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src/apis/controllers/destination_controller.py

@@ -2,7 +2,24 @@ from typing import List, Dict, Any
 import aiohttp
 from fastapi import HTTPException
 from src.utils.logger import logger
-async def destination_suggestion_controller(question: str, top_k: int = 5) -> List[Dict[str, Any]]:
+import json
+from src.langgraph.langchain.prompt import (
+    routing_recommender_chain,
+    characteristic_extractor_chain,
+    RoutingRecommender,
+    CharacteristicExtractor,
+)
+from src.apis.controllers.location_controller import (
+    get_lat_long_location,
+    get_places,
+)
+from src.langgraph.config.constant import available_categories
+
+
+async def destination_suggestion_controller(
+    question: str, top_k: int = 5
+) -> List[Dict[str, Any]]:
+
     async with aiohttp.ClientSession() as session:
         # Get question tags
         try:
@@ -16,7 +33,7 @@ async def destination_suggestion_controller(question: str, top_k: int = 5) -> Li
                 else:
                     raise HTTPException(
                         status_code=response_tag.status,
-                        detail=f"Tag request failed with status {response_tag.status}"
+                        detail=f"Tag request failed with status {response_tag.status}",
                     )
 
             # Get destinations list
@@ -30,8 +47,32 @@ async def destination_suggestion_controller(question: str, top_k: int = 5) -> Li
                 else:
                     raise HTTPException(
                         status_code=response.status,
-                        detail=f"Destinations request failed with status {response.status}"
+                        detail=f"Destinations request failed with status {response.status}",
                     )
-                    
+
         except aiohttp.ClientError as e:
-            raise HTTPException(status_code=500, detail=f"Request failed: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Request failed: {str(e)}")
+
+
+async def destination_recommendation_func(query, top_k=5):
+    routing: RoutingRecommender = routing_recommender_chain.invoke({"query": query})
+    print("label", routing.label)
+    if routing.label == "characteristic":
+        output = await destination_suggestion_controller(query, top_k)
+        return output
+    characteristic_extract_response: CharacteristicExtractor = (
+        await characteristic_extractor_chain.ainvoke({"query": query})
+    )
+    print("characterist", characteristic_extract_response)
+    lat, lon = get_lat_long_location(characteristic_extract_response.main_place)
+    response = get_places(
+        lat,
+        lon,
+        5000,
+        available_categories.get(characteristic_extract_response.kind, None),
+        top_k,
+    )
+    response = json.loads(response.body)
+    output = [i.get("Accommodation_Name", None) for i in response]
+
+    return output

src/apis/controllers/location_controller.py

@@ -40,7 +40,6 @@ def get_nearby_places(lat, long, radius, kinds):
         "apikey": api_key,
     }
     response = requests.get(url, params=params, headers={"accept": "application/json"})
-    # print(response.json())
     if response.status_code == 200:
         logger.info("Places fetched successfully")
         return JSONResponse(
@@ -59,7 +58,6 @@ def get_places(lat, long, radius, categories, limit=20):
     response = requests.get(url)
     if response.status_code == 200:
         response = response.json().get("features", [])
-        # logger.info(f"RESPONSE:{response}")
         if response:
             response = format_geoapify_response(response, long, lat)
         return JSONResponse(content=response, status_code=200)
@@ -113,11 +111,5 @@ def get_geometry_nearby_places(lat, lon, radius, kinds):
     res = get_nearby_places(lat=lat, long=lon, radius=radius, kinds=kinds)
     data = json.loads(res.body)
     recommend_places = data.get("places", [])
-    recommend_places = [
-        {
-            "name": place["properties"]["name"],
-            "link_google_map": get_google_map_url(place["geometry"]["coordinates"]),
-        }
-        for place in recommend_places
-    ]
+    recommend_places = [place["properties"]["name"] for place in recommend_places]
     return recommend_places

src/apis/routes/travel_dest_route.py

@@ -8,9 +8,11 @@ from src.apis.middlewares.auth_middleware import get_current_user
 from src.utils.logger import logger
 from src.apis.controllers.destination_controller import (
     destination_suggestion_controller,
+    destination_recommendation_func,
 )
 from fastapi.responses import JSONResponse
 from src.utils.mongo import DestinationCRUD
+
 router = APIRouter(prefix="/dest", tags=["Destination"])
 
 user_dependency = Annotated[User, Depends(get_current_user)]
@@ -53,7 +55,7 @@ def get_tourist(page: int = Query(default=1, ge=1)):
 async def get_tourist_names():
     try:
         destination_data = await DestinationCRUD.find_all()
-        
+
         # Drop the fields created_at, updated_at, expire_at from each item
 
         serialized_data = []
@@ -74,4 +76,4 @@ async def get_tourist_names():
 
 @router.get("/suggest")
 async def destination_suggestion(question: str, top_k: int = Query(default=5, ge=1)):
-    return JSONResponse(await destination_suggestion_controller(question, top_k))
+    return JSONResponse(await destination_recommendation_func(question))

src/langgraph/config/constant.py

@@ -21,3 +21,17 @@ class MongoCfg:
     MAX_HISTORY_SIZE: int = 15
 class RedisCfg:
     REDIS_URL: str = os.getenv("REDIS_URL")
+
+available_categories = {
+    "food_and_beverage": "catering",
+    "accommodation": "accommodation",
+    "entertainment": "entertainment",
+    "leisure": "leisure",
+    "natural": "natural",
+    "national_park": "national_park",
+    "tourism": "tourism",
+    "camping": "camping",
+    "religion": "religion",
+    "sport": "sport",
+    "commercial": "commercial",
+}

src/langgraph/langchain/prompt.py

@@ -1,4 +1,4 @@
-from langchain.prompts import ChatPromptTemplate
+from langchain.prompts import ChatPromptTemplate, PromptTemplate
 from datetime import datetime
 from pydantic import BaseModel, Field
 from langchain_core.messages import HumanMessage
@@ -7,6 +7,8 @@ import pytz
 from src.langgraph.state import State
 from src.utils.logger import logger
 from langchain_core.prompts import PromptTemplate
+from .llm import llm, llm_flash
+from src.langgraph.config.constant import available_categories
 
 vietnam_timezone = pytz.timezone("Asia/Ho_Chi_Minh")
 vietnam_time = datetime.now(vietnam_timezone).strftime("%Y-%m-%d %H:%M:%S")
@@ -138,6 +140,53 @@ book_hotel_prompt = ChatPromptTemplate.from_messages(
     ]
 ).partial(current_time=vietnam_time)
 
+routing_recommender_prompt = PromptTemplate.from_template(
+    """You are a tour guide.
+### Instruction:
+You are given a query sentence
+Classify the query as 'characteristic' or 'geographic' based on its content.
+
+- 'Characteristic' if it asks about a place's features (e.g., camping, relaxing, scenic).
+- 'Geographic' if it involves location specifics (e.g., near a city, in a province, on a road).
+
+###
+Query sentence: {query}
+"""
+)
+characteristic_extractor_prompt = PromptTemplate.from_template(
+    """You are a tour guide.
+### Instruction:
+Read the sentence carefully
+Given a query about a destination, extract two things:
+1. **Kind**: Choose one from {available_categories}.
+2. **Main Place**: Identify the main place mentioned in the query. Include 'Quy Nhon' or 'Binh Dinh' term in the main place response
+
+Query sentence: {query}
+."""
+).partial(available_categories=list(available_categories.keys()))
+
+
+class RoutingRecommender(BaseModel):
+    label: str = Field(..., description="is 'characteristic' or 'geographic'")
+
+
+class CharacteristicExtractor(BaseModel):
+    kind: str = Field(
+        ...,
+        description=f"Choose one from {list(available_categories.keys())}",
+    )
+    main_place: str = Field(..., description="main place mentioned in the query")
+
+
+routing_recommender_chain = (
+    routing_recommender_prompt | llm_flash.with_structured_output(RoutingRecommender)
+)
+
+characteristic_extractor_chain = (
+    characteristic_extractor_prompt
+    | llm_flash.with_structured_output(CharacteristicExtractor)
+)
+
 
 class CompleteOrRoute(BaseModel):
     """Call this function to complete the current assistant's task and route the conversation back to the primary assistant."""

src/langgraph/models/model_validator.py

@@ -1,5 +1,6 @@
 from pydantic import BaseModel, Field, EmailStr
 
+
 # from typing import Optional
 # from datetime import datetime, timezone
 
@@ -65,4 +66,6 @@ class NearlyDestinationRecommendation(BaseModel):
     )
 
     class Config:
-        schema_extra = {"example": {"query": "Địa điểm gần Hà Nội" "places" "Hà Nội"}}
+        json_schema_extra = {
+            "example": {"query": "Địa điểm gần Hà Nội" "places" "Hà Nội"}
+        }

src/langgraph/multi_agent/chat/chat_flow.py

@@ -16,12 +16,12 @@ from src.langgraph.utils_function.function_graph import (
     get_history,
     save_history,
 )
-from src.langgraph.tools.destination_tools import destination_recommendation, nearly_destination_recommendation
+from src.langgraph.tools.destination_tools import destination_recommendation
 from src.utils.logger import logger
 
 primary_assistant_tools = [
     TavilySearchResults(max_results=2),
-    destination_recommendation,nearly_destination_recommendation
+    destination_recommendation,
 ]
 
 assistant_runnable = primary_assistant_prompt | llm.bind_tools(
@@ -58,7 +58,6 @@ class ChatBot:
         self.primary_assistant_tools = [
             TavilySearchResults(max_results=2),
             destination_recommendation,
-            nearly_destination_recommendation
         ]
         self.assistant_runnable = assistant_runnable
 

src/langgraph/tools/destination_tools.py

@@ -1,6 +1,7 @@
 from langchain_core.tools import tool
 from src.apis.controllers.destination_controller import (
     destination_suggestion_controller,
+    destination_recommendation_func,
 )
 from langchain_core.runnables.config import RunnableConfig
 from src.utils.logger import logger
@@ -11,7 +12,12 @@ from src.apis.controllers.location_controller import (
 )
 from src.langgraph.models.model_validator import NearlyDestinationRecommendation
 from src.utils.helper import get_google_map_url
-
+from src.langgraph.langchain.prompt import (
+    routing_recommender_chain,
+    characteristic_extractor_chain,
+    RoutingRecommender,
+    CharacteristicExtractor,
+)
 import json
 
 
@@ -19,21 +25,11 @@ import json
 async def destination_recommendation(query: str, config: RunnableConfig):
     """Call tool when user want to recommend a travel destination(tourist attractions, restaurants). Not require user typing anything.
     Args:
-    query (str): query related to wanting to go somewhere. Auto extracted from user's message. Using Vietnamese language for better results.
-    Output: A list of recommended destinations.
+    query (str): query related to wanting to go somewhere locations near a certain area/location or location's characteristics user want to go. Auto extracted from user's message.
+    Using Vietnamese language for better results
     """
     logger.info(f"Destination recommendation query: {query}")
-    output = await destination_suggestion_controller(query, 3)
+    output = await destination_recommendation_func(query)
+    logger.info(f"Destination recommendation output: {output}")
     return output
 
-
-@tool(args_schema=NearlyDestinationRecommendation)
-async def nearly_destination_recommendation(
-    query: str, places, kinds, config: RunnableConfig
-):
-    """Gọi tool này khi query đề cập đến việc tìm địa điểm gần một khu vực/ địa điểm nào đó"""
-    radius = 1000
-    lat, lon = get_lat_long_location(places)
-    recommend_places = get_geometry_nearby_places(lat, lon, radius, kinds)
-    logger.info(f"Nearly Destination recommendation: {places} >> {kinds} >> {recommend_places}")
-    return recommend_places

src/utils/helper.py

@@ -104,7 +104,7 @@ def format_geoapify_response(
         if include_latnlong:
             formatted_item["lat"] = place_lat
             formatted_item["lon"] = place_lon
-        formatted_item["Accommodation Name"] = feature["properties"]["address_line1"]
+        formatted_item["Accommodation_Name"] = feature["properties"]["address_line1"]
         formatted_item["Address"] = feature["properties"]["formatted"]
         formatted_item["distance_km"] = str(round(distance, 2)) + " km"