""" Dynamic Configuration - rider-api Replaces all hardcoded hyperparameters with DB-backed values. The autonomous agents (ETA sync, road-sequencing, rider affinity) and the ml_admin API write tuned values here; services read from here. Fallback: If DB is unavailable or no tuned values exist, defaults are used. This means zero risk - the system works day 1 with no data. """ import json import logging import os import sqlite3 from datetime import datetime from typing import Any, Dict, Optional logger = logging.getLogger(__name__) # --- DB Path ------------------------------------------------------------------ _DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db") # --- Hard Defaults (What the system used before ML) --------------------------- DEFAULTS: Dict[str, Any] = { # AssignmentService "max_pickup_distance_km": 10.0, "max_kitchen_distance_km": 3.0, "max_orders_per_rider": 12, "ideal_load": 6, "workload_balance_threshold": 0.7, "workload_penalty_weight": 100.0, "distance_penalty_weight": 2.0, "preference_bonus": -15.0, "home_zone_bonus_4km": -3.0, "home_zone_bonus_2km": -5.0, "emergency_load_penalty": 3.0, # km penalty per order in emergency assign # RouteOptimizer "search_time_limit_seconds": 5, "avg_speed_kmh": 18.0, "road_factor": 1.3, # ClusteringService "cluster_radius_km": 3.0, # RealisticETACalculator "eta_pickup_time_min": 3.0, "eta_delivery_time_min": 4.0, "eta_navigation_buffer_min": 1.5, "eta_short_trip_factor": 0.8, # speed multiplier for dist < 2km "eta_long_trip_factor": 1.1, # speed multiplier for dist > 8km # EmpiricalETACalculator (learned ETAs from actual delivery times) "eta_empirical_enabled": True, # False -> instantly revert to the formula "eta_min_samples": 10, # min history samples a key needs before trust # (backtest on live 14d data: 10 -> MAE 4.85 vs # formula 5.73; 20 -> 5.64. 10 wins on held-out.) "eta_history_days": 14, # rolling window pulled from nearledb "eta_stat": "median", # "median" or "p75" (p75 = more conservative) "eta_sync_interval_hours": 6, # autonomous background sync cadence # Road-aware sequencing (Phase 2). OFF by default: enabling adds a Google # Directions call (cost + latency) to the route hot path. Results are cached. # Only the *visiting order* changes; step/ETA metrics stay aerial-based. "routing_use_road_distance": False, # AGENT-MANAGED (see routing_auto_manage) "routing_road_cache_ttl_seconds": 86400, # road geometry is stable; cache 24h "routing_road_max_stops": 25, # Google distance-matrix practical cap # Autonomous road-sequencing decision agent: measures road-vs-aerial travel # time on real batches and flips routing_use_road_distance on its own. "routing_auto_manage": True, # False -> humans own the flag "routing_auto_enable_gain_pct": 3.0, # enable when mean gain >= this "routing_auto_disable_gain_pct": 1.0, # disable when mean gain < this (hysteresis) "routing_eval_sample_batches": 8, # batches measured per cycle (cost bound) "routing_eval_min_batches": 3, # need >= this evaluated to decide "routing_eval_interval_hours": 24, # decision cadence "routing_eval_days": 14, # window sampled from the local mirror # Learned rider->kitchen affinity (soft steering only; union with curated config). "rider_affinity_enabled": True, # False -> pure curated config "rider_affinity_min_deliveries": 10, # learned owner needs >= this many deliveries "rider_affinity_refresh_hours": 6, # recompute cadence (piggybacks the agent) # Phase-0 kitchen+zone pattern store (delivery_history_store.py): which rider # historically owns a kitchen->drop-zone pair, used to pre-assign orders. # "csv" -> legacy: built from delivery_details.csv, only refreshed when a # human re-exports it and calls POST /ml/reload-history. # "db" -> built from the already-synced nearledb mirror (delivery_raw), # rebuilt automatically every eta_sync_interval_hours — no manual # step, no extra DB load (reuses rows the ETA sync already pulled). "pattern_source": "csv", "pattern_history_days": 30, # retention floor for delivery_raw kept for pattern- # matching volume; independent of eta_history_days # so the (already-validated) empirical ETA window # is untouched. } class DynamicConfig: """ Thread-safe, DB-backed configuration store. Usage: cfg = DynamicConfig() max_dist = cfg.get("max_pickup_distance_km") all_params = cfg.get_all() """ _instance: Optional["DynamicConfig"] = None def __new__(cls) -> "DynamicConfig": """Singleton - one config per process.""" if cls._instance is None: cls._instance = super().__new__(cls) cls._instance._initialized = False return cls._instance def __init__(self): if self._initialized: return self._initialized = True self._cache: Dict[str, Any] = {} self._last_loaded: Optional[datetime] = None self._ensure_db() self._load() # -------------------------------------------------------------------------- # Public API # -------------------------------------------------------------------------- def get(self, key: str, default: Any = None) -> Any: """Get a config value. Returns ML-tuned value if available, else default.""" self._maybe_reload() val = self._cache.get(key) if val is not None: return val fallback = default if default is not None else DEFAULTS.get(key) return fallback def get_all(self) -> Dict[str, Any]: """Return all current config values (ML-tuned + defaults for missing keys).""" self._maybe_reload() result = dict(DEFAULTS) result.update(self._cache) return result def set(self, key: str, value: Any, source: str = "manual") -> None: """Write a config value to DB (used by the ml_admin API and agents).""" try: os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True) conn = sqlite3.connect(_DB_PATH) conn.execute( """ INSERT INTO dynamic_config (key, value, source, updated_at) VALUES (?, ?, ?, ?) ON CONFLICT(key) DO UPDATE SET value=excluded.value, source=excluded.source, updated_at=excluded.updated_at """, (key, json.dumps(value), source, datetime.utcnow().isoformat()), ) conn.commit() conn.close() self._cache[key] = value logger.info(f"[DynamicConfig] Set {key}={value} (source={source})") except Exception as e: logger.error(f"[DynamicConfig] Failed to set {key}: {e}") def set_bulk(self, params: Dict[str, Any], source: str = "manual") -> None: """Write multiple config values at once.""" for key, value in params.items(): self.set(key, value, source=source) logger.info(f"[DynamicConfig] Bulk update: {len(params)} params from {source}") def reset_to_defaults(self) -> None: """Wipe all ML-tuned values, revert to hardcoded defaults.""" try: conn = sqlite3.connect(_DB_PATH) conn.execute("DELETE FROM dynamic_config") conn.commit() conn.close() self._cache.clear() logger.warning("[DynamicConfig] Reset to factory defaults.") except Exception as e: logger.error(f"[DynamicConfig] Reset failed: {e}") # -------------------------------------------------------------------------- # Internal # -------------------------------------------------------------------------- def _ensure_db(self) -> None: try: os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True) conn = sqlite3.connect(_DB_PATH) conn.execute(""" CREATE TABLE IF NOT EXISTS dynamic_config ( key TEXT PRIMARY KEY, value TEXT NOT NULL, source TEXT DEFAULT 'manual', updated_at TEXT ) """) conn.commit() conn.close() except Exception as e: logger.error(f"[DynamicConfig] DB init failed: {e}") def _load(self) -> None: try: conn = sqlite3.connect(_DB_PATH) rows = conn.execute("SELECT key, value FROM dynamic_config").fetchall() conn.close() self._cache = {} for key, raw in rows: try: self._cache[key] = json.loads(raw) except Exception: self._cache[key] = raw self._last_loaded = datetime.utcnow() if self._cache: logger.info( f"[DynamicConfig] Loaded {len(self._cache)} ML-tuned params from DB" ) except Exception as e: logger.warning( f"[DynamicConfig] Could not load from DB (using defaults): {e}" ) self._cache = {} def _maybe_reload(self, interval_seconds: int = 300) -> None: """Reload from DB every 5 minutes - picks up new tuned params without restart.""" if self._last_loaded is None: self._load() return delta = (datetime.utcnow() - self._last_loaded).total_seconds() if delta > interval_seconds: self._load() # --- Module-level convenience singleton --------------------------------------- _cfg = DynamicConfig() def get_config() -> DynamicConfig: """Get the global DynamicConfig singleton.""" return _cfg __all__ = [ "DynamicConfig", "get_config", ]