deploy: groups + search-point API

backend/export_data.py

@@ -0,0 +1,59 @@
+import csv
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from config import supabase
+
+
+def export_streets(output_path="export_streets.csv"):
+    all_data = []
+    page_size = 1000
+    offset = 0
+    while True:
+        result = (
+            supabase.table("Streets")
+            .select("id, name")
+            .order("id")
+            .range(offset, offset + page_size - 1)
+            .execute()
+        )
+        rows = result.data or []
+        all_data.extend(rows)
+        if len(rows) < page_size:
+            break
+        offset += page_size
+    with open(output_path, "w", encoding="utf-8", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=["id", "name"])
+        writer.writeheader()
+        writer.writerows(all_data)
+    print(f"Exported {len(all_data)} streets → {output_path}")
+
+
+def export_positions(output_path="export_positions.csv"):
+    all_data = []
+    page_size = 1000
+    offset = 0
+    while True:
+        result = (
+            supabase.table("Positions")
+            .select("id, long, lat, streets")
+            .order("id")
+            .range(offset, offset + page_size - 1)
+            .execute()
+        )
+        rows = result.data or []
+        all_data.extend(rows)
+        if len(rows) < page_size:
+            break
+        offset += page_size
+    with open(output_path, "w", encoding="utf-8", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=["id", "long", "lat", "streets"])
+        writer.writeheader()
+        writer.writerows(all_data)
+    print(f"Exported {len(all_data)} positions → {output_path}")
+
+
+if __name__ == "__main__":
+    export_streets()
+    export_positions()

backend/export_positions.csv

@@ -0,0 +1,335 @@
+id,long,lat,streets
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+    "id": 321,
+    "long": 106.657176896438,
+    "lat": 10.7907837853502
+  },
+  {
+    "id": 322,
+    "long": 106.66647941375,
+    "lat": 10.7858089191921
+  },
+  {
+    "id": 323,
+    "long": 106.67711535775,
+    "lat": 10.7801728398752
+  },
+  {
+    "id": 324,
+    "long": 106.678641663375,
+    "lat": 10.7793384316921
+  },
+  {
+    "id": 325,
+    "long": 106.6837025715,
+    "lat": 10.776662028086
+  },
+  {
+    "id": 326,
+    "long": 106.686835490062,
+    "lat": 10.7749932117198
+  },
+  {
+    "id": 327,
+    "long": 106.688281469437,
+    "lat": 10.7742375212899
+  },
+  {
+    "id": 328,
+    "long": 106.628867833563,
+    "lat": 10.7820384135576
+  },
+  {
+    "id": 329,
+    "long": 106.621429123,
+    "lat": 10.7843526696255
+  },
+  {
+    "id": 330,
+    "long": 106.622023581188,
+    "lat": 10.7860844258669
+  },
+  {
+    "id": 331,
+    "long": 106.623212447563,
+    "lat": 10.7897998589459
+  },
+  {
+    "id": 332,
+    "long": 106.620127741563,
+    "lat": 10.7804168649098
+  },
+  {
+    "id": 333,
+    "long": 106.627036259688,
+    "lat": 10.7717421124827
+  },
+  {
+    "id": 334,
+    "long": 106.632643446375,
+    "lat": 10.755808863939
+  },
+  {
+    "id": 335,
+    "long": 106.631872257375,
+    "lat": 10.7543760872399
+  },
+  {
+    "id": 336,
+    "long": 106.631534862188,
+    "lat": 10.7534943785021
+  },
+  {
+    "id": 337,
+    "long": 106.632386383375,
+    "lat": 10.7524552217753
+  }
+]

backend/export_streets.csv

@@ -0,0 +1,104 @@
+id,name
+1,Võ Văn Kiệt
+2,Quốc lộ 13
+3,Đinh Bộ Lĩnh
+4,Xô Viết Nghệ Tĩnh
+5,Phạm Văn Đồng
+6,Nguyễn Xí
+7,Chu Văn An
+8,Xa lộ Hà Nội
+9,Võ Nguyên Giáp
+10,Mai Chí Thọ
+11,Nguyễn Thị Định
+12,Đồng Văn Cống
+13,Nguyễn Duy Trinh
+14,Vành đai 2
+15,Liên Phường
+16,Võ Chí Công
+17,Đỗ Xuân Hợp
+18,Lã Xuân Oai
+19,Nguyễn Xiển
+20,Quốc lộ 22
+21,Trường Chinh
+22,Cộng Hòa
+23,Hoàng Văn Thụ
+24,Quang Trung
+25,Tô Ký
+26,Nguyễn Ảnh Thủ
+27,Phan Văn Hớn
+28,Lý Thái Tổ
+29,Quốc lộ 1A Bình Chánh
+30,Kinh Dương Vương
+31,An Dương Vương
+32,Nguyễn Văn Linh
+33,Hồng Bàng
+34,Nguyễn Hữu Thọ
+35,Huỳnh Tấn Phát
+36,Nguyễn Chí Thanh
+37,Nguyễn Tất Thành
+38,Tôn Đức Thắng
+39,Khánh Hội
+40,Điện Biên Phủ
+41,Nguyễn Thị Minh Khai
+42,Võ Thị Sáu
+43,Nam Kỳ Khởi Nghĩa
+44,Hai Bà Trưng
+45,Pasteur
+46,Cách Mạng Tháng Tám
+47,Lý Thường Kiệt
+48,Ba Tháng Hai
+49,Nguyễn Văn Cừ
+50,Trần Hưng Đạo
+51,Tân Kỳ Tân Quý
+52,Tân Sơn Nhì
+53,Lê Tấn Quốc
+54,Lê Trọng Tấn
+55,Nguyễn Cửu Đàm
+56,Nguyễn Quý Anh
+57,Cầu Xéo
+58,Diệp Minh Châu
+59,Trương Vĩnh Ký
+60,Gò Dầu
+61,Độc Lập
+62,Luỹ Bán Bích
+63,Vườn Lài
+64,Thống Nhất
+65,Hồng Lạc
+66,Âu Cơ
+67,Thoại Ngọc Hầu
+68,Đồng Đen
+69,Lạc Long Quân
+70,Phan Huy Ích
+71,Trương Công Định
+72,Hoàng Hoa Thám
+73,C12 - Sân Bay
+74,Bình Thới
+75,Lãnh Binh Thăng
+76,Ông ích Khiêm
+77,Hoà Bình
+78,Lê Đại Hành
+79,Nguyễn Thị Nhỏ
+80,Thiên Phước
+81,Lữ Gia
+82,Tô Hiến Thành
+83,Thành Thái
+84,Bắc Hải
+85,Sư Vạn Hạnh
+86,Đồng Nai
+87,Hồ Bá Kiện
+88,Nguyễn Tri Phương
+89,3 Tháng 2
+90,Lê Hồng Phong
+91,Cao Thắng
+92,Ngô Gia Tự
+93,Hùng Vương
+94,Trần Phú
+95,Nguyễn Trãi
+96,Minh Phụng
+97,Bình Tiên
+98,Phạm Phú Thứ
+99,Hậu Giang
+100,Nguyễn Thị Nhỏ Quận 6
+101,Nguyễn Sơn
+102,Văn Cao
+103,Đặng Nguyên Cẩn

backend/export_streets.json

@@ -0,0 +1,414 @@
+[
+  {
+    "id": 1,
+    "name": "Võ Văn Kiệt"
+  },
+  {
+    "id": 2,
+    "name": "Quốc lộ 13"
+  },
+  {
+    "id": 3,
+    "name": "Đinh Bộ Lĩnh"
+  },
+  {
+    "id": 4,
+    "name": "Xô Viết Nghệ Tĩnh"
+  },
+  {
+    "id": 5,
+    "name": "Phạm Văn Đồng"
+  },
+  {
+    "id": 6,
+    "name": "Nguyễn Xí"
+  },
+  {
+    "id": 7,
+    "name": "Chu Văn An"
+  },
+  {
+    "id": 8,
+    "name": "Xa lộ Hà Nội"
+  },
+  {
+    "id": 9,
+    "name": "Võ Nguyên Giáp"
+  },
+  {
+    "id": 10,
+    "name": "Mai Chí Thọ"
+  },
+  {
+    "id": 11,
+    "name": "Nguyễn Thị Định"
+  },
+  {
+    "id": 12,
+    "name": "Đồng Văn Cống"
+  },
+  {
+    "id": 13,
+    "name": "Nguyễn Duy Trinh"
+  },
+  {
+    "id": 14,
+    "name": "Vành đai 2"
+  },
+  {
+    "id": 15,
+    "name": "Liên Phường"
+  },
+  {
+    "id": 16,
+    "name": "Võ Chí Công"
+  },
+  {
+    "id": 17,
+    "name": "Đỗ Xuân Hợp"
+  },
+  {
+    "id": 18,
+    "name": "Lã Xuân Oai"
+  },
+  {
+    "id": 19,
+    "name": "Nguyễn Xiển"
+  },
+  {
+    "id": 20,
+    "name": "Quốc lộ 22"
+  },
+  {
+    "id": 21,
+    "name": "Trường Chinh"
+  },
+  {
+    "id": 22,
+    "name": "Cộng Hòa"
+  },
+  {
+    "id": 23,
+    "name": "Hoàng Văn Thụ"
+  },
+  {
+    "id": 24,
+    "name": "Quang Trung"
+  },
+  {
+    "id": 25,
+    "name": "Tô Ký"
+  },
+  {
+    "id": 26,
+    "name": "Nguyễn Ảnh Thủ"
+  },
+  {
+    "id": 27,
+    "name": "Phan Văn Hớn"
+  },
+  {
+    "id": 28,
+    "name": "Lý Thái Tổ"
+  },
+  {
+    "id": 29,
+    "name": "Quốc lộ 1A Bình Chánh"
+  },
+  {
+    "id": 30,
+    "name": "Kinh Dương Vương"
+  },
+  {
+    "id": 31,
+    "name": "An Dương Vương"
+  },
+  {
+    "id": 32,
+    "name": "Nguyễn Văn Linh"
+  },
+  {
+    "id": 33,
+    "name": "Hồng Bàng"
+  },
+  {
+    "id": 34,
+    "name": "Nguyễn Hữu Thọ"
+  },
+  {
+    "id": 35,
+    "name": "Huỳnh Tấn Phát"
+  },
+  {
+    "id": 36,
+    "name": "Nguyễn Chí Thanh"
+  },
+  {
+    "id": 37,
+    "name": "Nguyễn Tất Thành"
+  },
+  {
+    "id": 38,
+    "name": "Tôn Đức Thắng"
+  },
+  {
+    "id": 39,
+    "name": "Khánh Hội"
+  },
+  {
+    "id": 40,
+    "name": "Điện Biên Phủ"
+  },
+  {
+    "id": 41,
+    "name": "Nguyễn Thị Minh Khai"
+  },
+  {
+    "id": 42,
+    "name": "Võ Thị Sáu"
+  },
+  {
+    "id": 43,
+    "name": "Nam Kỳ Khởi Nghĩa"
+  },
+  {
+    "id": 44,
+    "name": "Hai Bà Trưng"
+  },
+  {
+    "id": 45,
+    "name": "Pasteur"
+  },
+  {
+    "id": 46,
+    "name": "Cách Mạng Tháng Tám"
+  },
+  {
+    "id": 47,
+    "name": "Lý Thường Kiệt"
+  },
+  {
+    "id": 48,
+    "name": "Ba Tháng Hai"
+  },
+  {
+    "id": 49,
+    "name": "Nguyễn Văn Cừ"
+  },
+  {
+    "id": 50,
+    "name": "Trần Hưng Đạo"
+  },
+  {
+    "id": 51,
+    "name": "Tân Kỳ Tân Quý"
+  },
+  {
+    "id": 52,
+    "name": "Tân Sơn Nhì"
+  },
+  {
+    "id": 53,
+    "name": "Lê Tấn Quốc"
+  },
+  {
+    "id": 54,
+    "name": "Lê Trọng Tấn"
+  },
+  {
+    "id": 55,
+    "name": "Nguyễn Cửu Đàm"
+  },
+  {
+    "id": 56,
+    "name": "Nguyễn Quý Anh"
+  },
+  {
+    "id": 57,
+    "name": "Cầu Xéo"
+  },
+  {
+    "id": 58,
+    "name": "Diệp Minh Châu"
+  },
+  {
+    "id": 59,
+    "name": "Trương Vĩnh Ký"
+  },
+  {
+    "id": 60,
+    "name": "Gò Dầu"
+  },
+  {
+    "id": 61,
+    "name": "Độc Lập"
+  },
+  {
+    "id": 62,
+    "name": "Luỹ Bán Bích"
+  },
+  {
+    "id": 63,
+    "name": "Vườn Lài"
+  },
+  {
+    "id": 64,
+    "name": "Thống Nhất"
+  },
+  {
+    "id": 65,
+    "name": "Hồng Lạc"
+  },
+  {
+    "id": 66,
+    "name": "Âu Cơ"
+  },
+  {
+    "id": 67,
+    "name": "Thoại Ngọc Hầu"
+  },
+  {
+    "id": 68,
+    "name": "Đồng Đen"
+  },
+  {
+    "id": 69,
+    "name": "Lạc Long Quân"
+  },
+  {
+    "id": 70,
+    "name": "Phan Huy Ích"
+  },
+  {
+    "id": 71,
+    "name": "Trương Công Định"
+  },
+  {
+    "id": 72,
+    "name": "Hoàng Hoa Thám"
+  },
+  {
+    "id": 73,
+    "name": "C12 - Sân Bay"
+  },
+  {
+    "id": 74,
+    "name": "Bình Thới"
+  },
+  {
+    "id": 75,
+    "name": "Lãnh Binh Thăng"
+  },
+  {
+    "id": 76,
+    "name": "Ông ích Khiêm"
+  },
+  {
+    "id": 77,
+    "name": "Hoà Bình"
+  },
+  {
+    "id": 78,
+    "name": "Lê Đại Hành"
+  },
+  {
+    "id": 79,
+    "name": "Nguyễn Thị Nhỏ"
+  },
+  {
+    "id": 80,
+    "name": "Thiên Phước"
+  },
+  {
+    "id": 81,
+    "name": "Lữ Gia"
+  },
+  {
+    "id": 82,
+    "name": "Tô Hiến Thành"
+  },
+  {
+    "id": 83,
+    "name": "Thành Thái"
+  },
+  {
+    "id": 84,
+    "name": "Bắc Hải"
+  },
+  {
+    "id": 85,
+    "name": "Sư Vạn Hạnh"
+  },
+  {
+    "id": 86,
+    "name": "Đồng Nai"
+  },
+  {
+    "id": 87,
+    "name": "Hồ Bá Kiện"
+  },
+  {
+    "id": 88,
+    "name": "Nguyễn Tri Phương"
+  },
+  {
+    "id": 89,
+    "name": "3 Tháng 2"
+  },
+  {
+    "id": 90,
+    "name": "Lê Hồng Phong"
+  },
+  {
+    "id": 91,
+    "name": "Cao Thắng"
+  },
+  {
+    "id": 92,
+    "name": "Ngô Gia Tự"
+  },
+  {
+    "id": 93,
+    "name": "Hùng Vương"
+  },
+  {
+    "id": 94,
+    "name": "Trần Phú"
+  },
+  {
+    "id": 95,
+    "name": "Nguyễn Trãi"
+  },
+  {
+    "id": 96,
+    "name": "Minh Phụng"
+  },
+  {
+    "id": 97,
+    "name": "Bình Tiên"
+  },
+  {
+    "id": 98,
+    "name": "Phạm Phú Thứ"
+  },
+  {
+    "id": 99,
+    "name": "Hậu Giang"
+  },
+  {
+    "id": 100,
+    "name": "Nguyễn Thị Nhỏ Quận 6"
+  },
+  {
+    "id": 101,
+    "name": "Nguyễn Sơn"
+  },
+  {
+    "id": 102,
+    "name": "Văn Cao"
+  },
+  {
+    "id": 103,
+    "name": "Đặng Nguyên Cẩn"
+  }
+]

backend/main.py

@@ -8,7 +8,7 @@ from fastapi import FastAPI, HTTPException, Query, Body
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.responses import JSONResponse
+from starlette.responses import JSONResponse, Response
 from typing import Optional, List
 import logging
 
@@ -20,6 +20,7 @@ from models import (
     District, DistrictCreate, DistrictUpdate,
     Street, StreetCreate, StreetUpdate,
     Position, PositionCreate, PositionUpdate,
+    Group, GroupCreate, GroupUpdate,
 )
 from services.category_service import CategoryService
 from services.intent_service import IntentService
@@ -90,6 +91,42 @@ async def health_check():
         raise HTTPException(status_code=500, detail="Database connection failed")
 
 
+@app.get("/api/pois")
+async def list_pois(city_id: Optional[int] = Query(None), district_id: Optional[int] = Query(None)):
+    try:
+        query = supabase.table("Pois").select("id, name, address, type, lat, long, city_id, district_id")
+        if district_id is not None:
+            query = query.eq("district_id", district_id)
+        elif city_id is not None:
+            query = query.eq("city_id", city_id)
+        result = query.order("name").execute()
+        return result.data or []
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.put("/api/pois/{poi_id}")
+async def update_poi(poi_id: int, body: dict = Body(...)):
+    try:
+        return await MapsService.update_poi(poi_id, body)
+    except Exception as e:
+        logger.error(f"Error updating POI {poi_id}: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/pois/bulk")
+async def update_pois_bulk(payload: List[dict] = Body(...)):
+    """Bulk-update POIs.
+    Payload: [{poi_id, lat?, long?, name?, address?, type?}, ...]
+    """
+    try:
+        updated = await MapsService.update_pois_bulk(payload)
+        return {"updated": updated}
+    except Exception as e:
+        logger.error(f"Error updating POIs bulk: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 # ==================== CATEGORIES ENDPOINTS ====================
 
 @app.get("/api/categories", response_model=List[Category])
@@ -364,14 +401,47 @@ async def search_streets(payload: dict = Body(...)):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@app.post("/api/districts/search")
+async def search_districts(payload: dict = Body(...)):
+    """Search districts by text.
+    Payload: { text: str, city_id?: int, limit?: int }
+    Returns list of matches with score.
+    """
+    try:
+        text = payload.get('text')
+        if not text:
+            raise HTTPException(status_code=422, detail='text is required')
+        city_id = payload.get('city_id')
+        limit = int(payload.get('limit') or 10)
+        results = await MapsService.search_districts_by_text(text, city_id=city_id, limit=limit)
+        return { 'results': results }
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error searching districts: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @app.post("/api/streets", response_model=Street)
 async def create_street(street: StreetCreate):
-    """Create a new street"""
+    """Create a new street. Returns 409 with {error:'duplicate', street:{id,name}} if similar exists."""
     try:
         return await MapsService.create_street(street)
     except Exception as e:
-        logger.error(f"Error creating street: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
+        msg = str(e)
+        # Structured duplicate signal: "Failed to create street: DUPLICATE_STREET:<id>:<name>"
+        inner = msg.replace("Failed to create street: ", "")
+        if inner.startswith("DUPLICATE_STREET:"):
+            parts = inner.split(":", 2)  # ["DUPLICATE_STREET", "<id>", "<name>"]
+            dup_id   = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else None
+            dup_name = parts[2] if len(parts) > 2 else ""
+            from fastapi.responses import JSONResponse
+            return JSONResponse(
+                status_code=409,
+                content={"error": "duplicate", "street": {"id": dup_id, "name": dup_name}}
+            )
+        logger.error(f"Error creating street: {msg}")
+        raise HTTPException(status_code=500, detail=msg)
 
 
 @app.get("/api/streets/{street_id}", response_model=Street)
@@ -476,6 +546,80 @@ async def delete_position_cascade(position_id: int):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+# ==================== GROUPS ENDPOINTS ====================
+
+@app.get("/api/groups", response_model=List[Group])
+async def list_groups():
+    """Get all groups (used by the neighbor dropdown in Create Group)."""
+    try:
+        return await MapsService.get_groups()
+    except Exception as e:
+        logger.error(f"Error fetching groups: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/groups/{group_id}", response_model=Group)
+async def get_group(group_id: int):
+    try:
+        group = await MapsService.get_group_by_id(group_id)
+        if not group:
+            raise HTTPException(status_code=404, detail="Group not found")
+        return group
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/groups", response_model=Group)
+async def create_group(group: GroupCreate):
+    """Create a new group.
+    Body: { lat_center, long_center, streets: [street_id, ...], neighbor?: [group_id, ...] }
+    """
+    try:
+        return await MapsService.create_group(group)
+    except Exception as e:
+        logger.error(f"Error creating group: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.put("/api/groups/{group_id}", response_model=Group)
+async def update_group(group_id: int, group: GroupUpdate):
+    try:
+        return await MapsService.update_group(group_id, group)
+    except Exception as e:
+        logger.error(f"Error updating group {group_id}: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.delete("/api/groups/{group_id}")
+async def delete_group(group_id: int):
+    try:
+        await MapsService.delete_group(group_id)
+        return {"message": "Group deleted"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/pathfind")
+async def pathfind(payload: dict = Body(...)):
+    """A* pathfinding between two position IDs.
+    Body: { start_id: int, end_id: int }
+    Returns: { found, node_path, street_path, street_details, total_m }
+    """
+    try:
+        start_id = payload.get("start_id")
+        end_id = payload.get("end_id")
+        if start_id is None or end_id is None:
+            raise HTTPException(status_code=422, detail="start_id and end_id are required")
+        return await MapsService.pathfind(int(start_id), int(end_id))
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Pathfind error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @app.post("/api/search")
 async def search_nearest(payload: dict = Body(...)):
     """Find the nearest DB position for each query point.
@@ -494,6 +638,71 @@ async def search_nearest(payload: dict = Body(...)):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@app.post("/api/search/point")
+async def search_point(payload: dict = Body(...)):
+    """Nearest single DB position to a lat/long (2-step Group strategy).
+    Body: { lat, lon }  (also accepts long/lng for the longitude)
+    Returns: { found, position: {id,lat,lon,streets,street_names}|None, distance_m, group_id }
+    """
+    try:
+        lat = payload.get("lat")
+        lon = payload.get("lon")
+        if lon is None:
+            lon = payload.get("long", payload.get("lng"))
+        if lat is None or lon is None:
+            raise HTTPException(status_code=422, detail="lat and lon are required")
+        return await MapsService.nearest_position(float(lat), float(lon))
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error searching nearest point: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ==================== TEST / UTILITY ENDPOINTS ====================
+
+@app.get("/search/uuid")
+async def search_uuid(id: str = Query(..., description="ID string to search for in utest table")):
+    """Search the utest table for the most similar id string.
+    Returns the best-matching row and its normalised Levenshtein score (0.0 = exact).
+    """
+    try:
+        if not id or not id.strip():
+            raise HTTPException(status_code=422, detail="id query param is required")
+        return await MapsService.search_uuid(id.strip())
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"search_uuid error: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ==================== EXPORT ENDPOINTS ====================
+
+@app.get("/export/position.bin")
+async def export_position_bin():
+    """Export the full position graph as a compact binary file for A* pathfinding.
+
+    Format: POSBIN v1 — header (16B) + node ids (4B each) + edges (12B each).
+    Nodes contain id only; client maps back to DB to retrieve lat/lon/name.
+    Edges are undirected (from_id < to_id); client mirrors both directions for A*.
+    """
+    try:
+        data = await MapsService.export_graph_bin()
+        logger.info(f"Exported position.bin: {len(data)} bytes")
+        return Response(
+            content=data,
+            media_type="application/octet-stream",
+            headers={
+                "Content-Disposition": 'attachment; filename="position.bin"',
+                "Content-Length": str(len(data)),
+            },
+        )
+    except Exception as e:
+        logger.error(f"Error exporting position.bin: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 # Serve admin static files (must be mounted last, after all API routes)
 _admin_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'admin')
 if os.path.isdir(_admin_dir):

backend/models.py

@@ -165,3 +165,31 @@ class PositionUpdate(BaseModel):
     lane: Optional[int] = None
     tool: Optional[int] = None
     flooding: Optional[bool] = None
+
+
+# ==================== GROUPS MODEL ====================
+
+class Group(BaseModel):
+    id: Optional[int] = None
+    lat_center: Optional[float] = None
+    long_center: Optional[float] = None
+    streets: Optional[list] = None       # JSON array of street ids
+    neighbor: Optional[list] = None      # JSON array of group ids
+    created_at: Optional[str] = None
+
+    class Config:
+        from_attributes = True
+
+
+class GroupCreate(BaseModel):
+    lat_center: float
+    long_center: float
+    streets: List[int]
+    neighbor: Optional[List[int]] = None
+
+
+class GroupUpdate(BaseModel):
+    lat_center: Optional[float] = None
+    long_center: Optional[float] = None
+    streets: Optional[List[int]] = None
+    neighbor: Optional[List[int]] = None

backend/php_unserialize.py

@@ -0,0 +1,143 @@
+"""
+PHP unserialize — parse PHP serialized strings into Python objects.
+Usage:
+    python php_unserialize.py 'a:2:{s:3:"foo";i:1;s:3:"bar";b:1;}'
+    or import and call php_unserialize(data)
+"""
+
+import sys
+import json
+
+
+def php_unserialize(data: str):
+    """Deserialize a PHP serialized string into a Python object."""
+    data = data.strip()
+    obj, pos = _parse(data, 0)
+    return obj
+
+
+# ── Internal parser ──────────────────────────────────────────────────────────
+
+def _parse(s: str, pos: int):
+    if pos >= len(s):
+        raise ValueError(f"Unexpected end of data at position {pos}")
+
+    token = s[pos]
+
+    if token == 'N':                        # null  →  N;
+        return None, pos + 2               # skip 'N' and ';'
+
+    if token == 'b':                        # bool  →  b:0; or b:1;
+        val = s[pos + 2] == '1'
+        return val, pos + 4
+
+    if token == 'i':                        # int   →  i:123;
+        end = s.index(';', pos)
+        return int(s[pos + 2:end]), end + 1
+
+    if token == 'd':                        # float →  d:1.5;
+        end = s.index(';', pos)
+        return float(s[pos + 2:end]), end + 1
+
+    if token == 's':                        # string → s:3:"foo";
+        colon = s.index(':', pos + 2)
+        length = int(s[pos + 2:colon])
+        # skip colon and opening quote
+        start = colon + 2
+        value = s[start:start + length]
+        return value, start + length + 2    # skip closing '";'
+
+    if token == 'a':                        # array  → a:2:{...}
+        colon = s.index(':', pos + 2)
+        count = int(s[pos + 2:colon])
+        pos = colon + 2                     # skip past '{'
+        result = {}
+        for _ in range(count):
+            key, pos = _parse(s, pos)
+            val, pos = _parse(s, pos)
+            result[key] = val
+        return result, pos + 1             # skip '}'
+
+    if token == 'O':                        # object → O:9:"ClassName":2:{...}
+        # class name length
+        colon1 = s.index(':', pos + 2)
+        cls_len = int(s[pos + 2:colon1])
+        cls_start = colon1 + 2
+        class_name = s[cls_start:cls_start + cls_len]
+        # property count
+        colon2 = s.index(':', cls_start + cls_len + 2)
+        prop_count = int(s[cls_start + cls_len + 2:colon2])
+        pos = colon2 + 2                   # skip past '{'
+        props = {}
+        for _ in range(prop_count):
+            key, pos = _parse(s, pos)
+            val, pos = _parse(s, pos)
+            props[key] = val
+        result = {"__class__": class_name, **props}
+        return result, pos + 1            # skip '}'
+
+    if token == 'C':                        # custom serialized → C:classname:len:{data}
+        colon1 = s.index(':', pos + 2)
+        cls_len = int(s[pos + 2:colon1])
+        cls_start = colon1 + 2
+        class_name = s[cls_start:cls_start + cls_len]
+        colon2 = s.index(':', cls_start + cls_len + 1)
+        data_len = int(s[cls_start + cls_len + 1:colon2])
+        data_start = colon2 + 2
+        raw = s[data_start:data_start + data_len]
+        result = {"__class__": class_name, "__data__": raw}
+        return result, data_start + data_len + 1
+
+    raise ValueError(f"Unknown type token '{token}' at position {pos}")
+
+
+# ── Serializer ───────────────────────────────────────────────────────────────
+
+def php_serialize(obj) -> str:
+    """Serialize a Python object back into a PHP serialized string."""
+    if obj is None:
+        return 'N;'
+    if isinstance(obj, bool):
+        return f'b:{1 if obj else 0};'
+    if isinstance(obj, int):
+        return f'i:{obj};'
+    if isinstance(obj, float):
+        return f'd:{obj};'
+    if isinstance(obj, str):
+        return f's:{len(obj.encode("utf-8"))}:"{obj}";'
+    if isinstance(obj, (list, tuple)):
+        parts = ''.join(php_serialize(i) + php_serialize(v) for i, v in enumerate(obj))
+        return f'a:{len(obj)}:{{{parts}}}'
+    if isinstance(obj, dict):
+        # Object with __class__ key → O type
+        if '__class__' in obj:
+            class_name = obj['__class__']
+            props = {k: v for k, v in obj.items() if k != '__class__'}
+            parts = ''.join(php_serialize(k) + php_serialize(v) for k, v in props.items())
+            return f'O:{len(class_name)}:"{class_name}":{len(props)}:{{{parts}}}'
+        parts = ''.join(php_serialize(k) + php_serialize(v) for k, v in obj.items())
+        return f'a:{len(obj)}:{{{parts}}}'
+    raise TypeError(f"Cannot serialize type: {type(obj)}")
+
+
+# ── Input string ─────────────────────────────────────────────────────────────
+
+PHP_STRING = 'a:4:{s:5:"_core";a:1:{s:19:"default_config_hash";s:43:"R4IF-ClDHXxblLcG0L7MgsLvfBIMAvi_skumNFQwkDc";}s:6:"module";a:276:{s:7:"address";i:0;s:13:"admin_toolbar";i:0;s:19:"admin_toolbar_tools";i:0;s:23:"advanced_text_formatter";i:0;s:10:"autologout";i:0;s:3:"ban";i:0;s:8:"big_pipe";i:0;s:5:"blazy";i:0;s:5:"block";i:0;s:13:"block_content";i:0;s:11:"block_field";i:0;s:17:"bootstrap_library";i:0;s:10:"breakpoint";i:0;s:13:"campaign_erun";i:0;s:16:"campaign_lead_ad";i:0;s:22:"campaign_sportify_2021";i:0;s:7:"captcha";i:0;s:9:"ckeditor5";i:0;s:17:"classy_paragraphs";i:0;s:8:"colorbox";i:0;s:7:"comment";i:0;s:6:"config";i:0;s:13:"config_filter";i:0;s:13:"config_ignore";i:0;s:12:"config_pages";i:0;s:12:"config_split";i:0;s:13:"config_update";i:0;s:7:"contact";i:0;s:7:"context";i:0;s:18:"context_breadcrumb";i:0;s:10:"context_ui";i:0;s:10:"contextual";i:0;s:12:"core_context";i:0;s:4:"crop";i:0;s:17:"csv_serialization";i:0;s:6:"ctools";i:0;s:12:"ctools_block";i:0;s:9:"datalayer";i:0;s:8:"datetime";i:0;s:10:"dropzonejs";i:0;s:20:"dropzonejs_eb_widget";i:0;s:15:"dsu_c_accordion";i:0;s:26:"dsu_c_background_component";i:0;s:18:"dsu_c_contentcycle";i:0;s:17:"dsu_c_entitycycle";i:0;s:22:"dsu_c_extend_component";i:0;s:19:"dsu_c_externalvideo";i:0;s:13:"dsu_c_gallery";i:0;s:11:"dsu_c_image";i:0;s:29:"dsu_c_link_document_container";i:0;s:21:"dsu_c_product_extends";i:0;s:18:"dsu_c_sharebuttons";i:0;s:19:"dsu_c_sideimagetext";i:0;s:12:"dsu_c_slider";i:0;s:10:"dsu_c_text";i:0;s:15:"dsu_c_text_card";i:0;s:14:"dsu_c_text_dyk";i:0;s:10:"dsu_c_view";i:0;s:8:"dsu_core";i:0;s:19:"dsu_ratings_reviews";i:0;s:6:"editor";i:0;s:5:"embed";i:0;s:7:"encrypt";i:0;s:14:"entity_browser";i:0;s:22:"entity_class_formatter";i:0;s:12:"entity_clone";i:0;s:12:"entity_embed";i:0;s:26:"entity_reference_revisions";i:0;s:17:"external_hreflang";i:0;s:12:"externalauth";i:0;s:6:"facets";i:0;s:8:"features";i:0;s:11:"features_ui";i:0;s:5:"field";i:0;s:11:"field_group";i:0;s:11:"field_timer";i:0;s:8:"field_ui";i:0;s:4:"file";i:0;s:12:"file_browser";i:0;s:28:"file_upload_secure_validator";i:0;s:6:"filter";i:0;s:8:"fivestar";i:0;s:4:"flag";i:0;s:11:"focal_point";i:0;s:11:"geolocation";i:0;s:5:"gigya";i:0;s:10:"gigya_raas";i:0;s:19:"gigya_user_deletion";i:0;s:16:"google_analytics";i:0;s:15:"google_optimize";i:0;s:18:"google_optimize_js";i:0;s:10:"google_tag";i:0;s:8:"honeypot";i:0;s:5:"image";i:0;s:17:"image_widget_crop";i:0;s:18:"inline_entity_form";i:0;s:9:"jquery_ui";i:0;s:22:"jquery_ui_autocomplete";i:0;s:16:"jquery_ui_button";i:0;s:23:"jquery_ui_checkboxradio";i:0;s:22:"jquery_ui_controlgroup";i:0;s:20:"jquery_ui_datepicker";i:0;s:19:"jquery_ui_draggable";i:0;s:19:"jquery_ui_droppable";i:0;s:14:"jquery_ui_menu";i:0;s:17:"jquery_ui_spinner";i:0;s:3:"key";i:0;s:8:"language";i:0;s:14:"layout_builder";i:0;s:38:"layout_builder_expose_all_field_blocks";i:0;s:16:"layout_discovery";i:0;s:14:"layout_library";i:0;s:4:"link";i:0;s:15:"link_attributes";i:0;s:33:"link_attributes_menu_link_content";i:0;s:8:"ln_adimo";i:0;s:10:"ln_article";i:0;s:20:"ln_c_background_only";i:0;s:22:"ln_c_buy_now_component";i:0;s:9:"ln_c_card";i:0;s:13:"ln_c_cardgrid";i:0;s:11:"ln_c_spacer";i:0;s:11:"ln_fusepump";i:0;s:11:"ln_hreflang";i:0;s:9:"ln_m_core";i:0;s:13:"ln_m_document";i:0;s:19:"ln_m_external_video";i:0;s:9:"ln_n_core";i:0;s:15:"ln_price_spider";i:0;s:10:"ln_product";i:0;s:11:"ln_sso_saml";i:0;s:17:"ln_tint_connector";i:0;s:6:"locale";i:0;s:14:"login_security";i:0;s:5:"media";i:0;s:13:"media_library";i:0;s:8:"memcache";i:0;s:17:"menu_link_content";i:0;s:7:"menu_ui";i:0;s:7:"metatag";i:0;s:16:"metatag_hreflang";i:0;s:18:"metatag_open_graph";i:0;s:27:"metatag_open_graph_products";i:0;s:21:"metatag_twitter_cards";i:0;s:7:"migrate";i:0;s:30:"migrate_example_advanced_setup";i:0;s:21:"migrate_example_setup";i:0;s:12:"migrate_plus";i:0;s:18:"migrate_source_csv";i:0;s:13:"migrate_tools";i:0;s:10:"milo_covid";i:0;s:16:"milo_gtm_tagging";i:0;s:14:"milo_teen_quiz";i:0;s:18:"milo_vietnam_pages";i:0;s:5:"mysql";i:0;s:4:"node";i:0;s:10:"noreferrer";i:0;s:16:"ogilvy_c_article";i:0;s:18:"ogilvy_c_milestone";i:0;s:26:"ogilvy_c_sharebuttons_zalo";i:0;s:13:"ogilvy_c_text";i:0;s:9:"ogilvy_co";i:0;s:30:"ogilvy_co_article_listing_3in1";i:0;s:26:"ogilvy_co_bar_notification";i:0;s:29:"ogilvy_co_carousel_fulllength";i:0;s:28:"ogilvy_co_carousel_imageetch";i:0;s:27:"ogilvy_co_ciam_gigya_screen";i:0;s:25:"ogilvy_co_content_related";i:0;s:20:"ogilvy_co_error_page";i:0;s:23:"ogilvy_co_faq_accordion";i:0;s:24:"ogilvy_co_faq_search_bar";i:0;s:38:"ogilvy_co_infographic_nutritionalvalue";i:0;s:26:"ogilvy_co_intro_fulllength";i:0;s:24:"ogilvy_co_masthead_short";i:0;s:30:"ogilvy_co_nutritionalindicator";i:0;s:34:"ogilvy_co_nutritionalindicator_tab";i:0;s:25:"ogilvy_co_partnerslisting";i:0;s:32:"ogilvy_co_recipedescription_list";i:0;s:34:"ogilvy_co_recipedetails_headerdesc";i:0;s:38:"ogilvy_co_recipedetails_productmention";i:0;s:34:"ogilvy_co_recipedetails_stepbystep";i:0;s:32:"ogilvy_co_reviews_ratings_teaser";i:0;s:29:"ogilvy_co_section_teaser_card";i:0;s:22:"ogilvy_co_sportscentre";i:0;s:32:"ogilvy_co_sportslanding_masthead";i:0;s:14:"ogilvy_co_tint";i:0;s:35:"ogilvy_co_videolisting_1hero_3small";i:0;s:23:"ogilvy_co_view_selector";i:0;s:20:"ogilvy_co_winnerlist";i:0;s:13:"ogilvy_common";i:0;s:12:"ogilvy_gigya";i:0;s:20:"ogilvy_module_extend";i:0;s:29:"ogilvy_recipe_structured_data";i:0;s:13:"ogilvy_search";i:0;s:19:"ogilvy_sport_center";i:0;s:26:"ogilvy_template_suggestion";i:0;s:7:"options";i:0;s:13:"options_table";i:0;s:6:"panels";i:0;s:10:"panels_ipe";i:0;s:18:"paragraphs_browser";i:0;s:19:"paragraphs_features";i:0;s:20:"paragraphs_previewer";i:0;s:31:"password_policy_character_types";i:0;s:26:"password_policy_characters";i:0;s:23:"password_policy_history";i:0;s:22:"password_policy_length";i:0;s:24:"password_policy_username";i:0;s:4:"path";i:0;s:10:"path_alias";i:0;s:6:"phpass";i:0;s:15:"project_browser";i:0;s:8:"real_aes";i:0;s:9:"recaptcha";i:0;s:12:"recaptcha_v3";i:0;s:8:"redirect";i:0;s:16:"responsive_image";i:0;s:4:"rest";i:0;s:20:"restrict_route_by_ip";i:0;s:6:"restui";i:0;s:9:"robotstxt";i:0;s:8:"samlauth";i:0;s:19:"samlauth_user_roles";i:0;s:9:"scheduler";i:0;s:14:"schema_article";i:0;s:14:"schema_metatag";i:0;s:19:"schema_organization";i:0;s:14:"schema_product";i:0;s:13:"schema_recipe";i:0;s:15:"schema_web_site";i:0;s:10:"search_api";i:0;s:13:"search_api_db";i:0;s:6:"seckit";i:0;s:13:"serialization";i:0;s:13:"session_limit";i:0;s:6:"shield";i:0;s:8:"shortcut";i:0;s:5:"slick";i:0;s:21:"slick_entityreference";i:0;s:16:"slick_paragraphs";i:0;s:8:"slick_ui";i:0;s:12:"social_media";i:0;s:15:"svg_image_field";i:0;s:6:"syslog";i:0;s:6:"system";i:0;s:8:"taxonomy";i:0;s:4:"text";i:0;s:5:"token";i:0;s:7:"toolbar";i:0;s:10:"twig_tweak";i:0;s:4:"user";i:0;s:17:"video_embed_field";i:0;s:21:"views_infinite_scroll";i:0;s:8:"views_ui";i:0;s:14:"viewsreference";i:0;s:25:"vn_campaign_canister_2021";i:0;s:9:"votingapi";i:0;s:7:"webform";i:0;s:13:"webform_gigya";i:0;s:12:"webform_rest";i:0;s:23:"webform_scheduled_email";i:0;s:10:"webform_ui";i:0;s:13:"webform_views";i:0;s:17:"xls_serialization";i:0;s:2:"ds";i:1;s:10:"dsu_c_core";i:1;s:8:"pathauto";i:1;s:10:"xmlsitemap";i:1;s:15:"password_policy";i:10;s:5:"views";i:10;s:10:"paragraphs";i:11;s:15:"paragraphs_sets";i:12;s:13:"paragraphs_ee";i:15;s:12:"dsu_security";i:50;s:9:"lightnest";i:1000;s:3:"shs";i:1000;}s:5:"theme";a:5:{s:16:"bootstrap_barrio";i:0;s:5:"seven";i:0;s:6:"stable";i:0;s:4:"milo";i:0;s:5:"claro";i:0;}s:7:"profile";s:9:"lightnest";}'
+
+# ── CLI ──────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    raw = sys.argv[1] if len(sys.argv) > 1 else PHP_STRING
+    try:
+        result = php_unserialize(raw)
+
+        # Remove milo_teen_quiz from module dict
+        if isinstance(result.get('module'), dict) and 'milo_teen_quiz' in result['module']:
+            del result['module']['milo_teen_quiz']
+            print("[removed] milo_teen_quiz from module", file=sys.stderr)
+
+        serialized = php_serialize(result)
+        print(serialized)
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)

backend/position.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cf7c3998a47e495367f5dae77c09b8040c30f99c006b78bee36232317eed970
+size 27852

backend/services/maps_service.py

@@ -4,6 +4,7 @@ from models import (
     District, DistrictCreate, DistrictUpdate,
     Street, StreetCreate, StreetUpdate,
     Position, PositionCreate, PositionUpdate,
+    Group, GroupCreate, GroupUpdate,
 )
 from typing import List, Optional
 import unicodedata
@@ -175,51 +176,24 @@ class MapsService:
                 return prev[-1]
 
             name_norm = _normalize(data.name)
-            city_id = data.city_id
-            district_id = data.district_id
 
-            # Fetch candidate existing streets in same district or city
-            query = supabase.table("Streets").select("id, name, district_id, city_id").order("name")
-            if district_id is not None:
-                query = query.eq("district_id", district_id)
-            elif city_id is not None:
-                query = query.eq("city_id", city_id)
-            existing_res = query.execute()
+            # Only check duplicates WITHIN THE SAME district -> cho phép cùng tên đường ở phường khác.
+            # Chỉ chặn khi trùng tên VÀ trùng district_id.
+            existing_res = supabase.table("Streets").select("id, name, district_id, city_id").eq("district_id", data.district_id).order("name").execute()
             existing_list = existing_res.data or []
             for item in existing_list:
                 existing_name = item.get('name') or ''
                 en_norm = _normalize(existing_name)
-                # exact or containment
-                if en_norm == name_norm or (name_norm and en_norm and (name_norm in en_norm or en_norm in name_norm)):
-                    raise Exception(f"Similar street exists: {existing_name} (id={item.get('id')})")
-                # small edit distance -> likely duplicate; threshold scales with length
+                if not en_norm:
+                    continue
+                # exact or containment match
+                if en_norm == name_norm or (name_norm and (name_norm in en_norm or en_norm in name_norm)):
+                    raise Exception(f"DUPLICATE_STREET:{item.get('id')}:{existing_name}")
+                # edit distance threshold scales with length
                 max_dist = max(2, int(max(len(en_norm), len(name_norm)) * 0.2))
                 dist = _levenshtein(en_norm, name_norm)
-                # debug log
-                try:
-                    import logging
-                    logging.getLogger(__name__).info(f"create_street: compare '{name_norm}' vs '{en_norm}' -> dist={dist} threshold={max_dist}")
-                except Exception:
-                    pass
                 if dist <= max_dist:
-                    raise Exception(f"Similar street exists: {existing_name} (id={item.get('id')})")
-
-            # Also perform a global fuzzy search to catch similar names in other districts/cities
-            try:
-                global_results = await MapsService.search_streets_by_text(data.name, district_id=None, city_id=None, limit=10)
-                # Accept if any match has a low score (<= 0.15)
-                for r in (global_results or []):
-                    if float(r.get('score', 1.0)) <= 0.15:
-                        raise Exception(f"Similar street exists elsewhere: {r.get('name')} (id={r.get('id')})")
-            except Exception as e:
-                # If search_streets_by_text raised an exception because of DB, re-raise as generic
-                if str(e).startswith('Failed to create street'):
-                    raise
-                # If we raised above due to similarity, pass it up
-                if 'Similar street exists' in str(e) or 'Similar street exists elsewhere' in str(e):
-                    raise
-                # otherwise ignore search errors and proceed with insert
-                pass
+                    raise Exception(f"DUPLICATE_STREET:{item.get('id')}:{existing_name}")
 
             # No similar found -> proceed to insert
             next_id = _next_id("Streets")
@@ -331,6 +305,83 @@ class MapsService:
         except Exception as e:
             raise Exception(f"Failed to search streets: {str(e)}")
 
+    @staticmethod
+    async def search_districts_by_text(text: str, city_id: Optional[int] = None, limit: int = 10) -> list:
+        """Search districts by text with normalization and fuzzy matching.
+        Matches against both `name` and `lname` (best score wins).
+        Returns list of dicts: {id, name, lname, city, score}
+        Lower score == better match.
+        """
+        try:
+            if not text or not str(text).strip():
+                return []
+            import re
+            def _normalize(s: str) -> str:
+                s = str(s or '').strip().lower()
+                s = unicodedata.normalize('NFKD', s)
+                s = ''.join(ch for ch in s if not unicodedata.combining(ch))
+                s = re.sub(r'[^0-9a-z\s]', ' ', s)
+                s = ' '.join(s.split())
+                return s
+
+            def _lev(a: str, b: str) -> int:
+                if a == b:
+                    return 0
+                if len(a) == 0:
+                    return len(b)
+                if len(b) == 0:
+                    return len(a)
+                prev = list(range(len(b) + 1))
+                for i, ca in enumerate(a, start=1):
+                    curr = [i]
+                    for j, cb in enumerate(b, start=1):
+                        insert_cost = curr[j-1] + 1
+                        delete_cost = prev[j] + 1
+                        replace_cost = prev[j-1] + (0 if ca == cb else 1)
+                        curr.append(min(insert_cost, delete_cost, replace_cost))
+                    prev = curr
+                return prev[-1]
+
+            def _score(query_norm: str, cand_norm: str) -> float:
+                if not cand_norm:
+                    return 1.0
+                if cand_norm == query_norm:
+                    return 0.0
+                if query_norm in cand_norm or cand_norm in query_norm:
+                    return 0.1
+                dist = _lev(query_norm, cand_norm)
+                max_len = max(len(query_norm), len(cand_norm), 1)
+                return dist / max_len
+
+            q = _normalize(text)
+            # Fetch candidate districts, optionally filtered by city
+            query = supabase.table("Districts").select("id, name, lname, city").order("name")
+            if city_id is not None:
+                query = query.eq("city", city_id)
+            res = query.execute()
+            candidates = res.data or []
+
+            scored = []
+            for item in candidates:
+                name  = item.get('name') or ''
+                lname = item.get('lname') or ''
+                score = _score(q, _normalize(name))
+                if lname:
+                    score = min(score, _score(q, _normalize(lname)))
+                scored.append({
+                    'id': item.get('id'),
+                    'name': name,
+                    'lname': item.get('lname'),
+                    'city': item.get('city'),
+                    'score': round(float(score), 3)
+                })
+
+            # sort by score asc, then by name
+            scored.sort(key=lambda x: (x['score'], x['name']))
+            return scored[:limit]
+        except Exception as e:
+            raise Exception(f"Failed to search districts: {str(e)}")
+
     # ==================== POSITIONS ====================
 
     @staticmethod
@@ -390,9 +441,12 @@ class MapsService:
     @staticmethod
     async def update_position(position_id: int, data: PositionUpdate) -> Position:
         try:
-            payload = {k: v for k, v in data.model_dump().items() if v is not None}
+            raw = {k: v for k, v in data.model_dump().items() if v is not None}
+            # Map model fields -> DB columns (x->long, y->lat); 'street_id' is not a column
+            col_map = {'x': 'long', 'y': 'lat'}
+            payload = {col_map.get(k, k): v for k, v in raw.items() if k != 'street_id'}
             result = supabase.table("Positions").update(payload).eq("id", position_id).execute()
-            return Position(**result.data[0])
+            return Position(**MapsService._db_pos_to_model(result.data[0]))
         except Exception as e:
             raise Exception(f"Failed to update position: {str(e)}")
 
@@ -478,6 +532,405 @@ class MapsService:
         except Exception as e:
             raise Exception(f"Failed to create/update positions bulk: {str(e)}")
 
+    # ==================== GROUPS ====================
+    @staticmethod
+    async def get_groups() -> List[Group]:
+        try:
+            result = supabase.table("Groups").select("*").order("id").execute()
+            return [Group(**item) for item in (result.data or [])]
+        except Exception as e:
+            raise Exception(f"Failed to fetch groups: {str(e)}")
+
+    @staticmethod
+    async def get_group_by_id(group_id: int) -> Optional[Group]:
+        try:
+            result = supabase.table("Groups").select("*").eq("id", group_id).limit(1).execute()
+            return Group(**result.data[0]) if result.data else None
+        except Exception as e:
+            raise Exception(f"Failed to fetch group: {str(e)}")
+
+    @staticmethod
+    async def create_group(data: GroupCreate) -> Group:
+        try:
+            next_id = _next_id("Groups")
+            payload = {
+                "id": next_id,
+                "lat_center": data.lat_center,
+                "long_center": data.long_center,
+                "streets": [int(s) for s in (data.streets or [])],
+                "neighbor": [int(n) for n in (data.neighbor or [])],
+            }
+            result = supabase.table("Groups").insert(payload).execute()
+            if not result.data:
+                raise Exception("Failed to insert group")
+            return Group(**result.data[0])
+        except Exception as e:
+            raise Exception(f"Failed to create group: {str(e)}")
+
+    @staticmethod
+    async def update_group(group_id: int, data: GroupUpdate) -> Group:
+        try:
+            payload = {}
+            for field in ("lat_center", "long_center", "streets", "neighbor"):
+                val = getattr(data, field)
+                if val is None:
+                    continue
+                payload[field] = [int(x) for x in val] if field in ("streets", "neighbor") else val
+            if not payload:
+                raise Exception("No valid fields to update")
+            result = supabase.table("Groups").update(payload).eq("id", group_id).execute()
+            if not result.data:
+                raise Exception(f"Group #{group_id} not found or update failed")
+            return Group(**result.data[0])
+        except Exception as e:
+            raise Exception(f"Failed to update group: {str(e)}")
+
+    @staticmethod
+    async def delete_group(group_id: int) -> bool:
+        try:
+            supabase.table("Groups").delete().eq("id", group_id).execute()
+            return True
+        except Exception as e:
+            raise Exception(f"Failed to delete group: {str(e)}")
+
+    @staticmethod
+    async def update_poi(poi_id: int, fields: dict) -> dict:
+        """Update a single Pois row with the provided fields (lat/long/name/address/type).
+        Returns the updated row.
+        """
+        try:
+            allowed = {"lat", "long", "name", "address", "type", "city_id", "district_id"}
+            payload = {k: v for k, v in (fields or {}).items() if k in allowed and v is not None}
+            if not payload:
+                raise Exception("No valid fields to update")
+            result = supabase.table("Pois").update(payload).eq("id", poi_id).execute()
+            if not result.data:
+                raise Exception(f"POI #{poi_id} not found or update failed")
+            return result.data[0]
+        except Exception as e:
+            raise Exception(f"Failed to update POI: {str(e)}")
+
+    @staticmethod
+    async def update_pois_bulk(payload: list) -> list:
+        """payload: [{poi_id, lat?, long?, name?, address?, type?}, ...]
+        Updates each POI by id. Returns list of updated rows.
+        """
+        updated = []
+        allowed = {"lat", "long", "name", "address", "type", "city_id", "district_id"}
+        try:
+            for item in payload or []:
+                poi_id = item.get("poi_id") or item.get("id")
+                if not poi_id:
+                    continue
+                fields = {k: v for k, v in item.items() if k in allowed and v is not None}
+                if not fields:
+                    continue
+                result = supabase.table("Pois").update(fields).eq("id", int(poi_id)).execute()
+                if result.data:
+                    updated.append(result.data[0])
+            return updated
+        except Exception as e:
+            raise Exception(f"Failed to update POIs bulk: {str(e)}")
+
+    # Path where position.bin is cached on disk (backend/position.bin)
+    _BIN_PATH: str = ""
+
+    @classmethod
+    def _bin_path(cls) -> str:
+        if not cls._BIN_PATH:
+            import os
+            cls._BIN_PATH = os.path.join(
+                os.path.dirname(os.path.abspath(__file__)), '..', 'position.bin'
+            )
+        return cls._BIN_PATH
+
+    @staticmethod
+    async def export_graph_bin() -> bytes:
+        """Serialize the position graph as a compact binary file for A* pathfinding.
+
+        Format (little-endian):
+          HEADER  (16 bytes): magic b"POSBIN\x01\x00" (8B) | node_count uint32 | edge_count uint32
+          NODES   (4B x node_count): sorted node ids as int32
+          EDGES   (12B x edge_count): from_id int32 | to_id int32 | weight_m float32
+              edges are undirected and stored with from_id < to_id.
+
+        Nodes contain id only — client maps back to DB for lat/lon/name.
+        Also writes the file to disk at backend/position.bin for pathfind() to consume.
+        """
+        import math
+        import struct
+
+        def haversine_m(lat1, lon1, lat2, lon2):
+            R = 6_371_000
+            phi1, phi2 = math.radians(lat1), math.radians(lat2)
+            dphi = math.radians(lat2 - lat1)
+            dlam = math.radians(lon2 - lon1)
+            a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlam / 2) ** 2
+            return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+
+        try:
+            result = supabase.table("Positions").select("id, lat, long, streets").execute()
+            all_positions = result.data or []
+
+            # Index valid positions (must have lat and long)
+            pos_by_id: dict = {}
+            for p in all_positions:
+                if p.get("lat") is not None and p.get("long") is not None:
+                    pos_by_id[p["id"]] = p
+
+            # Build street → list of position ids
+            street_to_pids: dict = {}
+            for pid, pos in pos_by_id.items():
+                for sid in (pos.get("streets") or []):
+                    street_to_pids.setdefault(sid, []).append(pid)
+
+            # Build edges: undirected, from_id < to_id, keep minimum weight per pair
+            edge_map: dict = {}  # (from_id, to_id) → weight_m
+            for sid, pids in street_to_pids.items():
+                for i in range(len(pids)):
+                    for j in range(i + 1, len(pids)):
+                        a_id, b_id = pids[i], pids[j]
+                        if a_id > b_id:
+                            a_id, b_id = b_id, a_id
+                        pa = pos_by_id[a_id]
+                        pb = pos_by_id[b_id]
+                        w = haversine_m(
+                            float(pa["lat"]), float(pa["long"]),
+                            float(pb["lat"]), float(pb["long"])
+                        )
+                        key = (a_id, b_id)
+                        if key not in edge_map or w < edge_map[key]:
+                            edge_map[key] = w
+
+            sorted_node_ids = sorted(pos_by_id.keys())
+            edges = [(a, b, w) for (a, b), w in edge_map.items()]
+
+            node_count = len(sorted_node_ids)
+            edge_count = len(edges)
+
+            buf = bytearray()
+            buf += struct.pack('<8sII', b"POSBIN\x01\x00", node_count, edge_count)
+            if node_count:
+                buf += struct.pack(f'<{node_count}i', *sorted_node_ids)
+            for a_id, b_id, w in edges:
+                buf += struct.pack('<iif', a_id, b_id, w)
+
+            data = bytes(buf)
+
+            # Save to disk so pathfind() can consume it without re-fetching DB
+            with open(MapsService._bin_path(), 'wb') as f:
+                f.write(data)
+
+            return data
+        except Exception as e:
+            raise Exception(f"Failed to export graph binary: {str(e)}")
+
+    @staticmethod
+    async def pathfind(start_id: int, end_id: int) -> dict:
+        """Run Dijkstra on the pre-built position graph stored in position.bin.
+
+        Graph is loaded from the binary file (no full DB fetch).
+        Only queries DB for: street arrays of nodes in the found path + street details.
+        Returns node path, ordered street ids, street details, and total distance.
+
+        Requires position.bin to exist — call GET /export/position.bin first to generate it.
+        """
+        import struct
+        import heapq
+        import os
+
+        bin_path = MapsService._bin_path()
+        if not os.path.exists(bin_path):
+            raise Exception(
+                "position.bin not found. Call GET /export/position.bin first to generate the graph file."
+            )
+
+        try:
+            with open(bin_path, 'rb') as f:
+                data = f.read()
+
+            # --- Parse header ---
+            magic, node_count, edge_count = struct.unpack_from('<8sII', data, 0)
+            if magic != b"POSBIN\x01\x00":
+                raise Exception("Invalid position.bin format — unexpected magic bytes")
+
+            # --- Parse node ids ---
+            node_ids: list = list(struct.unpack_from(f'<{node_count}i', data, 16))
+            node_set: set = set(node_ids)
+
+            if start_id not in node_set:
+                raise Exception(f"Start position #{start_id} not found in graph")
+            if end_id not in node_set:
+                raise Exception(f"End position #{end_id} not found in graph")
+            if start_id == end_id:
+                return {"found": True, "node_path": [start_id], "street_path": [], "street_details": [], "total_m": 0}
+
+            # --- Parse edges → build undirected adjacency ---
+            adjacency: dict = {nid: {} for nid in node_ids}
+            edge_offset = 16 + node_count * 4
+            for i in range(edge_count):
+                a, b, w = struct.unpack_from('<iif', data, edge_offset + i * 12)
+                # bin stores from_id < to_id; mirror both directions
+                if b not in adjacency[a] or adjacency[a][b] > w:
+                    adjacency[a][b] = w
+                    adjacency[b][a] = w
+
+            # --- Dijkstra (no lat/lon in bin → no heuristic needed) ---
+            g: dict = {nid: float("inf") for nid in node_ids}
+            g[start_id] = 0.0
+            prev: dict = {nid: None for nid in node_ids}
+            heap = [(0.0, start_id)]
+
+            while heap:
+                cost, u = heapq.heappop(heap)
+                if u == end_id:
+                    break
+                if cost > g[u]:
+                    continue
+                for v, w in adjacency[u].items():
+                    ng = g[u] + w
+                    if ng < g[v]:
+                        g[v] = ng
+                        prev[v] = u
+                        heapq.heappush(heap, (ng, v))
+
+            if g[end_id] == float("inf"):
+                return {"found": False, "node_path": [], "street_path": [], "street_details": [], "total_m": 0}
+
+            # --- Reconstruct node path ---
+            path: list = []
+            cur = end_id
+            while cur is not None:
+                path.append(cur)
+                cur = prev[cur]
+            path.reverse()
+
+            # --- Minimal DB query: only fetch streets array for nodes in path ---
+            pos_result = supabase.table("Positions") \
+                .select("id, streets") \
+                .in_("id", path).execute()
+            pos_streets: dict = {
+                p["id"]: (p.get("streets") or [])
+                for p in (pos_result.data or [])
+            }
+
+            # --- Extract ordered unique street IDs from consecutive node pairs ---
+            street_path: list = []
+            seen_streets: set = set()
+            for i in range(len(path) - 1):
+                shared = (set(pos_streets.get(path[i], [])) &
+                          set(pos_streets.get(path[i + 1], [])))
+                for sid in shared:
+                    if sid not in seen_streets:
+                        seen_streets.add(sid)
+                        street_path.append(sid)
+
+            # --- Fetch street details for the path ---
+            street_details: list = []
+            if street_path:
+                sr = supabase.table("Streets") \
+                    .select("id, name, type, district_id, city_id") \
+                    .in_("id", street_path).execute()
+                street_map = {s["id"]: s for s in (sr.data or [])}
+                street_details = [street_map[sid] for sid in street_path if sid in street_map]
+
+            return {
+                "found": True,
+                "node_path": path,
+                "street_path": street_path,
+                "street_details": street_details,
+                "total_m": round(g[end_id]),
+            }
+        except Exception as e:
+            raise Exception(f"Pathfind failed: {str(e)}")
+
+    @staticmethod
+    async def nearest_position(qlat: float, qlon: float, top_k: int = 3) -> dict:
+        """Find the single nearest position to (qlat, qlon) with a 2-step Group strategy:
+          1) pick the `top_k` nearest Groups by (lat_center, long_center)
+          2) load ONLY those groups' positions (via Streets.positions) — not the whole DB
+          3) find the nearest position inside EACH group
+          4) return the closest among the per-group winners
+        Falls back to a full DB scan when there are no groups / no group positions.
+        Returns: {found, position: {id,lat,lon,streets,street_names}|None, distance_m, group_id}
+        """
+        import math
+
+        def haversine_m(lat1, lon1, lat2, lon2):
+            R = 6_371_000
+            phi1, phi2 = math.radians(lat1), math.radians(lat2)
+            dphi = math.radians(lat2 - lat1)
+            dlam = math.radians(lon2 - lon1)
+            a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlam / 2) ** 2
+            return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+
+        best_pos, best_d, best_gid = None, float("inf"), None
+
+        # 1) Nearest `top_k` groups by their center
+        groups_res = supabase.table("Groups").select("id, lat_center, long_center, streets").execute()
+        groups = [g for g in (groups_res.data or [])
+                  if g.get("lat_center") is not None and g.get("long_center") is not None]
+        groups.sort(key=lambda g: haversine_m(qlat, qlon, float(g["lat_center"]), float(g["long_center"])))
+        nearest_groups = groups[:top_k]
+
+        # 2) Group streets → position ids (via Streets.positions); batch-load ONLY those positions
+        all_street_ids = sorted({int(s) for g in nearest_groups for s in (g.get("streets") or [])})
+        if all_street_ids:
+            sres = supabase.table("Streets").select("id, positions").in_("id", all_street_ids).execute()
+            street_positions = {s["id"]: [int(p) for p in (s.get("positions") or [])] for s in (sres.data or [])}
+            group_pos_ids: dict = {}
+            all_pos_ids: set = set()
+            for g in nearest_groups:
+                pids = set()
+                for sid in (g.get("streets") or []):
+                    pids.update(street_positions.get(int(sid), []))
+                group_pos_ids[g["id"]] = pids
+                all_pos_ids.update(pids)
+            if all_pos_ids:
+                pres = supabase.table("Positions").select("*").in_("id", list(all_pos_ids)).execute()
+                pos_by_id = {p["id"]: p for p in (pres.data or [])
+                             if p.get("lat") is not None and p.get("long") is not None}
+                # 3) nearest position inside each group  +  4) compare the winners
+                for g in nearest_groups:
+                    g_pos, g_d = None, float("inf")
+                    for pid in group_pos_ids.get(g["id"], ()):  # chỉ xét position của group này
+                        pos = pos_by_id.get(pid)
+                        if not pos:
+                            continue
+                        d = haversine_m(qlat, qlon, float(pos["lat"]), float(pos["long"]))
+                        if d < g_d:
+                            g_d, g_pos = d, pos
+                    if g_pos is not None and g_d < best_d:
+                        best_d, best_pos, best_gid = g_d, g_pos, g["id"]
+
+        # Fallback: không có group / group rỗng → quét toàn bộ DB (đúng tuyệt đối)
+        if best_pos is None:
+            res = supabase.table("Positions").select("id, lat, long, streets").execute()
+            for p in (res.data or []):
+                if p.get("lat") is None or p.get("long") is None:
+                    continue
+                d = haversine_m(qlat, qlon, float(p["lat"]), float(p["long"]))
+                if d < best_d:
+                    best_d, best_pos, best_gid = d, p, None
+
+        if best_pos is None:
+            return {"found": False, "position": None, "distance_m": None, "group_id": None}
+
+        # Resolve street names for the chosen position
+        street_ids = best_pos.get("streets") or []
+        smap = {}
+        if street_ids:
+            sr = supabase.table("Streets").select("id, name").in_("id", street_ids).execute()
+            smap = {s["id"]: s["name"] for s in (sr.data or [])}
+        position = {
+            "id": best_pos["id"],
+            "lat": best_pos["lat"],
+            "lon": best_pos["long"],
+            "streets": street_ids,
+            "street_names": [smap.get(sid, str(sid)) for sid in street_ids],
+        }
+        return {"found": True, "position": position, "distance_m": round(best_d), "group_id": best_gid}
+
     @staticmethod
     async def search_nearest(points: list) -> dict:
         """
@@ -585,6 +1038,23 @@ class MapsService:
             return ". ".join(instructions) + "."
 
         try:
+            # ── Single-point: reuse the extracted 2-step group-based nearest search ──
+            if len(points) < 2:
+                res = await MapsService.nearest_position(float(points[0]["lat"]), float(points[0]["lon"]))
+                if not res.get("found"):
+                    return {"data": [], "label": "Không có điểm nào trong CSDL", "tts": "", "warning": "", "message": ""}
+                pt, dist_m, gid = res["position"], res["distance_m"], res["group_id"]
+                sname = pt["street_names"][0] if pt["street_names"] else "vị trí không xác định"
+                dl = f"~{dist_m}m" if dist_m < 1000 else f"~{round(dist_m / 1000, 1)}km"
+                gtxt = f"  |  Group #{gid}" if gid is not None else ""
+                return {
+                    "data": [pt],
+                    "label": f"Điểm gần nhất: {sname} ({dl})",
+                    "tts": f"Vị trí gần nhất nằm trên {sname}, cách bạn khoảng {dist_m} mét.",
+                    "warning": "",
+                    "message": f"Khoảng cách: ~{dist_m}m{gtxt}",
+                }
+
             result = supabase.table("Positions").select("*").execute()
             all_positions = result.data or []
             if not all_positions:
@@ -620,20 +1090,7 @@ class MapsService:
                 return f"~{m}m" if m < 1000 else f"~{round(m / 1000, 1)}km"
 
             # ── Single-point mode: return nearest ────────────────────────────
-            if len(points) < 2:
-                qlat, qlon = float(points[0]["lat"]), float(points[0]["lon"])
-                nid, dist_m = find_nearest_id(qlat, qlon)
-                if nid is None:
-                    return {"data": [], "label": "Không tìm thấy", "tts": "", "warning": "", "message": ""}
-                pt    = make_result_point(pos_by_id[nid])
-                sname = pt["street_names"][0] if pt["street_names"] else "vị trí không xác định"
-                return {
-                    "data": [pt],
-                    "label": f"Điểm gần nhất: {sname} ({dist_label(round(dist_m))})",
-                    "tts": f"Vị trí gần nhất nằm trên {sname}, cách bạn khoảng {round(dist_m)} mét.",
-                    "warning": "",
-                    "message": f"Khoảng cách: ~{round(dist_m)}m",
-                }
+            # (single-point handled earlier via MapsService.nearest_position)
 
             # ── Two-point mode: shortest path via Dijkstra ───────────────────
             q1, q2 = points[0], points[1]
@@ -729,6 +1186,60 @@ class MapsService:
         except Exception as e:
             raise Exception(f"Search nearest failed: {str(e)}")
 
+    @staticmethod
+    async def search_uuid(query_id: str) -> dict:
+        """Search the utest table for the row whose id is most similar to query_id.
+        Uses Levenshtein distance (normalised) as similarity score.
+        Returns: {found, id, score, row} where score=0.0 is exact match.
+        """
+        def _lev(a: str, b: str) -> int:
+            if a == b:
+                return 0
+            if not a:
+                return len(b)
+            if not b:
+                return len(a)
+            prev = list(range(len(b) + 1))
+            for i, ca in enumerate(a, start=1):
+                curr = [i]
+                for j, cb in enumerate(b, start=1):
+                    curr.append(min(curr[j - 1] + 1, prev[j] + 1, prev[j - 1] + (0 if ca == cb else 1)))
+                prev = curr
+            return prev[-1]
+
+        try:
+            result = supabase.table("utest").select("*").execute()
+            rows = result.data or []
+            if not rows:
+                return {"found": False, "id": None, "score": None, "row": None}
+
+            q = str(query_id).strip()
+            best_row = None
+            best_score = float("inf")
+
+            for row in rows:
+                row_id = str(row.get("id") or "").strip()
+                if not row_id:
+                    continue
+                dist = _lev(q, row_id)
+                max_len = max(len(q), len(row_id), 1)
+                score = dist / max_len
+                if score < best_score:
+                    best_score = score
+                    best_row = row
+
+            if best_row is None:
+                return {"found": False, "id": None, "score": None, "row": None}
+
+            return {
+                "found": True,
+                "id": best_row.get("id"),
+                "score": round(best_score, 4),
+                "row": best_row,
+            }
+        except Exception as e:
+            raise Exception(f"search_uuid failed: {str(e)}")
+
     @staticmethod
     async def delete_position_cascade(position_id: int) -> bool:
         """Delete a position and remove its id from any Streets.positions arrays listed in position.streets"""