UPDATE: New retrieve algorithm. Solve multiple sungalsses issue. Only find item with score is positive.

app/server/ChatbotAgent/agent_server.py

@@ -336,16 +336,16 @@ if __name__ == "__main__":
             request_data = json.load(f)
 
         tasks_with_metadata = []
-        for test_content in request_data[0:1]:
+        for test_content in request_data[14:20]:
             occasions = test_content['occasions']
             request_summary = test_content['request_summary']
-            for stylist_name in ["vera", "edi"]:
+            for stylist_name in ["edi"]:
                 stylist_agent_kwages['outfit_id'] = test_content['test_case_id'] + "_" + "_".join(occasions) + f"_{stylist_name}"
                 stylist_agent_kwages['stylist_name'] = stylist_name
                 stylist_agent_kwages['gender'] = "female"
                 agent = AsyncStylistAgent(**stylist_agent_kwages)
-                coro = agent.run_iterative_styling(
+                # coro = agent.run_iterative_styling(
-                    # coro = agent.run_quick_batch_styling(
+                coro = agent.run_quick_batch_styling(
                     request_summary=request_summary,
                     occasions=occasions,
                     start_outfit=[],

app/server/ChatbotAgent/core/stylist_agent_server.py

@@ -178,6 +178,7 @@ class AsyncStylistAgent:
             results = self.local_db.get_matched_item(
                 query_embedding,
                 category,
+                subcategory,
                 occasions=occasions,
                 batch_sources=batch_sources,
                 gender=gender,
@@ -388,6 +389,7 @@ class AsyncStylistAgent:
         gemini_data = self._parse_gemini_response(gemini_response_text)
         recommended_items = gemini_data.get('recommended_items', [])
         reason = gemini_data.get('reason', '')
+        failed_found_item_count = 0
 
         if not recommended_items or not isinstance(recommended_items, List):
             print("No recommended item from Gemini, terminating process.")
@@ -419,10 +421,21 @@ class AsyncStylistAgent:
 
                 new_item = self._get_next_item(description, category, subcategory, occasions, batch_sources, self.gender)
                 if not new_item or new_item['item_id'] in [x['item_id'] for x in self.outfit_items]:
+                    failed_found_item_count += 1
                     continue
                 else:
                     self.outfit_items.append(new_item)
                     print(f"Item {idx + 1}: ({subcategory}) {rec_item}, found item: {new_item}")
+            
+            # 如果没有找到的item过于多，需要重试
+            if failed_found_item_count / len(recommended_items) > 0.5:
+                self.post_operation(
+                    status="failed",
+                    message=f"There are {failed_found_item_count} items (total {len(recommended_items)}) are not found in the database",
+                    callback_url=url,
+                    img_path=merged_image_path
+                )
+                print(f"There are {failed_found_item_count} items (total {len(recommended_items)}) are not found in the database")
         return reason
 
     async def run_iterative_styling(self, request_summary, occasions, start_outfit: Optional[List] = None, batch_sources: List = [], user_id="test", callback_url=""):

app/server/ChatbotAgent/core/vector_database.py

@@ -46,7 +46,7 @@ class VectorDatabase():
 
         return features.cpu().numpy().flatten().tolist()
         
-    def get_matched_item(self, embedding: List[float], category: str, occasions: List[str] = [], batch_sources: List[str] = [], gender: str = 'female', n_results: int = 1) -> List[Dict[str, Any]]:
+    def get_matched_item(self, embedding: List[float], category: str, subcategory: str, occasions: List[str] = [], batch_sources: List[str] = [], gender: str = 'female', n_results: int = 1) -> List[Dict[str, Any]]:
         if category not in CATEGORY_LIST:
             raise ValueError(f"Recommended {category} is not valid.")
         
@@ -86,10 +86,14 @@ class VectorDatabase():
             return []
         
         metadatas = results['metadatas'][0]  # List[Dict[str, Any]]
-        final_scores = []
+        candidate_pool = []
+        all_scores = []
         for idx, metadata in enumerate(metadatas):
             dist_img = results['distances'][0][idx]
             score_vec = 1 - dist_img  # cosine similarity range: [-1, 1]
+            score_subcategory = 0.0
+            if subcategory == metadata['subcategory']:
+                score_subcategory = 1
 
             score_occ = 0.0
             if occasions:
@@ -100,26 +104,42 @@ class VectorDatabase():
                     count += 1
                     status_val = metadata.get(occ, -1)
                     if status_val == 1:
-                        score_occ += 1.0
+                        score_occ += 5.0
                     elif status_val == 0:
                         score_occ += 0.0
                     else:
-                        score_occ -= 100.0
+                        score_occ -= 5.0
 
                 score_occ = score_occ / count if count else 0.0
 
-            final_score = 0.6 * score_vec + 0.4 * score_occ
+            final_score = 0.5 * score_vec + 0.1 * score_occ + 0.5 * score_subcategory
-            final_scores.append(final_score)
+            all_scores.append(final_score)
+            if final_score > 0:
+                candidate_pool.append({
+                    "score": final_score,
+                    "metadata": metadata
+                })
 
-        scores_arr = np.array(final_scores)
+        if not candidate_pool:
-        temperature = 0.5
+            print(f"All scores are negative: Ignore")
-        scores_arr = scores_arr / temperature
+            return []
+
+        # 采取topk截断
+        candidate_pool.sort(key=lambda x: x['score'], reverse=True)
+        current_k = min(10, len(candidate_pool))
+        top_candidates = candidate_pool[:current_k]
+        top_scores = np.array([x['score'] for x in top_candidates])
+
+        #降低温度，使得选择稳定
+        temperature = 0.2
+        scaled_scores = top_scores / temperature
         
         # Softmax: 将分数转换为概率
-        exp_scores = np.exp(scores_arr - np.max(scores_arr))
+        exp_scores = np.exp(scaled_scores - np.max(scaled_scores))
         probabilities = exp_scores / np.sum(exp_scores)
         
         # 采样 (或直接取 Top 1)
-        sampled_index = np.random.choice(a=len(results['ids'][0]), p=probabilities, size=n_results, replace=False)  # 不重复采样
+        sampled_index = np.random.choice(a=current_k, p=probabilities, size=min(n_results, current_k), replace=False)  # 不重复采样
-        sampled_items = [metadatas[i] for i in sampled_index]
+        sampled_items = [top_candidates[i]['metadata'] for i in sampled_index]
+
         return sampled_items