feat: refactor product data retrieval - Fix URL parsing in...

feat: refactor product data retrieval - Fix URL parsing in description_text_full - Update system_prompt to require product_ids in responses - Remove graph singleton for hot reload support - Improve product enrichment logic

backend/agent/graph.py

@@ -129,10 +129,10 @@ class CANIFAGraph:
 _instance: list[CANIFAGraph | None] = [None]
 
 def build_graph(config: AgentConfig | None = None, llm: BaseChatModel | None = None, tools: list | None = None) -> Any:
-    """Get compiled graph (singleton)."""
-    if _instance[0] is None:
-        _instance[0] = CANIFAGraph(config, llm, tools)
-    return _instance[0].build()
+    """Get compiled graph (always fresh to pick up prompt changes)."""
+    # Always create new instance to pick up prompt changes during hot reload
+    instance = CANIFAGraph(config, llm, tools)
+    return instance.build()
 
 
 def get_graph_manager(

backend/agent/helper.py

@@ -31,20 +31,31 @@ def extract_product_ids(messages: list) -> list[dict]:
                 # Tool result is JSON string
                 tool_result = json.loads(msg.content)
 
-                # Check if tool returned products
-                if tool_result.get("status") == "success" and "products" in tool_result:
-                    for product in tool_result["products"]:
-                        sku = product.get("internal_ref_code")
+                # Check if tool returned products (new format with "results" wrapper)
+                if tool_result.get("status") == "success":
+                    # Handle both direct "products" and nested "results" format
+                    product_list = []
+                    
+                    if "results" in tool_result:
+                        # New format: {"results": [{"products": [...]}]}
+                        for result_item in tool_result["results"]:
+                            product_list.extend(result_item.get("products", []))
+                    elif "products" in tool_result:
+                        # Legacy format: {"products": [...]}
+                        product_list = tool_result["products"]
+                    
+                    for product in product_list:
+                        sku = product.get("sku") or product.get("internal_ref_code")
                         if sku and sku not in seen_skus:
                             seen_skus.add(sku)
 
-                            # Extract full product info
+                            # Extract full product info (already parsed by tool)
                             product_obj = {
                                 "sku": sku,
-                                "name": product.get("magento_product_name", ""),
-                                "price": product.get("price_vnd", 0),
-                                "sale_price": product.get("sale_price_vnd"),  # null nếu không sale
-                                "url": product.get("magento_url_key", ""),
+                                "name": product.get("name", ""),
+                                "price": product.get("price", 0),
+                                "sale_price": product.get("sale_price"),
+                                "url": product.get("url", ""),
                                 "thumbnail_image_url": product.get("thumbnail_image_url", ""),
                             }
                             products.append(product_obj)
@@ -55,32 +66,53 @@ def extract_product_ids(messages: list) -> list[dict]:
     return products
 
 
-def parse_ai_response(ai_raw_content: str, all_product_ids: list) -> tuple[str, list]:
+def parse_ai_response(ai_raw_content: str, all_products: list) -> tuple[str, list]:
     """
-    Parse AI response từ LLM output.
+    Parse AI response từ LLM output và map SKUs với product data.
+    
+    Flow:
+    - LLM trả về: {"ai_response": "...", "product_ids": ["SKU1", "SKU2"]}
+    - all_products: List products enriched từ tool messages
+    - Map SKUs → enriched products
     
     Args:
         ai_raw_content: Raw content từ AI response
-        all_product_ids: Product IDs extracted từ tool messages
+        all_products: Products extracted từ tool messages (đã có đầy đủ info)
         
     Returns:
-        tuple: (ai_text_response, final_product_ids)
+        tuple: (ai_text_response, final_products)
     """
     ai_text_response = ai_raw_content
-    final_product_ids = all_product_ids
+    final_products = all_products  # Default: trả về tất cả products từ tool
 
     try:
         # Try to parse if it's a JSON string from LLM
         ai_json = json.loads(ai_raw_content)
         ai_text_response = ai_json.get("ai_response", ai_raw_content)
-        explicit_ids = ai_json.get("product_ids", [])
-        if explicit_ids and isinstance(explicit_ids, list):
-            # Replace with explicit IDs from LLM
-            final_product_ids = explicit_ids
+        explicit_skus = ai_json.get("product_ids", [])
+        
+        if explicit_skus and isinstance(explicit_skus, list):
+            # LLM trả về list SKUs → Map với products đã có
+            # Build lookup dict từ all_products
+            product_lookup = {p["sku"]: p for p in all_products if p.get("sku")}
+            
+            # Map SKUs → enriched products
+            mapped_products = []
+            for sku in explicit_skus:
+                if isinstance(sku, str) and sku in product_lookup:
+                    mapped_products.append(product_lookup[sku])
+                elif isinstance(sku, dict):
+                    # LLM có thể trả về dict (legacy) → giữ nguyên
+                    mapped_products.append(sku)
+            
+            if mapped_products:
+                final_products = mapped_products
+            # Nếu không map được → giữ all_products
+            
     except (json.JSONDecodeError, TypeError):
         pass
 
-    return ai_text_response, final_product_ids
+    return ai_text_response, final_products
 
 
 def prepare_execution_context(query: str, user_id: str, history: list, images: list | None):

backend/agent/tools/data_retrieval_tool.py

@@ -200,21 +200,60 @@ async def _execute_single_search(db, item: SearchItem, query_vector: list[float]
 
 
 def _format_product_results(products: list[dict]) -> list[dict]:
-    """Lọc và format kết quả trả về cho Agent."""
+    """Lọc và format kết quả trả về cho Agent - Parse description_text_full thành structured fields."""
     max_items = 15
     formatted: list[dict] = []
 
     for p in products[:max_items]:
+        desc_full = p.get("description_text_full", "")
+        
+        # Parse các field từ description_text_full
+        parsed = _parse_description_text(desc_full)
+        
         formatted.append(
             {
-                "internal_ref_code": p.get("internal_ref_code"),
-                # Chuỗi text dài, đã bao gồm: product_name, master_color, image, web_url, material, style, ...
-                "description_text": p.get("description_text_full"),
+                "sku": p.get("internal_ref_code"),
+                "name": parsed.get("product_name", ""),
+                "price": p.get("original_price"),
                 "sale_price": p.get("sale_price"),
-                "original_price": p.get("original_price"),
+                "url": parsed.get("product_web_url", ""),
+                "thumbnail_image_url": parsed.get("product_image_url_thumbnail", ""),
                 "discount_amount": p.get("discount_amount"),
                 "max_score": p.get("max_score"),
             }
         )
 
     return formatted
+
+
+def _parse_description_text(desc: str) -> dict:
+    """
+    Parse description_text_full thành dict các field.
+    Format: "product_name: X. master_color: Y. product_web_url: https://canifa.com/... ..."
+    """
+    import re
+    result = {}
+    if not desc:
+        return result
+    
+    # Extract product_name: từ đầu đến ". master_color:" hoặc ". product_image_url:"
+    name_match = re.search(r"product_name:\s*(.+?)\.(?:\s+master_color:|$)", desc)
+    if name_match:
+        result["product_name"] = name_match.group(1).strip()
+    
+    # Extract product_image_url_thumbnail: từ field name đến ". product_web_url:"
+    thumb_match = re.search(r"product_image_url_thumbnail:\s*(https?://[^\s]+?)\.(?:\s+product_web_url:|$)", desc)
+    if thumb_match:
+        result["product_image_url_thumbnail"] = thumb_match.group(1).strip()
+    
+    # Extract product_web_url: từ field name đến ". description_text:"
+    url_match = re.search(r"product_web_url:\s*(https?://[^\s]+?)\.(?:\s+description_text:|$)", desc)
+    if url_match:
+        result["product_web_url"] = url_match.group(1).strip()
+    
+    # Extract master_color: từ field name đến ". product_image_url:"
+    color_match = re.search(r"master_color:\s*(.+?)\.(?:\s+product_image_url:|$)", desc)
+    if color_match:
+        result["master_color"] = color_match.group(1).strip()
+    
+    return result

backend/agent/tools/product_search_helpers.py

@@ -176,61 +176,3 @@ async def build_starrocks_query(params, query_vector: list[float] | None = None)
     """
 
     return sql
-
-
-# ============================================================
-# TEMPORARILY COMMENTED OUT - save_query_to_log
-# ============================================================
-# async def save_query_to_log(sql: str):
-#     """Lưu query full vào file hyde_pure_query.txt."""
-#     import os
-#     log_path = r"D:\cnf\chatbot_canifa\backend\logs\hyde_pure_query.txt"
-#     try:
-#         log_dir = os.path.dirname(log_path)
-#         if not os.path.exists(log_dir):
-#             os.makedirs(log_dir)
-#         with open(log_path, "w", encoding="utf-8") as f:
-#             f.write(sql)
-#         print(f"💾 Full Query saved to: {log_path}")
-#     except Exception as e:
-#         print(f"Save query log failed: {e}")
-
-
-# ============================================================
-# TEMPORARILY COMMENTED OUT - save_preview_to_log
-# ============================================================
-# async def save_preview_to_log(search_query: str, products: list[dict]):
-#     """Lưu kết quả DB trả về vào db_preview.txt (Format đẹp cho AI)."""
-#     import os
-#     preview_path = r"D:\cnf\chatbot_canifa\backend\logs\db_preview.txt"
-#     try:
-#         log_dir = os.path.dirname(preview_path)
-#         if not os.path.exists(log_dir):
-#             os.makedirs(log_dir)
-#
-#         with open(preview_path, "a", encoding="utf-8") as f:
-#             f.write(f"\n{'='*60}\n")
-#             f.write(f"⏰ TIME: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
-#             f.write(f"🔍 SEARCH: {search_query}\n")
-#             f.write(f"📊 RESULTS COUNT: {len(products)}\n")
-#             f.write(f"{'-'*60}\n")
-#
-#             if not products:
-#                 f.write("❌ NO PRODUCTS FOUND\n")
-#             else:
-#                 for idx, p in enumerate(products[:5], 1):
-#                     code = p.get("internal_ref_code", "N/A")
-#                     sale = p.get("sale_price", "N/A")
-#                     orig = p.get("original_price", "N/A")
-#                     disc = p.get("discount_amount", "0")
-#                     score = p.get("max_score", p.get("similarity_score", "N/A"))
-#                     desc = p.get("description_text_full", "No Description")
-#
-#                     f.write(f"{idx}. [{code}] Score: {score}\n")
-#                     f.write(f"   💰 Price: {sale} (Orig: {orig}, Disc: {disc}%)\n")
-#                     f.write(f"   📝 Desc: {desc}\n")
-#
-#             f.write(f"{'='*60}\n")
-#         print(f"💾 DB Preview (Results) saved to: {preview_path}")
-#     except Exception as e:
-#         print(f"Save preview log failed: {e}")

backend/common/cache.py

@@ -15,12 +15,9 @@ from config import (
 
 logger = logging.getLogger(__name__)
 
-# ====================== CACHE CONFIGURATION ======================
-# Layer 1: Response Cache (Short TTL to keep stock/price safe)
 DEFAULT_RESPONSE_TTL = 300  # 5 minutes
 RESPONSE_KEY_PREFIX = "resp_cache:"
 
-# Layer 2: Embedding Cache (Long TTL since vectors are static)
 EMBEDDING_CACHE_TTL = 86400  # 24 hours
 EMBEDDING_KEY_PREFIX = "emb_cache:"