement
preserve_original_templates = true
# Template-specific settings
[integration.neocoder.templates]
KNOWLEDGE_EXTRACT = { entropy_boost = 0.1, max_strategies = 5 }
KNOWLEDGE_QUERY = { entropy_boost = 0.2, max_strategies = 4 }
FEATURE = { entropy_boost = 0.0, max_strategies = 3 }
FIX = { entropy_boost = -0.1, max_strategies = 2 }
# Security Settings
[security]
# API security
enable_api_authentication = false
api_key_header = "X-API-Key"
rate_limiting_enabled = false
max_requests_per_minute = 60
# Database security
encrypt_database_connections = true
validate_ssl_certificates = true
# Data privacy
anonymize_logs = true
data_retention_days = 90
# Backup and Recovery
[backup]
# Automatic backup settings
enable_automatic_backup = false
backup_interval_hours = 24
backup_retention_days = 30
backup_directory = "backups/"
# What to backup
backup_neo4j_data = true
backup_qdrant_data = true
backup_configuration = true
backup_logs = false
