feat: Nagercoil single-rider assignment endpoint

Adds POST /api/v1/optimization/nagercoil/riderassign. Nagercoil runs with one
rider, so this skips the matching/VRP/pattern logic in riderassign and straight-
assigns every order to the single rider, then reuses the shared TSP sequencer
(_sync_optimize_route) so stop order, cumulative kms and per-stop ETAs match the
main endpoint's response shape.

Rider defaults to Sivakumar Subramani (userid 852, 8248176099) via the
NAGERCOIL_RIDER constant; a `rider` object in the request body overrides it.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018jhRcey2fxhwz2SuPVm7c5

.gitignore

@@ -2,3 +2,15 @@ __pycache__/
 *.db
 *.sqlite3
 *.pyc
+
+# Valhalla OSM tiles + extracts (multi-GB, rebuilt from tile_urls)
+valhalla_tiles/
+
+# Runtime state written by the container — never source of truth in git.
+# ml_data holds the live FAISS delivery-history index and bandit state;
+# the .pkl files hold live rider state. Reverting these from git would
+# silently reset learned behaviour.
+ml_data/
+data/
+*.pkl
+.env.bak-*

app/config/dynamic_config.py

@@ -38,6 +38,9 @@ DEFAULTS: Dict[str, Any] = {
     "emergency_load_penalty": 3.0,  # km penalty per order in emergency assign
     # RouteOptimizer
     "search_time_limit_seconds": 5,
+    # Active-rider roster cache. The upstream getriderlogs call measured ~6s on
+    # live traffic. Short TTL so on/off-duty changes surface quickly; 0 = off.
+    "rider_roster_cache_ttl_seconds": 30,
     "avg_speed_kmh": 18.0,
     "road_factor": 1.3,
     # ClusteringService
@@ -56,12 +59,20 @@ DEFAULTS: Dict[str, Any] = {
     "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.
+    # Road-aware sequencing (Phase 2). Backed by self-hosted Valhalla, so there is
+    # no per-request cost — only local latency. Still OFF by default and left to
+    # the agent to enable on measured gain. 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
+    "routing_road_max_stops": 25,             # beyond this the solver time outweighs
+                                              # the ordering gain (not a backend cap)
+    # Valhalla matrix backend. "motorcycle" models the lane access and one-way
+    # behaviour our riders actually have; "auto" would overstate their travel time.
+    "routing_valhalla_costing": "motorcycle",
+    "routing_matrix_timeout_seconds": 15.0,
+    "routing_matrix_max_unroutable_pct": 20.0,  # above this -> tiles likely missing
+                                                # this region, fall back to aerial
     # 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

app/main.py

@@ -6,7 +6,7 @@ import sys
 import time
 from contextlib import asynccontextmanager
 
-# Load .env (DB_*, REDIS_*, GOOGLE_MAPS_API_KEY) into the environment early,
+# Load .env (DB_*, REDIS_*, VALHALLA_URL) into the environment early,
 # before any service reads os.getenv at import time.
 try:
     from dotenv import load_dotenv
@@ -28,7 +28,7 @@ from app.core.exception_handlers import (
 )
 from app.core.exceptions import APIException
 from app.middleware.request_id import RequestIDMiddleware
-from app.routes import cache_router, health_router, ml_router, ml_web_router, optimization_router, batch_analytics_router, riders_router, doormile_router
+from app.routes import cache_router, health_router, ml_router, ml_web_router, optimization_router, batch_analytics_router, riders_router, doormile_router, nagercoil_router
 
 # ---------------------------------------------------------------------------
 # Logging
@@ -111,6 +111,27 @@ async def lifespan(app: FastAPI):
     except Exception as e:
         logger.warning(f"[RoadAgent] Decision agent start failed (non-fatal): {e}")
 
+    # Probe the Valhalla matrix backend once. Road sequencing degrades to aerial
+    # silently by design, so without this an unreachable or still-building backend
+    # stays invisible — which is exactly how the expired Google key went unnoticed.
+    try:
+        from app.services.routing.route_optimizer import road_backend_status
+        _rb = await road_backend_status()
+        if not _rb.get("configured"):
+            logger.warning("[RoadMatrix] VALHALLA_URL not set - aerial sequencing only.")
+        elif _rb.get("reachable"):
+            logger.info(
+                f"[RoadMatrix] Valhalla ready at {_rb.get('url')} "
+                f"(version={_rb.get('version')}, tiles={_rb.get('tileset_last_modified')})"
+            )
+        else:
+            logger.warning(
+                f"[RoadMatrix] Valhalla at {_rb.get('url')} unreachable "
+                f"({_rb.get('detail')}) - aerial fallback until it answers."
+            )
+    except Exception as e:
+        logger.warning(f"[RoadMatrix] Backend probe failed (non-fatal): {e}")
+
     logger.info("[OK] Application ready.")
     yield
     logger.info("[STOP] Route Optimization API shutting down.")
@@ -163,6 +184,7 @@ app.include_router(ml_web_router)
 app.include_router(batch_analytics_router)
 app.include_router(riders_router)
 app.include_router(doormile_router)
+app.include_router(nagercoil_router)
 
 
 @app.get("/", tags=["Root"])

app/routes/__init__.py

@@ -7,6 +7,7 @@ from .ml_admin import router as ml_router, web_router as ml_web_router
 from .batch_analytics import router as batch_analytics_router
 from .riders import router as riders_router
 from .doormile import router as doormile_router
+from .nagercoil import router as nagercoil_router
 
 __all__ = [
     "optimization_router",
@@ -17,4 +18,5 @@ __all__ = [
     "batch_analytics_router",
     "riders_router",
     "doormile_router",
+    "nagercoil_router",
 ]

app/routes/health.py

@@ -84,6 +84,26 @@ async def readiness_check(request: Request):
         }
 
 
+@router.get("/road-backend")
+async def road_backend_check(request: Request):
+    """
+    Status of the Valhalla road-matrix backend used for road-aware stop sequencing.
+
+    Deliberately separate from /ready: an unreachable backend is a degradation,
+    not an outage — routing falls back to aerial ordering and keeps serving. This
+    endpoint is how you notice the degradation.
+    """
+    from app.services.routing.route_optimizer import road_backend_status
+
+    status = await road_backend_status()
+    return {
+        **status,
+        "sequencing_mode": "road" if status.get("reachable") else "aerial",
+        "timestamp": datetime.utcnow().isoformat() + "Z",
+        "request_id": getattr(request.state, "request_id", None),
+    }
+
+
 @router.get("/live")
 async def liveness_check(request: Request):
     """

app/routes/nagercoil.py

@@ -0,0 +1,248 @@
+"""Nagercoil single-rider assignment endpoint.
+
+Nagercoil currently operates with exactly one active rider, so the full
+matching / VRP / pattern-preassignment machinery in ``riderassign`` is
+unnecessary here: every created order goes to that one rider. This endpoint
+keeps the same request/response contract as
+``POST /api/v1/optimization/riderassign`` but replaces the assignment step
+with a straight "all orders → the single rider" hand-off, then reuses the
+existing TSP sequencer so the rider still gets an optimised stop order and
+per-stop ETAs.
+
+If Nagercoil later grows to more than one rider, switch its caller back to the
+main ``riderassign`` endpoint (or add real matching here) — this route
+deliberately assumes a single rider and will reject an ambiguous roster.
+"""
+
+import asyncio
+import logging
+import time
+from datetime import timedelta
+from typing import Any
+
+from fastapi import APIRouter, Body, Query, status
+from dateutil.parser import parse as parse_date
+
+# Reuse the exact same sequencer + thread pool the main endpoint uses, so the
+# stop ordering and cumulative-km output are identical.
+from app.routes.optimization import _sync_optimize_route, _route_executor
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/optimization/nagercoil", tags=["Nagercoil"])
+
+# Nagercoil currently runs with exactly one rider. Every order goes to him
+# unless the caller overrides via a `rider` object in the request body.
+NAGERCOIL_RIDER = {
+    "userid": 852,
+    "username": "Sivakumar Subramani",
+    "contactno": "8248176099",
+}
+
+
+def _rider_id(r: dict) -> int:
+    """Extract a rider's numeric id, tolerating the roster's field variants."""
+    try:
+        return int(r.get("userid") or r.get("riderid") or r.get("id") or 0)
+    except (TypeError, ValueError):
+        return 0
+
+
+@router.post(
+    "/riderassign",
+    status_code=status.HTTP_200_OK,
+    summary="Assign all created orders to Nagercoil's single rider",
+    description="""
+    Straight-assigns every order to Nagercoil's one active rider, sequences the
+    stops with the shared TSP optimizer, and returns the same shape as the main
+    `/api/v1/optimization/riderassign` endpoint.
+
+    Rider resolution (first match wins):
+      1. `rider` object in the body — `{"userid": 555, "username": "...", "contactno": "..."}`
+      2. otherwise the single active rider returned by the live roster; if the
+         roster returns zero or more than one rider this endpoint returns a 200
+         with `status: false` and a message, since Nagercoil is single-rider.
+
+    Orders:
+      - `deliveries` in the body (or a legacy flat list) are used as-is.
+      - if empty, all `created` orders are fetched from the external API.
+    """,
+    responses={
+        200: {
+            "content": {
+                "application/json": {
+                    "example": {
+                        "code": 200,
+                        "details": [{"orderid": "...", "userid": 555, "step": 1}],
+                        "message": "Success",
+                        "status": True,
+                    }
+                }
+            }
+        }
+    },
+)
+async def nagercoil_assign(
+    body: Any = Body(default=None),
+    reshuffle: bool = Query(False, alias="reshuffle"),
+):
+    from app.services.rider.get_active_riders import fetch_created_orders
+    from app.services.routing.empirical_eta_calculator import EmpiricalETACalculator
+    from app.services.routing.realistic_eta_calculator import get_time_of_day_category
+
+    eta_calculator = EmpiricalETACalculator()
+    _t0 = time.time()
+
+    try:
+        # ── Parse payload: {"deliveries": [...], "rider": {...}} or flat list ──
+        payload_rider = None
+        if isinstance(body, dict):
+            payload_rider = body.get("rider") or None
+            orders = body.get("deliveries") or []
+        elif isinstance(body, list):
+            orders = body
+        else:
+            orders = []
+
+        # ── Resolve the single rider ─────────────────────────────────────────
+        rider = None
+        if isinstance(payload_rider, dict) and _rider_id(payload_rider):
+            rider = payload_rider
+            if not orders:
+                orders = await fetch_created_orders()
+        else:
+            # No rider in the payload — default to Nagercoil's fixed single rider.
+            rider = dict(NAGERCOIL_RIDER)
+            if not orders:
+                orders = await fetch_created_orders()
+
+        rid = _rider_id(rider)
+        rider_name = rider.get("username", "") or rider.get("name", "")
+        rider_contact = rider.get("contactno", "")
+
+        if not orders:
+            logger.info("[Nagercoil] No orders to assign.")
+            return {
+                "code": 200,
+                "details": [],
+                "message": "No created orders to assign.",
+                "status": True,
+                "meta": {
+                    "total_orders": 0,
+                    "assigned_orders": 0,
+                    "rider": {"userid": rid, "username": rider_name},
+                },
+            }
+
+        logger.info(
+            f"[Nagercoil] Straight-assigning {len(orders)} orders to rider "
+            f"{rid} ({rider_name})."
+        )
+
+        # ── Sequence the single rider's stops with the shared TSP optimizer ──
+        loop = asyncio.get_running_loop()
+        optimized_route = await loop.run_in_executor(
+            _route_executor, _sync_optimize_route, orders
+        ) or orders  # fall back to input order if the optimizer returns nothing
+
+        # Total distance for the rider (mirrors riderassign)
+        try:
+            total_rider_kms = max(
+                float(o.get("cumulativekms", 0)) for o in optimized_route
+            )
+        except Exception:
+            total_rider_kms = sum(
+                float(o.get("actualkms", o.get("kms", 0))) for o in optimized_route
+            )
+
+        # ── Enrich each order exactly like riderassign does ──────────────────
+        for order in optimized_route:
+            order["userid"] = rid
+            order["username"] = rider_name
+            order["rider"] = rider_name
+            order["ridercontactno"] = rider_contact
+            order["riderkms"] = str(round(total_rider_kms, 2))
+
+            pickup_slot_str = (
+                order.get("pickupSlot")
+                or order.get("pickupslot")
+                or order.get("pickup_slot")
+                or order.get("pickuptime")
+            )
+            if pickup_slot_str:
+                try:
+                    pickup_time = parse_date(str(pickup_slot_str))
+                    if order.get("cumulative_eta"):
+                        eta_mins = int(order["cumulative_eta"])
+                    else:
+                        dist_km = float(
+                            order.get("cumulativekms")
+                            or order.get("actualkms", order.get("kms", 0))
+                        )
+                        step = int(order.get("step", 1))
+                        order_type = order.get("ordertype", "Economy")
+                        _dcoords = None
+                        try:
+                            _dlat = float(order.get("deliverylat") or order.get("droplat") or 0)
+                            _dlon = float(order.get("deliverylong") or order.get("droplon") or 0)
+                            if _dlat and _dlon:
+                                _dcoords = (_dlat, _dlon)
+                        except (TypeError, ValueError):
+                            _dcoords = None
+                        eta_mins = eta_calculator.calculate_eta(
+                            distance_km=dist_km,
+                            is_first_order=(step == 1),
+                            order_type=order_type,
+                            time_of_day=get_time_of_day_category(),
+                            kitchen=order.get("pickupcustomer") or order.get("locationname"),
+                            drop_coords=_dcoords,
+                            rider_id=rid,
+                        )
+                    expected_time = pickup_time + timedelta(minutes=eta_mins)
+                    order["expectedDeliveryTime"] = expected_time.strftime(
+                        "%Y-%m-%d %I:%M %p"
+                    )
+                    order["transitMinutes"] = eta_mins
+                    order["calculationDistanceKm"] = round(
+                        float(order.get("cumulativekms") or order.get("actualkms", 0)), 2
+                    )
+                except Exception as e:
+                    logger.warning(
+                        f"[Nagercoil] ETA calc failed for pickupSlot "
+                        f"'{pickup_slot_str}': {e}"
+                    )
+
+        _elapsed_ms = (time.time() - _t0) * 1000.0
+        logger.info(
+            f"[API] nagercoil/riderassign ◀ assigned={len(optimized_route)}/"
+            f"{len(orders)} rider={rid} elapsed={round(_elapsed_ms)}ms"
+        )
+
+        return {
+            "code": 200,
+            "details": optimized_route,
+            "message": "Success",
+            "status": True,
+            "meta": {
+                "total_orders": len(orders),
+                "assigned_orders": len(optimized_route),
+                "unassigned_orders": 0,
+                "rider": {
+                    "userid": rid,
+                    "username": rider_name,
+                    "contactno": rider_contact,
+                },
+                "rider_total_kms": round(total_rider_kms, 2),
+                "reshuffle_mode": reshuffle,
+                "elapsed_ms": round(_elapsed_ms, 1),
+            },
+        }
+
+    except Exception as e:
+        logger.error(f"[Nagercoil] assignment error: {e}", exc_info=True)
+        return {
+            "code": 500,
+            "details": [],
+            "message": "Internal server error during Nagercoil assignment.",
+            "status": False,
+        }

app/services/__init__.py

@@ -15,6 +15,31 @@ except Exception:  # pragma: no cover
 logger = logging.getLogger(__name__)
 
 
+def _redis_url_from_parts() -> Optional[str]:
+    """
+    Build a redis:// URL from discrete env vars, percent-encoding the credentials.
+
+    A hand-written REDIS_URL breaks the moment the password contains a reserved
+    character: an '@' splits the authority section early, so the client silently
+    dials a garbage host instead of failing loudly. Assembling the URL here with
+    quote() removes that whole class of bug - callers set REDIS_HOST/REDIS_PASSWORD
+    and never have to think about escaping.
+
+    Returns None when REDIS_HOST is unset, so an explicit REDIS_URL still wins.
+    """
+    host = os.getenv("REDIS_HOST")
+    if not host:
+        return None
+    from urllib.parse import quote
+
+    user = os.getenv("REDIS_USERNAME", "")
+    pwd = os.getenv("REDIS_PASSWORD", "")
+    port = os.getenv("REDIS_PORT", "6379")
+    db = os.getenv("REDIS_DB", "0")
+    auth = f"{quote(user, safe='')}:{quote(pwd, safe='')}@" if pwd else ""
+    return f"redis://{auth}{host}:{port}/{db}"
+
+
 class RedisCache:
     """Lightweight Redis cache wrapper with graceful in-memory fallback."""
 
@@ -33,9 +58,10 @@ class RedisCache:
         self._client = None
         self._stats = {"hits": 0, "misses": 0, "sets": 0}
 
-        url = os.getenv(url_env)
+        url = os.getenv(url_env) or _redis_url_from_parts()
         if not url or redis is None:
-            logger.warning("Redis not configured or client unavailable; falling back to local thread-safe in-memory cache")
+            _why = "redis client not installed" if redis is None else "no REDIS_URL/REDIS_HOST"
+            logger.warning(f"Redis not configured or client unavailable ({_why}); falling back to local thread-safe in-memory cache")
             return
         try:
             self._client = redis.Redis.from_url(url, decode_responses=True)

app/services/rider/get_active_riders.py

@@ -6,13 +6,45 @@ from typing import List, Dict, Any
 
 logger = logging.getLogger(__name__)
 
+def _roster_cache_ttl() -> int:
+    """Roster cache TTL in seconds. 0 disables caching entirely."""
+    try:
+        from app.config.dynamic_config import get_config
+        return int(get_config().get("rider_roster_cache_ttl_seconds", 30))
+    except Exception:
+        return 30
+
+
 async def fetch_active_riders() -> List[Dict[str, Any]]:
     """
     Fetch active rider logs from the external API for the current date.
     Returns a list of rider log dictionaries.
+
+    This upstream call measured ~6s on live traffic and was the single largest
+    component of riderassign latency, so successful responses are cached for
+    `rider_roster_cache_ttl_seconds`. The TTL is deliberately short: a rider
+    going on or off duty must become visible quickly, and the absent-rider
+    validation in the assignment path checks against this roster.
+
+    Only non-empty successes are cached. Caching an empty result or a failure
+    would turn a brief upstream blip into a full-TTL outage where every request
+    sees zero riders.
     """
+    today_str = datetime.now().strftime("%Y-%m-%d")
+    ttl = _roster_cache_ttl()
+    cache_key = f"riders:active:{today_str}"
+
+    if ttl > 0:
+        try:
+            from app.services import cache as _cache
+            cached = _cache.get_json(cache_key)
+            if isinstance(cached, list) and cached:
+                logger.debug(f"[Riders] roster cache hit ({len(cached)} riders)")
+                return cached
+        except Exception:
+            pass
+
     try:
-        today_str = datetime.now().strftime("%Y-%m-%d")
         url = "https://jupiter.nearle.app/live/api/v2/partners/getriderlogs/"
         params = {
             "applocationid": 1,
@@ -32,7 +64,14 @@ async def fetch_active_riders() -> List[Dict[str, Any]]:
                  # The user's example showed "onduty": 1. We might want to filter by that.
                  # For now, returning all logs, filtering can happen in assignment logic or here.
                  # Let's return the raw list as requested, filtering logic will be applied during assignment.
-                 return data.get("details", [])
+                 riders = data.get("details", [])
+                 if riders and ttl > 0:
+                     try:
+                         from app.services import cache as _cache
+                         _cache.set_json(cache_key, riders, ttl_seconds=ttl)
+                     except Exception:
+                         pass
+                 return riders
 
             logger.warning(f"Fetch active riders returned no details: {data}")
             return []

app/services/routing/road_sequencing_agent.py

@@ -61,8 +61,8 @@ class RoadSequencingAgent:
         from app.core.arrow_utils import calculate_haversine_matrix_vectorized
 
         opt = self._optimizer()
-        if not opt.use_google_maps:
-            return {"evaluated": 0, "reason": "no_google_key", "mean_gain_pct": 0.0}
+        if not opt.use_road_matrix:
+            return {"evaluated": 0, "reason": "no_matrix_backend", "mean_gain_pct": 0.0}
 
         batches = get_delivery_history_service().sample_batches(
             days=days, limit=sample_batches

app/services/routing/route_optimizer.py

@@ -8,7 +8,7 @@ ALGORITHM: TSP / VRP with Google OR-Tools
 
 FEATURES:
 - Automatic outlier detection and coordinate correction
-- Hybrid distance calculation (Google Maps + Haversine fallback)
+- Hybrid distance calculation (self-hosted Valhalla road matrix + Haversine fallback)
 - Robust error handling for invalid inputs
 """
 
@@ -37,6 +37,37 @@ except ImportError:
 logger = logging.getLogger(__name__)
 
 
+async def road_backend_status() -> Dict[str, Any]:
+    """
+    Probe the Valhalla matrix backend.
+
+    Module-level (not a RouteOptimizer method) so startup and health checks can
+    call it without paying for a full optimizer construction, which pulls in the
+    empirical ETA calculator and its history load.
+    """
+    url = os.getenv("VALHALLA_URL", "").strip().rstrip("/")
+    if not url:
+        return {
+            "configured": False,
+            "reachable": False,
+            "detail": "VALHALLA_URL not set - road sequencing disabled, aerial only",
+        }
+    try:
+        async with httpx.AsyncClient(timeout=5.0) as client:
+            resp = await client.get(f"{url}/status")
+            resp.raise_for_status()
+            data = resp.json()
+        return {
+            "configured": True,
+            "reachable": True,
+            "url": url,
+            "version": data.get("version"),
+            "tileset_last_modified": data.get("tileset_last_modified"),
+        }
+    except Exception as e:
+        return {"configured": True, "reachable": False, "url": url, "detail": str(e)}
+
+
 class RouteOptimizer:
     """Route optimization using Google OR-Tools (Async)."""
 
@@ -60,9 +91,11 @@ class RouteOptimizer:
         # Road factor (haversine -> road distance multiplier, ML-tuned)
         self.road_factor = float(_cfg.get("road_factor"))
 
-        # Google Maps API settings
-        self.google_maps_api_key = os.getenv("GOOGLE_MAPS_API_KEY", "")
-        self.use_google_maps = bool(self.google_maps_api_key)
+        # Road travel-time matrix backend: self-hosted Valhalla (no per-request
+        # cost, no rate limit). Unset VALHALLA_URL -> road sequencing stays off
+        # and every caller falls back to aerial ordering.
+        self.valhalla_url = os.getenv("VALHALLA_URL", "").strip().rstrip("/")
+        self.use_road_matrix = bool(self.valhalla_url)
 
         # Solver time limit (ML-tuned)
         self.search_time_limit_seconds = int(_cfg.get("search_time_limit_seconds"))
@@ -90,47 +123,94 @@ class RouteOptimizer:
     # ROAD-AWARE VISITING ORDER  (Phase 2 - opt-in, cached)
     # ------------------------------------------------------------------
 
+    def _aerial_minutes(
+        self, a: Tuple[float, float], b: Tuple[float, float]
+    ) -> float:
+        """Haversine travel-time estimate (minutes), used to patch unroutable pairs."""
+        km = self.haversine_distance(a[0], a[1], b[0], b[1]) * self.road_factor
+        return (km / (self.avg_speed_kmh or 20.0)) * 60.0
+
     async def _road_duration_matrix(
         self, coords: _List[Tuple[float, float]]
     ) -> Optional[_List[_List[float]]]:
         """
-        Full NxN road travel-TIME matrix (minutes) via Google Distance Matrix.
+        Full NxN road travel-TIME matrix (minutes) via self-hosted Valhalla.
+
+        One /sources_to_targets call returns the whole matrix, so unlike the old
+        Google Distance Matrix path there is no per-request element cap and no
+        chunking. Costing defaults to `motorcycle`, which models the lane access
+        and one-way behaviour our riders actually have — a car matrix systematically
+        overstates their travel time.
+
+        Valhalla reports an unroutable pair as time=null. Those are patched with an
+        aerial estimate rather than left at 0, because a 0-cost edge would look
+        free to the TSP and pull the whole sequence through it. If too many pairs
+        are unroutable the tileset probably doesn't cover this region, so we bail
+        to aerial entirely.
 
-        Chunks destinations to respect Google's ~100-elements-per-request limit.
         Returns None on any failure so the caller falls back to aerial ordering.
         """
-        if not self.use_google_maps:
+        if not self.use_road_matrix:
             return None
+        cfg = get_config()
+        costing = str(cfg.get("routing_valhalla_costing", "motorcycle"))
+        timeout = float(cfg.get("routing_matrix_timeout_seconds", 15.0))
+        max_unroutable = float(cfg.get("routing_matrix_max_unroutable_pct", 20.0))
+
         n = len(coords)
-        origins = "|".join(f"{la},{lo}" for la, lo in coords)
+        locations = [{"lat": float(la), "lon": float(lo)} for la, lo in coords]
         matrix = [[0.0] * n for _ in range(n)]
-        dest_chunk = max(1, 100 // max(1, n))
         try:
-            async with httpx.AsyncClient(timeout=15.0) as client:
-                for j0 in range(0, n, dest_chunk):
-                    js = list(range(j0, min(j0 + dest_chunk, n)))
-                    dests = "|".join(f"{coords[j][0]},{coords[j][1]}" for j in js)
-                    resp = await client.get(
-                        "https://maps.googleapis.com/maps/api/distancematrix/json",
-                        params={"origins": origins, "destinations": dests,
-                                "key": self.google_maps_api_key, "units": "metric"},
-                    )
-                    resp.raise_for_status()
-                    data = resp.json()
-                    if data.get("status") != "OK":
-                        logger.debug(f"[RoadSeq] DistanceMatrix status={data.get('status')}")
-                        return None
-                    for i, row in enumerate(data.get("rows", [])):
-                        for k, el in enumerate(row.get("elements", [])):
-                            if el.get("status") == "OK":
-                                dur = el.get("duration", {}).get("value")
-                                if dur is not None:
-                                    matrix[i][js[k]] = dur / 60.0
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                resp = await client.post(
+                    f"{self.valhalla_url}/sources_to_targets",
+                    json={
+                        "sources": locations,
+                        "targets": locations,
+                        "costing": costing,
+                        "units": "km",
+                    },
+                )
+                resp.raise_for_status()
+                data = resp.json()
+
+            rows = data.get("sources_to_targets") or []
+            if len(rows) != n:
+                logger.debug(
+                    f"[RoadSeq] Valhalla returned {len(rows)} rows, expected {n}"
+                )
+                return None
+
+            unroutable = 0
+            for i, row in enumerate(rows):
+                for el in row or []:
+                    j = el.get("to_index")
+                    if j is None or not (0 <= j < n):
+                        continue
+                    secs = el.get("time")
+                    if secs is None:
+                        unroutable += 1
+                        matrix[i][j] = self._aerial_minutes(coords[i], coords[j])
+                    else:
+                        matrix[i][j] = secs / 60.0
+
+            pct = 100.0 * unroutable / max(1, n * n)
+            if pct > max_unroutable:
+                logger.warning(
+                    f"[RoadSeq] {unroutable}/{n * n} pairs ({pct:.0f}%) unroutable via "
+                    f"Valhalla - tileset likely missing this region; using aerial"
+                )
+                return None
+            if unroutable:
+                logger.debug(
+                    f"[RoadSeq] patched {unroutable}/{n * n} unroutable pairs with aerial"
+                )
             return matrix
         except Exception as e:
             logger.debug(f"[RoadSeq] matrix build failed: {e}")
             return None
 
+
     async def _road_optimal_order(
         self,
         start_lat: float,
@@ -140,27 +220,25 @@ class RouteOptimizer:
         """
         Road-aware visiting order for `points`, starting from (start_lat, start_lon).
 
-        Builds a real road travel-TIME matrix (Google Distance Matrix) and solves
-        an OPEN TSP with OR-Tools (return-to-depot edge = 0), so the sequence
-        respects real road geometry/one-ways instead of straight-line distance.
-        Validated on live batches to cut real travel time ~5-13% vs aerial; note
-        Google's Directions optimize:true is NOT used - it optimises a closed loop
-        and measured *worse* than aerial for our open delivery routes.
+        Builds a real road travel-TIME matrix (Valhalla) and solves an OPEN TSP
+        with OR-Tools (return-to-depot edge = 0), so the sequence respects real
+        road geometry/one-ways instead of straight-line distance. Validated on
+        live batches to cut real travel time ~5-13% vs aerial.
 
         Returns 0-based indices into `points` in optimal order, or None to signal
         the caller to fall back to the existing aerial greedy + 2-opt.
 
-        Safe + cheap on the live path:
-          * disabled unless `routing_use_road_distance` AND a Google key is set
+        Safe on the live path:
+          * disabled unless `routing_use_road_distance` AND VALHALLA_URL is set
           * only for 3..`routing_road_max_stops` stops (fewer is trivial; more
-            exceeds Google's waypoint-optimize cap)
+            costs more solver time than the ordering gain is worth)
           * result cached in Redis (default 24h) keyed by the rounded coords in
             input order, so the returned indices always map back correctly
         """
         cfg = get_config()
         if not cfg.get("routing_use_road_distance", False):
             return None
-        if not self.use_google_maps:
+        if not self.use_road_matrix:
             return None
         n = len(points)
         if n < 3 or n > int(cfg.get("routing_road_max_stops", 25)):
@@ -362,14 +440,17 @@ class RouteOptimizer:
         search_params.local_search_metaheuristic = (
             routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
         )
-        # TSP time limit hard-capped at 2 seconds per kitchen.
-        # search_time_limit_seconds can be tuned up to 8-10s via config, but at
-        # delivery scale (< 15 stops)
-        # OR-Tools finds a near-optimal solution in < 200ms.  Waiting 8-10s
-        # per kitchen x 3 kitchens x 4 riders = 96s of unnecessary waiting.
-        # The VRP already has its own 3s cap.  This cap applies to per-rider
-        # TSP and the per-kitchen beatmap solves.
-        search_params.time_limit.seconds = min(self.search_time_limit_seconds, 2)
+        # TSP time budget, ceiling 2 seconds per kitchen. This cap applies to
+        # per-rider TSP and the per-kitchen beatmap solves.
+        #
+        # GUIDED_LOCAL_SEARCH runs until its time limit EXPIRES - it does not
+        # return early once it has the optimum. A flat cap therefore burned the
+        # whole budget on trivial inputs, where PATH_CHEAPEST_ARC is done in
+        # under 200ms. Scale with stop count; large inputs still get the ceiling.
+        _cap_ms = min(self.search_time_limit_seconds, 2) * 1000
+        search_params.time_limit.FromMilliseconds(
+            max(200, min(_cap_ms, 200 * len(locations)))
+        )
 
         solution = routing.SolveWithParameters(search_params)
 
@@ -742,13 +823,18 @@ class RouteOptimizer:
             sp.local_search_metaheuristic = (
                 routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
             )
-            # VRP time budget: cap at 3 seconds so the API response is never
+            # VRP time budget: ceiling of 3 seconds so the API response is never
             # blocked longer than that.  The x 3 multiplier caused 15-second
             # responses with the default 5-second search_time_limit.
-            # PATH_CHEAPEST_ARC typically finds a good solution in < 1 second
-            # for our typical sizes (<= 15 riders, <= 60 orders); GLS then
-            # improves it within the remaining budget.
-            sp.time_limit.seconds = min(self.search_time_limit_seconds, 3)
+            #
+            # GUIDED_LOCAL_SEARCH runs until its time limit EXPIRES rather than
+            # stopping once the optimum is found, so a flat 3s spent ~2.9s of
+            # pure waiting on a 1-order batch (measured on live traffic).
+            # Scale with problem size; large batches still get the full ceiling.
+            _cap_ms = min(self.search_time_limit_seconds, 3) * 1000
+            sp.time_limit.FromMilliseconds(
+                max(250, min(_cap_ms, 250 * len(orders)))
+            )
 
             solution = routing.SolveWithParameters(sp)
 

data/rider_active_state.pkl

@@ -1 +0,0 @@
-<EFBFBD>}<7D>.

docker-compose.yml

@@ -3,6 +3,9 @@ version: "3.9"
 networks:
   frontend:
     external: true
+  # redis + postgres-logistics live here; routes_api joins it purely to reach redis.
+  logistics:
+    external: true
 
 services:
   routes_api:
@@ -14,13 +17,20 @@ services:
     restart: unless-stopped
     environment:
       - UVICORN_WORKERS=2
-      - REDIS_URL=redis://:${REDIS_PASSWORD}@routes_redis:6379/0
+      # Redis as discrete parts, NOT a hand-built REDIS_URL: the password contains
+      # reserved characters and inlining it produced redis://:pa@ss@routes_redis/...,
+      # which resolved to a garbage host and silently fell back to in-memory cache.
+      # app/services/__init__.py percent-encodes these when assembling the URL.
+      # Host is `redis` (its container name) on the logistics network.
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+      - REDIS_PASSWORD=${REDIS_PASSWORD}
       # Optional: Set cache TTL in seconds (default: 300 = 5 min, 86400 = 24h)
       # Uncomment and set in .env file: REDIS_CACHE_TTL_SECONDS=86400
       # - REDIS_CACHE_TTL_SECONDS=${REDIS_CACHE_TTL_SECONDS}
-      # Google Maps API key for accurate road distance calculation (actualkms)
-      # Set in .env file: GOOGLE_MAPS_API_KEY=your_api_key_here
-      - GOOGLE_MAPS_API_KEY=${GOOGLE_MAPS_API_KEY}
+      # Self-hosted Valhalla, source of the road travel-time matrix used for
+      # road-aware stop sequencing. Unset -> sequencing falls back to aerial.
+      - VALHALLA_URL=http://routes_valhalla:8002
       # nearledb (read-only) — source for empirical ETA learning
       - DB_HOST=${DB_HOST}
       - DB_PORT=${DB_PORT}
@@ -42,3 +52,44 @@ services:
       - ./delivery_corrections.csv:/app/delivery_corrections.csv:ro
     networks:
       - frontend
+      - logistics
+
+  # Road travel-time matrix backend. Replaces the Google Distance Matrix API:
+  # no key, no per-request cost, no element cap, and a real `motorcycle` costing
+  # model instead of car durations.
+  #
+  # Deliberately NOT using tile_urls. This host has 3.8 GB RAM and runs production
+  # alongside it; building the 556 MB South India extract here risks the OOM killer
+  # taking down routes_api/postgres. Instead ./valhalla_tiles holds a pre-clipped
+  # Coimbatore PBF (bbox 76.60,10.70 -> 77.30,11.35, derived from the actual
+  # delivery coordinates in delivery_details.csv, which span only ~19 km). Valhalla
+  # builds from any .pbf it finds in /custom_files.
+  #
+  # To re-clip after an OSM refresh, on the host:
+  #   osmium extract -b 76.60,10.70,77.30,11.35 <southern-zone>.osm.pbf \
+  #     -o valhalla_tiles/coimbatore.osm.pbf
+  #   docker compose up -d --force-recreate valhalla   # with force_rebuild=True
+  valhalla:
+    image: ghcr.io/valhalla/valhalla-scripted:latest
+    container_name: routes_valhalla
+    restart: unless-stopped
+    environment:
+      - serve_tiles=True
+      - force_rebuild=False
+      - build_elevation=False
+      # India drives on the left; admin regions are what teach Valhalla that.
+      - build_admins=True
+      - build_time_zones=True
+      # Reuse built tiles on restart instead of rebuilding from the PBF.
+      - use_tiles_ignore_pbf=True
+    # Hard cap so a runaway tile build gets killed instead of the OOM killer
+    # picking off production containers on this 3.8 GB host.
+    mem_limit: 2g
+    volumes:
+      - ./valhalla_tiles:/custom_files
+    # Loopback-only: the API reaches Valhalla over the docker network, so this
+    # is purely for local probing. Valhalla has no auth — do not bind 0.0.0.0.
+    ports:
+      - "127.0.0.1:8002:8002"
+    networks:
+      - frontend

ml_data/faiss_history/delivery_history.db.meta

@@ -1,7 +0,0 @@
-{
-  "source": "db",
-  "csv_mtime": 0,
-  "record_count": 4348,
-  "pattern_zones": 192,
-  "clear_patterns": 145
-}

ml_data/faiss_history/delivery_history.meta

@@ -1,7 +0,0 @@
-{
-  "source": "csv",
-  "csv_mtime": 1779805148.3161151,
-  "record_count": 3610,
-  "pattern_zones": 173,
-  "clear_patterns": 127
-}

ml_data/reports/tuning_20260406_101305.json

@@ -1,193 +0,0 @@
-{
-  "timestamp": "2026-04-06T10:13:05.823653",
-  "training_rows": 42,
-  "n_trials": 150,
-  "best_predicted_quality": 74.73,
-  "best_params": {
-    "max_pickup_distance_km": 7.314920981190028,
-    "max_kitchen_distance_km": 4.8848250212533415,
-    "max_orders_per_rider": 17,
-    "ideal_load": 6,
-    "workload_balance_threshold": 0.5921999872066053,
-    "workload_penalty_weight": 169.06315001171424,
-    "distance_penalty_weight": 1.1915857216557537,
-    "cluster_radius_km": 3.558231774658527,
-    "search_time_limit_seconds": 9,
-    "road_factor": 1.3099009557863963
-  },
-  "validation": {
-    "r2_score": 0.5681,
-    "mae": 8.266,
-    "folds": 5,
-    "trust_level": "acceptable",
-    "trust_score": 2,
-    "interpretation": "Predictions off by +/-8.3 pts (R2=0.57, trust=acceptable)"
-  },
-  "baseline_stats": {
-    "avg_quality": 57.09,
-    "best_quality": 94.11,
-    "worst_quality": 30.0,
-    "sample_size": 42
-  },
-  "feature_importance": {
-    "rolling_avg_quality_5": 38.71,
-    "quality_delta_10": 20.33,
-    "num_orders": 19.03,
-    "hour": 13.32,
-    "num_riders": 7.86,
-    "day_of_week": 0.75
-  },
-  "top_trials": [
-    {
-      "params": {
-        "max_pickup_distance_km": 7.314920981190028,
-        "max_kitchen_distance_km": 4.8848250212533415,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.5921999872066053,
-        "workload_penalty_weight": 169.06315001171424,
-        "distance_penalty_weight": 1.1915857216557537,
-        "cluster_radius_km": 3.558231774658527,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3099009557863963
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.237104762434787,
-        "max_kitchen_distance_km": 7.550474896953535,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.5890554510417833,
-        "workload_penalty_weight": 185.96752993783272,
-        "distance_penalty_weight": 1.31602349600973,
-        "cluster_radius_km": 3.8526888873500074,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.319424296170459
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.436288448216707,
-        "max_kitchen_distance_km": 4.919546048309047,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.591148300459874,
-        "workload_penalty_weight": 168.30230726961742,
-        "distance_penalty_weight": 1.2493078409207465,
-        "cluster_radius_km": 3.556472953280938,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3302725639166417
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.325529314582726,
-        "max_kitchen_distance_km": 4.649587629606767,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.590873440986851,
-        "workload_penalty_weight": 168.438723345984,
-        "distance_penalty_weight": 1.1422464826358736,
-        "cluster_radius_km": 3.849796402096792,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3098146276588136
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.280718743288656,
-        "max_kitchen_distance_km": 4.619748595285139,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.5876942359983096,
-        "workload_penalty_weight": 175.5427402190054,
-        "distance_penalty_weight": 1.234091340676156,
-        "cluster_radius_km": 3.596367136362332,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3269755250627069
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.492629035525449,
-        "max_kitchen_distance_km": 4.249165472314659,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.6002837964896123,
-        "workload_penalty_weight": 173.37495123965752,
-        "distance_penalty_weight": 1.1908166833096747,
-        "cluster_radius_km": 3.543115179563585,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3281385493416102
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.171116733882171,
-        "max_kitchen_distance_km": 4.315631232490625,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.5943191618773355,
-        "workload_penalty_weight": 167.9049180092559,
-        "distance_penalty_weight": 1.173862334055753,
-        "cluster_radius_km": 3.596417271778823,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.310786907921741
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.398947077752536,
-        "max_kitchen_distance_km": 4.573012985025286,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.588382785737964,
-        "workload_penalty_weight": 171.56836325664975,
-        "distance_penalty_weight": 1.2420650483510813,
-        "cluster_radius_km": 3.821463414056532,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3094083355796955
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.413205466370695,
-        "max_kitchen_distance_km": 4.882557712376696,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.5938285091458789,
-        "workload_penalty_weight": 169.18170748664954,
-        "distance_penalty_weight": 1.2065256426925077,
-        "cluster_radius_km": 3.567236434547114,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3261515305765197
-      },
-      "score": 74.7316665649414
-    },
-    {
-      "params": {
-        "max_pickup_distance_km": 7.321140036068694,
-        "max_kitchen_distance_km": 4.591113450191565,
-        "max_orders_per_rider": 17,
-        "ideal_load": 6,
-        "workload_balance_threshold": 0.595465392809937,
-        "workload_penalty_weight": 168.84486091695908,
-        "distance_penalty_weight": 1.1874534780471975,
-        "cluster_radius_km": 3.5809336353167915,
-        "search_time_limit_seconds": 9,
-        "road_factor": 1.3136308854673249
-      },
-      "score": 74.7316665649414
-    }
-  ],
-  "pareto_frontier": []
-}

ml_data/reports/tuning_20260406_111521.json

@@ -1,195 +0,0 @@
-{
-  "timestamp": "2026-04-06T11:15:21.558462",
-  "training_rows": 46,
-  "n_trials": 100,
-  "best_predicted_quality": 86.11,
-  "best_params": {
-    "max_pickup_distance_km": 6.392662787995912,
-    "max_kitchen_distance_km": 4.190289209141789,
-    "max_orders_per_rider": 16,
-    "ideal_load": 6,
-    "workload_balance_threshold": 0.5872426915922554,
-    "workload_penalty_weight": 159.52479411472802,
-    "distance_penalty_weight": 1.3976431795210602,
-    "cluster_radius_km": 3.8669714076918487,
-    "search_time_limit_seconds": 10,
-    "road_factor": 1.351027788721731
-  },
-  "validation": {
-    "r2_score": 0.6054,
-    "mae": 7.772,
-    "folds": 5,
-    "trust_level": "good",
-    "trust_score": 3,
-    "interpretation": "Predictions off by +/-7.8 pts (R2=0.61, trust=good)"
-  },
-  "baseline_stats": {
-    "avg_quality": 60.17,
-    "best_quality": 95.0,
-    "worst_quality": 30.0,
-    "sample_size": 46
-  },
-  "feature_importance": {
-    "rolling_avg_quality_5": 37.57,
-    "quality_delta_10": 20.49,
-    "num_orders": 19.39,
-    "hour": 14.34,
-    "num_riders": 7.31,
-    "day_of_week": 0.7,
-    "max_pickup_distance_km": 0.11,
-    "max_kitchen_distance_km": 0.09
-  },
-  "top_trials": [
-    {
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ml_data/reports/tuning_20260406_112705.json

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-{
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ml_data/reports/tuning_20260406_113420.json

@@ -1,197 +0,0 @@
-{
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ml_data/reports/tuning_20260407_015938.json

@@ -1,201 +0,0 @@
-{
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ml_data/reports/tuning_20260407_044422.json

@@ -1,201 +0,0 @@
-{
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ml_data/reports/tuning_20260407_053406.json

@@ -1,201 +0,0 @@
-{
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ml_data/reports/tuning_20260407_055038.json

@@ -1,202 +0,0 @@
-{
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ml_data/reports/tuning_20260407_055520.json

@@ -1,202 +0,0 @@
-{
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ml_data/reports/tuning_20260407_061251.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260407_072654.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260407_073036.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260407_083837.json

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-{
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ml_data/reports/tuning_20260407_100305.json

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-{
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ml_data/reports/tuning_20260407_110353.json

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-{
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ml_data/reports/tuning_20260407_111751.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260407_140402.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260408_015925.json

@@ -1,203 +0,0 @@
-{
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ml_data/reports/tuning_20260408_054840.json

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-{
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ml_data/reports/tuning_20260408_060150.json

@@ -1,203 +0,0 @@
-{
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