fix api response

app/agent/react_agent.py

@@ -9,6 +9,7 @@ Implements the ReAct (Reasoning + Acting) pattern:
 
 import time
 import json
+import re
 from typing import Any
 
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -157,15 +158,17 @@ class ReActAgent:
         
         if state.error:
             final_response = f"Xin lỗi, đã xảy ra lỗi: {state.error}"
+            selected_place_ids = []
         else:
-            final_response = await self._synthesize(state, history)
+            final_response, selected_place_ids = await self._synthesize(state, history)
         
         state.final_answer = final_response
+        state.selected_place_ids = selected_place_ids  # Store for later enrichment
         
         agent_logger.api_response(
             "/chat (ReAct)",
             200,
-            {"steps": len(state.steps), "tools": list(state.context.keys())},
+            {"steps": len(state.steps), "tools": list(state.context.keys()), "places": len(selected_place_ids)},
             state.total_duration_ms,
         )
         
@@ -291,15 +294,27 @@ class ReActAgent:
         else:
             return {"error": f"Unknown tool: {action}"}
     
-    async def _synthesize(self, state: AgentState, history: str | None = None) -> str:
-        """Synthesize final response from all collected information."""
+    async def _synthesize(self, state: AgentState, history: str | None = None) -> tuple[str, list[str]]:
+        """
+        Synthesize final response from all collected information.
+        
+        Returns:
+            Tuple of (response_text, selected_place_ids)
+        """
         # Build context from all steps
         context_parts = []
+        all_place_ids = []  # Collect all available place_ids
+        
         for step in state.steps:
             if step.observation and step.action != "finish":
                 context_parts.append(
                     f"Kết quả từ {step.action}:\n{json.dumps(step.observation, ensure_ascii=False, indent=2)}"
                 )
+                # Collect place_ids from observations
+                if isinstance(step.observation, list):
+                    for item in step.observation:
+                        if isinstance(item, dict) and 'place_id' in item:
+                            all_place_ids.append(item['place_id'])
         
         context = "\n\n".join(context_parts) if context_parts else "Không có kết quả."
         
@@ -324,15 +339,47 @@ Và kết quả thu thập được:
 Hãy trả lời câu hỏi của user một cách tự nhiên và hữu ích:
 "{state.query}"
 
-Trả lời tiếng Việt, thân thiện. Giới thiệu top 2-3 địa điểm phù hợp nhất với thông tin cụ thể."""
+**QUAN TRỌNG:** Trả lời theo format JSON:
+```json
+{{
+  "response": "Câu trả lời tiếng Việt, thân thiện. Giới thiệu top 2-3 địa điểm phù hợp nhất.",
+  "selected_place_ids": ["place_id_1", "place_id_2", "place_id_3"]
+}}
+```
+
+Chỉ chọn những place_id xuất hiện trong kết quả tìm kiếm ở trên. Nếu không có địa điểm, để mảng rỗng."""
 
         response = await self.llm_client.generate(
             prompt=prompt,
             temperature=0.7,
-            system_instruction="Bạn là trợ lý du lịch thông minh cho Đà Nẵng. Trả lời ngắn gọn, hữu ích.",
+            system_instruction="Bạn là trợ lý du lịch thông minh cho Đà Nẵng. Trả lời format JSON.",
         )
         
-        return response
+        # Parse JSON response
+        try:
+            # Extract JSON from response
+            json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', response, re.DOTALL)
+            if json_match:
+                response = json_match.group(1)
+            
+            json_start = response.find('{')
+            json_end = response.rfind('}')
+            if json_start != -1 and json_end != -1:
+                response = response[json_start:json_end + 1]
+            
+            data = json.loads(response)
+            text_response = data.get("response", response)
+            selected_ids = data.get("selected_place_ids", [])
+            
+            # Validate selected_ids are in available places
+            valid_ids = [pid for pid in selected_ids if pid in all_place_ids]
+            
+            return text_response, valid_ids
+            
+        except (json.JSONDecodeError, KeyError):
+            # Fallback: return raw response with no places
+            agent_logger.error("Failed to parse synthesis JSON", None)
+            return response, []
     
     def to_workflow(self, state: AgentState) -> AgentWorkflow:
         """Convert AgentState to AgentWorkflow for response."""

app/agent/state.py

@@ -46,6 +46,7 @@ class AgentState:
     max_steps: int = 5
     is_complete: bool = False
     final_answer: str = ""
+    selected_place_ids: list[str] = field(default_factory=list)  # LLM-selected places
     total_duration_ms: float = 0
     error: str | None = None
     

app/api/router.py

@@ -86,8 +86,21 @@ class WorkflowResponse(BaseModel):
     total_duration_ms: float = Field(..., description="Total processing time")
 
 
+class PlaceItem(BaseModel):
+    """Place item for FE rendering."""
+    place_id: str
+    name: str
+    category: str | None = None
+    lat: float | None = None
+    lng: float | None = None
+    rating: float | None = None
+    distance_km: float | None = None
+    address: str | None = None
+    image_url: str | None = None
+
+
 class ChatResponse(BaseModel):
-    """Chat response model with workflow trace."""
+    """Chat response model."""
 
     response: str = Field(..., description="Agent's response")
     status: str = Field(default="success", description="Response status")
@@ -95,7 +108,7 @@ class ChatResponse(BaseModel):
     model: str = Field(..., description="Model used")
     user_id: str = Field(..., description="User ID")
     session_id: str = Field(..., description="Session ID used")
-    workflow: WorkflowResponse | None = Field(None, description="Workflow trace for debugging")
+    places: list[PlaceItem] = Field(default_factory=list, description="LLM-selected places for FE rendering")
     tools_used: list[str] = Field(default_factory=list, description="MCP tools used")
     duration_ms: float = Field(default=0, description="Total processing time in ms")
 
@@ -225,6 +238,53 @@ async def find_nearby(request: NearbyRequest) -> NearbyResponse:
     )
 
 
+async def enrich_places_from_ids(place_ids: list[str], db: AsyncSession) -> list[PlaceItem]:
+    """
+    Enrich LLM-selected place_ids with full details from DB.
+    
+    Args:
+        place_ids: List of place_ids selected by LLM in synthesis
+        db: Database session
+        
+    Returns:
+        List of PlaceItem with full details
+    """
+    if not place_ids:
+        return []
+    
+    # Fetch full details from DB
+    from sqlalchemy import text
+    result = await db.execute(
+        text("""
+            SELECT place_id, name, category, address, rating,
+                   ST_X(coordinates::geometry) as lng, 
+                   ST_Y(coordinates::geometry) as lat
+            FROM places_metadata
+            WHERE place_id = ANY(:place_ids)
+        """),
+        {"place_ids": place_ids}
+    )
+    rows = result.fetchall()
+    
+    # Build PlaceItem list preserving LLM order
+    places_dict = {row.place_id: row for row in rows}
+    places = []
+    for pid in place_ids:
+        if pid in places_dict:
+            row = places_dict[pid]
+            places.append(PlaceItem(
+                place_id=row.place_id,
+                name=row.name,
+                category=row.category,
+                lat=row.lat,
+                lng=row.lng,
+                rating=float(row.rating) if row.rating else None,
+                address=row.address,
+            ))
+    
+    return places
+
+
 @router.post(
     "/chat",
     response_model=ChatResponse,
@@ -310,13 +370,8 @@ async def chat(
             session_id=session_id,
         )
         
-        workflow_response = WorkflowResponse(
-            query=workflow_data["query"],
-            intent_detected=workflow_data["intent_detected"],
-            tools_used=workflow_data["tools_used"],
-            steps=[WorkflowStepResponse(**s) for s in workflow_data["steps"]],
-            total_duration_ms=workflow_data["total_duration_ms"],
-        )
+        # Enrich LLM-selected place_ids with DB data
+        places = await enrich_places_from_ids(agent_state.selected_place_ids, db)
         
         return ChatResponse(
             response=response_text,
@@ -325,8 +380,8 @@ async def chat(
             model=model,
             user_id=request.user_id,
             session_id=session_id,
-            workflow=workflow_response,
-            tools_used=workflow.tools_used,
+            places=places,
+            tools_used=list(agent_state.context.keys()),
             duration_ms=agent_state.total_duration_ms,
         )
     
@@ -350,15 +405,21 @@ async def chat(
             session_id=session_id,
         )
 
-        # Build workflow response
-        workflow_data = result.workflow.to_dict()
-        workflow_response = WorkflowResponse(
-            query=workflow_data["query"],
-            intent_detected=workflow_data["intent_detected"],
-            tools_used=workflow_data["tools_used"],
-            steps=[WorkflowStepResponse(**s) for s in workflow_data["steps"]],
-            total_duration_ms=workflow_data["total_duration_ms"],
-        )
+        # Extract places from tool results if available
+        places = []
+        if hasattr(result, 'selected_place_ids') and result.selected_place_ids:
+            # If agent provides selected_place_ids, enrich from DB
+            places = await enrich_places_from_ids(result.selected_place_ids, db)
+        elif hasattr(result, 'tool_results') and result.tool_results:
+            # Fallback: extract all place_ids from tool results
+            place_ids = []
+            for tool_result in result.tool_results:
+                if isinstance(tool_result, list):
+                    for item in tool_result:
+                        if isinstance(item, dict) and 'place_id' in item:
+                            place_ids.append(item['place_id'])
+            if place_ids:
+                places = await enrich_places_from_ids(place_ids[:5], db)  # Limit to top 5
 
         return ChatResponse(
             response=result.response,
@@ -367,7 +428,7 @@ async def chat(
             model=model,
             user_id=request.user_id,
             session_id=session_id,
-            workflow=workflow_response,
+            places=places,
             tools_used=result.tools_used,
             duration_ms=result.total_duration_ms,
         )

docs/API_REFERENCE.md

@@ -51,31 +51,39 @@ Main endpoint for interacting with the AI assistant.
 **Response:**
 ```json
 {
-  "response": "Dựa trên yêu cầu của bạn, tôi tìm thấy...",
+  "response": "Mình gợi ý 3 quán cafe rất đẹp gần Mỹ Khê...",
   "status": "success",
   "provider": "MegaLLM",
   "model": "deepseek-ai/deepseek-v3.1-terminus",
   "user_id": "user_123",
   "session_id": "default",
-  "workflow": {
-    "query": "Quán cafe view đẹp gần Mỹ Khê",
-    "intent_detected": "location_search",
-    "tools_used": ["find_nearby_places"],
-    "steps": [
-      {
-        "step": "Execute find_nearby_places",
-        "tool": "find_nearby_places",
-        "purpose": "Tìm địa điểm gần vị trí được nhắc đến",
-        "results": 5
-      }
-    ],
-    "total_duration_ms": 5748.23
-  },
+  "places": [
+    {
+      "place_id": "cafe_001",
+      "name": "Cabanon Palace",
+      "category": "restaurant",
+      "lat": 16.06,
+      "lng": 108.24,
+      "rating": 4.8,
+      "address": "123 Võ Nguyên Giáp"
+    },
+    {
+      "place_id": "cafe_002",
+      "name": "Be Man Restaurant",
+      "category": "restaurant",
+      "lat": 16.07,
+      "lng": 108.25,
+      "rating": 4.5,
+      "address": "456 Phạm Văn Đồng"
+    }
+  ],
   "tools_used": ["find_nearby_places"],
   "duration_ms": 5748.23
 }
 ```
 
+> **Note:** `places` array contains LLM-selected places with full details. FE can render these as cards separately from text response.
+
 ---
 
 ### POST `/chat/clear`

tests/react_comparison_report.md

@@ -1,6 +1,6 @@
 # LocalMate Agent Test Report
 
-**Generated:** 2025-12-18 01:06:05
+**Generated:** 2025-12-18 01:17:38
 
 ## Summary
 
@@ -8,7 +8,7 @@
 |--------|-------------|------------|
 | Total Tests | 1 | 1 |
 | Success | 1 | 1 |
-| Avg Duration | 31219ms | 10765ms |
+| Avg Duration | 7584ms | 23328ms |
 
 ---
 
@@ -21,7 +21,7 @@
 #### Single Mode
 
 - **Status:** ✅ Success
-- **Duration:** 31219ms
+- **Duration:** 7584ms
 - **Tools Used:** find_nearby_places
 
 **Workflow:**
@@ -35,29 +35,35 @@
     Tool: `None` | Results: 0
 
 **Response Preview:**
-> Chào bạn! Dựa trên vị trí gần bãi biển Mỹ Khê, mình tìm được một số nhà hàng rất hay cho bạn đây:
+> Chào bạn! Mình đã tìm được một số nhà hàng ngon và gần bãi biển Mỹ Khê cho bạn đây. Dựa trên khoảng cách và đánh giá, đây là những gợi ý nổi bật nhất:
 
-**Top nhà hàng gần biển Mỹ Khê:**
+🍽️ **Top 3 nhà hàng gần bãi biển Mỹ Khê:**
 
-1.  **Cabanon Palace** (Nhà hàng Pháp)
-    *   *Khoảng cách:* Chỉ ...
+1. *...
 
 #### ReAct Mode
 
 - **Status:** ✅ Success
-- **Duration:** 10765ms
+- **Duration:** 23328ms
 - **Tools Used:** get_location_coordinates, find_nearby_places
-- **Steps:** 2
+- **Steps:** 3
 - **Intent Detected:** react_multi_step
 
 **Workflow Steps:**
   - Step 1: Để tìm nhà hàng gần bãi biển Mỹ Khê, trước tiên cầ...
     Tool: `get_location_coordinates` | Results: 0
-  - Step 2: Đã có tọa độ của bãi biển Mỹ Khê. Bây giờ cần tìm ...
+  - Step 2: Đã có tọa độ của bãi biển Mỹ Khê, bây giờ cần tìm ...
     Tool: `find_nearby_places` | Results: 5
+  - Step 3: Tôi đã có tọa độ của bãi biển Mỹ Khê và danh sách ...
+    Tool: `None` | Results: 0
 
 **Response Preview:**
-> Xin lỗi, đã xảy ra lỗi: 'str' object has no attribute 'get'...
+> Dạ chào bạn! Mình gợi ý một số nhà hàng ngon và gần bãi biển Mỹ Khê nhé:
+
+🍽️ **Cabanon Palace**  
+- *Chuyên:* Ẩm thực Pháp sang trọng  
+- *Khoảng cách:* ~0.94km từ biển  
+- *Đánh giá:* 4.8/5 – lý tưởn...
 
 ---
 
@@ -67,7 +73,7 @@
 
 | Test | Single Mode Tools | ReAct Mode Tools | ReAct Steps |
 |------|-------------------|------------------|-------------|
-| 2 | find_nearby_places | get_location_coordinates, find_nearby_places | 2 |
+| 2 | find_nearby_places | get_location_coordinates, find_nearby_places | 3 |
 
 
 ### Key Observations