返回增加reasoning

api/data_converter.py

@@ -159,19 +159,33 @@ def convert_pipeline_output_to_api_response(
                         if 0 <= note_index < len(notes_data):
                             note = notes_data[note_index]
                             note_id = note.get('id', '')
-                            
+
                             # 获取最高相似度（从相似度分析结果中获取）
                             max_similarity = similarity_map.get(note_id, 0)
-                            
+
                             # 计算贡献
                             contribution = evaluation_score * max_similarity if max_similarity > 0 else evaluation_score
-                            
+
+                            # 获取该帖子的解构特征匹配详情
+                            matched_features = extract_matched_features_from_results(
+                                similarity_results,
+                                note_id
+                            )
+
                             matched_note = {
                                 'note_id': note_id,
                                 'note_title': note.get('note_card', {}).get('display_title', ''),
                                 'evaluation_score': round(evaluation_score, 3),
                                 'max_similarity': round(max_similarity, 3),
                                 'contribution': round(contribution, 3),
+
+                                # 评估详情
+                                'evaluation_reasoning': note_eval.get('评分说明', ''),
+                                'key_matching_points': note_eval.get('关键匹配点', []),
+                                'query_relevance': note_eval.get('Query相关性', ''),
+                                'query_relevance_explanation': note_eval.get('Query相关性说明', ''),
+                                'matched_features': matched_features,
+
                                 'note_data': note  # 包含完整的搜索结果信息
                             }
                             matched_notes.append(matched_note)
@@ -206,26 +220,65 @@ def extract_similarity_map_from_results(
 ) -> Dict[str, float]:
     """
     从相似度分析结果中提取note_id到max_similarity的映射
-    
+
     Args:
         similarity_results: 相似度分析结果
-    
+
     Returns:
         note_id -> max_similarity 的映射
     """
     similarity_map = {}
-    
+
     if not similarity_results:
         return similarity_map
-    
+
     results_list = similarity_results.get('results', [])
     for result in results_list:
         note_id = result.get('note_id', '')
         statistics = result.get('similarity_statistics', {})
         max_similarity = statistics.get('max_similarity', 0)
-        
+
         if note_id:
             similarity_map[note_id] = max_similarity
-    
+
     return similarity_map
 
+
+def extract_matched_features_from_results(
+    similarity_results: Optional[Dict[str, Any]],
+    note_id: str
+) -> List[Dict[str, Any]]:
+    """
+    从相似度分析结果中提取指定帖子的解构特征匹配详情
+
+    Args:
+        similarity_results: 相似度分析结果
+        note_id: 帖子ID
+
+    Returns:
+        匹配的特征列表，每个特征包含名称、维度、相似度等信息
+    """
+    if not similarity_results or not note_id:
+        return []
+
+    results_list = similarity_results.get('results', [])
+    for result in results_list:
+        if result.get('note_id', '') == note_id:
+            # 提取解构特征列表（字段名为deconstructed_features）
+            deconstructed_features = result.get('deconstructed_features', [])
+            matched_features = []
+
+            for feature in deconstructed_features:
+                matched_feature = {
+                    'feature_name': feature.get('feature_name', ''),
+                    'dimension': feature.get('dimension', ''),
+                    'dimension_detail': feature.get('dimension_detail', ''),
+                    'weight': feature.get('weight', 0),
+                    'similarity_score': round(feature.get('similarity_score', 0), 3)
+                }
+                matched_features.append(matched_feature)
+
+            return matched_features
+
+    return []
+

api/search_service.py

@@ -48,6 +48,15 @@ class SearchRequest(BaseModel):
 
 
 # 响应模型
+class MatchedFeature(BaseModel):
+    """匹配的解构特征模型"""
+    feature_name: str = Field(..., description="特征名称")
+    dimension: str = Field(..., description="特征维度（如：灵感点-全新内容）")
+    dimension_detail: str = Field(..., description="维度详情（如：实质、形式、意图）")
+    weight: float = Field(..., description="特征权重")
+    similarity_score: float = Field(..., description="相似度得分")
+
+
 class MatchedNote(BaseModel):
     """匹配的帖子模型"""
     note_id: str
@@ -55,6 +64,14 @@ class MatchedNote(BaseModel):
     evaluation_score: float
     max_similarity: float
     contribution: float
+
+    # 评估详情
+    evaluation_reasoning: str = Field(..., description="综合得分的评分说明")
+    key_matching_points: List[str] = Field(..., description="关键匹配点列表")
+    query_relevance: str = Field(..., description="Query相关性（相关/不相关）")
+    query_relevance_explanation: str = Field(..., description="Query相关性说明")
+    matched_features: List[MatchedFeature] = Field(..., description="匹配的解构特征列表")
+
     note_data: Dict[str, Any]  # 完整的搜索结果信息