更新专业术语索引文件，优化意图识别逻辑，添加后缀项更新功能，调整重排序参数以提高相关性，同时修正文档中的描述信息。

rag2_0/dify/intent_recognition_api.py

@@ -63,7 +63,7 @@ def intent_recognize():
             for term in keywords["terms"]:
                 term_info = {
                     "名称": term["name"],
-                    # "同义词": ";".join(term["synonymous"]) if term["synonymous"] else "",
+                    # "同义词": ";".join(term["synonymous"]) if term["synonymous"] else [],
                     # "描述": term["description"]
                 }
                 term_details.append(term_info)

rag2_0/intent_recognition/IntentRecognition.py

@@ -184,7 +184,7 @@ class IntentRecognizer:
         except Exception as e:
             raise RuntimeError(f"无法解析LLM关键词提取响应: {e}") from e
     
-    def _rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2) -> List[Term]:
+    def _rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2, rerank_score:float = 0.6) -> List[Term]:
         """
         对召回的专业术语进行重排序，按与用户查询的相关性排序
         
@@ -199,9 +199,13 @@ class IntentRecognizer:
         if not matched_terms:
             return []
         
+        if len(matched_terms) <= top_k:
+            return list(matched_terms)
+
         try:
             # 将每个术语转换为可用于重排序的文本表示
-            term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) + "|" + "描述:" + term.description for term in matched_terms]
+            # term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) + "|" + "描述:" + term.description for term in matched_terms]
+            term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) for term in matched_terms]
             
             # 使用重排序模型
             xinference_reranker = SiliconFlowReRankerModel()
@@ -211,7 +215,7 @@ class IntentRecognizer:
             matched_terms_list = list(matched_terms)
             
             # 根据重排序结果获取排序后的术语列表
-            reranked_terms = [matched_terms_list[result["index"]] for result in rerank_results if result["score"] >= 0.6]
+            reranked_terms = [matched_terms_list[result["index"]] for result in rerank_results if result["score"] >= rerank_score]
             
             return reranked_terms
             
@@ -279,7 +283,8 @@ class IntentRecognizer:
             改写结果
         """
         # 准备问题改写提示
-        terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms] 
+        # terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms] 
+        terms_dict = [term.model_dump() for term in keywords.terms] 
         keywords_str = json.dumps(terms_dict, ensure_ascii=False)
         query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
         # formatted_prompt = query_rewrite_prompt.format(query=query, 
@@ -369,7 +374,7 @@ class IntentRecognizer:
         )
         
         # 步骤3: 进行意图识别和槽位填充
-        result = self._process_intent_and_slot(query, conversation_context, chat_history, previous_slots)
+        result = self._process_intent_and_slot(rewrite.rewrite, conversation_context, chat_history, previous_slots)
         result.update({"keywords": keywords_terms.model_dump(),
                               "rewrite": rewrite.model_dump(),
                               "query_keys": query_keys})

rag2_0/intent_recognition/Multi_PromptTemplates.py

@@ -159,7 +159,7 @@ graph TD
 
 ### 三、重构优先级
 1. **背景补充**
-   - 历史对话中确定的软件/地区必须继承（例："这软件"→"【配网工程D3】"）
+   - 历史对话中确定的背景信息需要保留（例："这软件"→"【配网工程D3】"）
  
 2. **术语处理**
    - 同义词转标准词 → 批量设置定额
@@ -190,9 +190,7 @@ graph TD
 
 
 intent_and_slot_prompt = """
-# 电力造价软件意图分类与槽位填充统一提示词
+# 你是一个专业的电力造价领域智能助手，负责对用户输入进行意图分类识别和关键信息槽位填充。
-
-你是一个专业的电力造价领域智能助手，负责对用户输入进行意图分类识别和关键信息槽位填充。
 
 {classification_info}
 
@@ -206,6 +204,7 @@ intent_and_slot_prompt = """
 - **技改检修工程计价通T1软件**：别名包括技改T1、T1软件、技改检修软件等
 - **技改检修清单计价通T1软件**：别名包括技改清单T1、T1清单软件、技改检修清单软件等
 - **主网电力建设计价通软件**：别名包括主网软件、电力建设软件、主网建设软件、博微电力建设计价通等
+不在上述软件之列的，使用用户输入中的软件名称
 
 ## 【任务要求】
 

rag2_0/intent_recognition/ProfessionalNounVector.py

@@ -127,7 +127,9 @@ class ProfessionalNounVectorizer:
             
             # 准备数据
             texts, metadatas = self._prepare_terms_for_faiss(deduplicated_terms)
-            
+            suffix_text,suffix_metadatas = self._updata_suffix_item()
+            texts.extend(suffix_text)
+            metadatas.extend(suffix_metadatas)
             # 创建索引
             faiss_index = self._create_index(texts, metadatas)
             
@@ -140,6 +142,30 @@ class ProfessionalNounVectorizer:
             logging.error(f"多文件向量化处理失败: {e}")
             return False
     
+    def _updata_suffix_item(self)->Tuple[List[str], List[Dict]] :
+        """
+        更新suffix_keywords.json文件
+        
+        Returns:
+            更新后的术语列表
+        """
+        # 加载suffix_keywords.json文件
+        text=[]
+        meta_info=[]
+
+        suffix_keywords_path = os.path.join(".", 'data', 'nouns', 'suffix_keywords.json')
+        if os.path.exists(suffix_keywords_path):
+            try:
+                with open(suffix_keywords_path, 'r', encoding='utf-8') as f:
+                    suffix_terms = json.load(f)
+                    suffix_terms = [{"name": term["name"].upper(), "synonymous": [], "description": ""} for term in suffix_terms]
+                    for cur_suffix in suffix_terms:
+                        text.append(cur_suffix["name"].upper())
+                        meta_info.append(cur_suffix)
+                    logging.info(f"加载{suffix_keywords_path}，共{len(suffix_terms)}条")
+            except Exception as e:
+                logging.warning(f"读取{suffix_keywords_path}失败: {e}")
+        return text,meta_info
     
     def _prepare_terms_for_faiss(self, terms: List[Dict[str, Any]]) -> Tuple[List[str], List[Dict]]:
         """
@@ -156,15 +182,9 @@ class ProfessionalNounVectorizer:
         
         for term in terms:
             name = term["name"]
-            texts.append(name.strip())
             synonymous = term.get("synonymous", [])
             description = term.get("description", "")
             # 记录元数据
-            metadatas.append({
-                "name": name,
-                "synonymous": synonymous,
-                "description": description
-            })
 
             if len(synonymous) > 0:
                 for synonyms_str in synonymous:
@@ -175,13 +195,21 @@ class ProfessionalNounVectorizer:
                         "description": description
                     })
             
-            if len(description) > 0:
+            # texts.append(name.strip())
-                texts.append(description.strip())
+            # metadatas.append({
-                metadatas.append({
+            #     "name": name,
-                    "name": name,
+            #     "synonymous": synonymous,
-                    "synonymous": synonymous,
+            #     "description": description
-                    "description": description
+            # })
-                })
+
+            # 不检索描述字段
+            # if len(description) > 0:
+            #     texts.append(description.strip())
+            #     metadatas.append({
+            #         "name": name,
+            #         "synonymous": synonymous,
+            #         "description": description
+            #     })
         
         return texts, metadatas