update flow

app/constants.py

@@ -1,3 +1,11 @@
 VEHICLE_KEYWORDS = ["xe máy", "ô tô", "xe đạp", "xe hơi"]
 SHEET_RANGE = 'chat!A2:F'
-EMBEDDING_DIM = 1536 
+EMBEDDING_DIM = 1536
+
+# Mapping từ từ khóa sang tên cột trong bảng tblPhuongtien
+VEHICLE_KEYWORD_TO_COLUMN = {
+    "xe máy": "xemay",
+    "ô tô": "oto",
+    "xe đạp": "xedap",
+    "xe hơi": "xehoi"
+} 

app/embedding.py

@@ -47,10 +47,16 @@ class EmbeddingClient:
                 json={"text": text}
             )
             response.raise_for_status()
-            return response.json()["embedding"]
-
+            data = await response.json()
+            logger.info(f"[DEBUG] Embedding API response: {data}")
+            return data["embedding"]
         except Exception as e:
             logger.error(f"Error creating embedding: {e}")
+            if 'response' in locals():
+                try:
+                    logger.error(f"Embedding API status: {response.status_code}, content: {await response.aread()}")
+                except Exception as ex:
+                    logger.error(f"Error reading response content: {ex}")
             raise
 
     @timing_decorator_sync

app/main.py

@@ -13,7 +13,7 @@ from .sheets import SheetsClient
 from .supabase_db import SupabaseClient
 from .embedding import EmbeddingClient
 from .utils import setup_logging, extract_command, extract_keywords, timing_decorator_async, timing_decorator_sync, ensure_log_dir, validate_config
-from .constants import VEHICLE_KEYWORDS, SHEET_RANGE
+from .constants import VEHICLE_KEYWORDS, SHEET_RANGE, VEHICLE_KEYWORD_TO_COLUMN
 from .health import router as health_router
 
 app = FastAPI(title="WeBot Facebook Messenger API")
@@ -95,6 +95,8 @@ async def webhook(request: Request):
     Input: request (Request) - request chứa payload JSON từ Facebook.
     Output: JSON status.
     """
+
+    logger.info(f"[DEBUG] Nhận message từ Facebook Messenger webhook...")
     body_bytes = await request.body()
     
     # Verify request is from Facebook
@@ -127,11 +129,12 @@ async def process_message(message_data: Dict[str, Any]):
 
     # Nếu không có message_text và attachments, không xử lý
     if not message_text and not attachments:
+        logger.info(f"[DEBUG] Không có message_text và attachments, không xử lý...")
         return
 
     # Get page access token (sync)
     page_token = supabase_client.get_page_token(page_id)
-    logger.info(f"[DEBUG] page_token: {page_token}")
+    logger.info(f"[DEBUG] page_token: {page_token[:10]} ... {page_token[-10:]}")
 
     # Nếu không có page_token, không xử lý
     if not page_token:
@@ -170,7 +173,7 @@ async def process_message(message_data: Dict[str, Any]):
                 # Có thông tin phương tiện
                 embedding = await embedding_client.create_embedding(message_text)
                 logger.info(f"[DEBUG] embedding: {embedding[:5]} ... (total {len(embedding)})")
-                matches = supabase_client.match_documents(embedding, vehicle_filters=keywords)
+                matches = supabase_client.match_documents(embedding, vehicle_keywords=keywords)
                 logger.info(f"[DEBUG] matches: {matches}")
                 if matches:
                     response = format_search_results(matches)
@@ -200,7 +203,7 @@ async def process_message(message_data: Dict[str, Any]):
             # Có thông tin phương tiện
             embedding = await embedding_client.create_embedding(message_text)
             logger.info(f"[DEBUG] embedding: {embedding[:5]} ... (total {len(embedding)})")
-            matches = supabase_client.match_documents(embedding, vehicle_filters=keywords)
+            matches = supabase_client.match_documents(embedding, vehicle_keywords=keywords)
             logger.info(f"[DEBUG] matches: {matches}")
             if matches:
                 response = format_search_results(matches)

app/sheets.py

@@ -90,6 +90,8 @@ class SheetsClient:
             history = []
 
             for row in values:
+                # Bổ sung cột rỗng cho đủ 9 cột
+                row = row + [""] * (9 - len(row))
                 if row[4] == user_id and row[5] == page_id and row[8].lower() == 'false':
                     history.append({
                         'conversation_id': row[0],

app/supabase_db.py

@@ -3,6 +3,7 @@ from supabase.client import create_client, Client
 from loguru import logger
 
 from .utils import timing_decorator_sync
+from .constants import VEHICLE_KEYWORD_TO_COLUMN
 
 class SupabaseClient:
     def __init__(self, url: str, key: str):
@@ -30,10 +31,10 @@ class SupabaseClient:
             return None
 
     @timing_decorator_sync
-    def match_documents(self, embedding: List[float], match_count: int = 5, vehicle_filters: Optional[List[str]] = None):
+    def match_documents(self, embedding: List[float], match_count: int = 5, vehicle_keywords: Optional[List[str]] = None):
         """
         Truy vấn vector similarity search qua RPC match_documents.
-        Input: embedding (list[float]), match_count (int), vehicle_filters (list[str] hoặc None)
+        Input: embedding (list[float]), match_count (int), vehicle_keywords (list[str] hoặc None)
         Output: list[dict] kết quả truy vấn.
         """
         try:
@@ -42,8 +43,10 @@ class SupabaseClient:
                 'match_threshold': 0.7,
                 'match_count': match_count
             }
-            if vehicle_filters is not None:
-                payload['vehicle_filters'] = vehicle_filters
+            if vehicle_keywords:
+                vehicle_columns = [VEHICLE_KEYWORD_TO_COLUMN[k] for k in vehicle_keywords if k in VEHICLE_KEYWORD_TO_COLUMN]
+                if vehicle_columns:
+                    payload['vehicle_filters'] = vehicle_columns
             response = self.client.rpc(
                 'match_documents',
                 payload