Update RAG_Learning_Assistant_with_Streaming.py via admin tool

RAG_Learning_Assistant_with_Streaming.py

@@ -11,27 +11,32 @@ import re
 class RAGLearningAssistant:
     def __init__(self, api_key: str, model: str = "gpt-4.1-nano-2025-04-14", vector_db_path: str = ""):
         """
-        初始化RAG学习助手
+        初始化RAG学习助手（适配学生Space）
         
         Args:
             api_key: OpenAI API密钥（必需）
             model: 使用的模型名称
-            vector_db_path: 向量数据库路径
+            vector_db_path: 向量数据库所在目录路径（数据存储仓库的本地目录）
         """
         self.client = OpenAI(api_key=api_key)
+        
+        # 使用修改后的KnowledgeBaseVectorizer，指定vector_db_dir
         self.vectorizer = KnowledgeBaseVectorizer(
             api_key=api_key,
-            #data_path=os.path.join(vector_db_path, "knowledge_base.md")
-            data_path="knowledge_base.md"
+            vector_db_dir=vector_db_path  # 传递数据存储仓库的本地目录
         )
         
         # 预加载向量数据库到缓存
-        print("预加载向量数据库...")
-        self.vectorizer.load_vector_database()
+        print("[RAGLearningAssistant] Preloading vector database...")
+        load_result = self.vectorizer.load_vector_database()
+        if load_result[0] is not None:
+            print(f"[RAGLearningAssistant] Vector database loaded successfully")
+        else:
+            print(f"[RAGLearningAssistant] Warning: Failed to load vector database")
         
         # 模型配置
         self.model = model
-        self.temperature = 0.2
+        self.temperature = 0.1
         self.max_tokens = 2000
         
         # 系统提示词
@@ -119,7 +124,7 @@ Return the entities as a JSON array of strings. Only include the most important
             response = self.client.chat.completions.create(
                 model=self.model,
                 messages=messages,
-                temperature=0.3,  # 低温度确保一致性
+                temperature=0.1,  # 低温度确保一致性
                 max_tokens=2000
             )
             
@@ -173,22 +178,22 @@ Return the entities as a JSON array of strings. Only include the most important
             
             # 如果仍然没有获得有效结果，使用更简单的方法
             if not summary and self.conversation_history:
-                summary = "继续之前的讨论"
+                summary = "Continue previous discussion"
             
             if not rewritten or rewritten == query:
                 rewritten = query
             
-            print(f"Raw query: {query}")
-            print(f"Chat history summary: {summary}")
-            print(f"Rewrite query: {rewritten}")
+            print(f"[rewrite_query] Raw query: {query}")
+            print(f"[rewrite_query] Chat history summary: {summary}")
+            print(f"[rewrite_query] Rewritten query: {rewritten}")
             return summary, rewritten
             
         except Exception as e:
-            print(f"查询重写失败: {e}")
+            print(f"[rewrite_query] Query rewriting failed: {e}")
             # 生成简单的历史总结作为备用
             simple_summary = ""
             if self.conversation_history:
-                simple_summary = "基于之前的对话内容"
+                simple_summary = "Based on previous conversation content"
             return simple_summary, query  # 失败时返回简单总结和原始查询
     
     def extract_entities(self, original_query: str, summary: str, rewritten_query: str) -> List[str]:
@@ -229,8 +234,8 @@ Return the entities as a JSON array of strings. Only include the most important
             response = self.client.chat.completions.create(
                 model=self.model,
                 messages=messages,
-                temperature=0.3,
-                max_tokens=200
+                temperature=self.temperature,
+                max_tokens=self.max_tokens
             )
             
             content = response.choices[0].message.content.strip()
@@ -244,18 +249,18 @@ Return the entities as a JSON array of strings. Only include the most important
                 else:
                     entities = json.loads(content)
                 
-                print(f"Extracted entities: {entities}")
+                print(f"[extract_entities] Extracted entities: {entities}")
                 return entities
                 
             except json.JSONDecodeError:
                 # 如果JSON解析失败，尝试简单的文本处理
-                print(f"JSON解析失败，使用备用方法")
+                print(f"[extract_entities] JSON parsing failed, using backup method")
                 # 查找引号中的内容
                 entities = re.findall(r'"([^"]+)"', content)
                 return entities if entities else self.simple_entity_extraction(combined_text)
                 
         except Exception as e:
-            print(f"实体提取失败: {e}")
+            print(f"[extract_entities] Entity extraction failed: {e}")
             # 失败时使用��单的关键词提取
             return self.simple_entity_extraction(combined_text)
     
@@ -284,9 +289,9 @@ Return the entities as a JSON array of strings. Only include the most important
         entities.extend(special_terms)
         
         # 去重并返回
-        return list(set(entities))[:5]  # 最多返回5个实体
+        return list(set(entities))[:10]  # 最多返回5个实体
     
-    def enhanced_search(self, query: str, top_k: int = 3) -> Tuple[str, str, List[str], List[Tuple[Dict, float, Dict]]]:
+    def enhanced_search(self, query: str, top_k: int = 5) -> Tuple[str, str, List[str], List[Tuple[Dict, float, Dict]]]:
         """
         增强搜索：重写查询 -> 提取实体 -> 基于实体搜索（优化版本）
         
@@ -309,12 +314,12 @@ Return the entities as a JSON array of strings. Only include the most important
             search_results = self.vectorizer.search_with_entities_optimized(entities, top_k)
         else:
             # 如果没有提取到实体，使用重写后的查询进行搜索
-            print("未提取到实体，使用完整查询搜索")
+            print("[enhanced_search] No entities extracted, using full query search")
             search_results = self.vectorizer.search_similar(
                 rewritten_query, 
                 top_k=top_k,
-                title_weight=0.4,
-                content_weight=0.3,
+                title_weight=0.2,
+                content_weight=0.5,
                 full_weight=0.3
             )
         
@@ -338,7 +343,6 @@ Return the entities as a JSON array of strings. Only include the most important
             entry, combined_score, details = result
             # 只显示 title, source, content，不显示 id
             context_parts.append(
-                #f"[Source {i}]\n"
                 f"Title: {entry['title']}\n"
                 f"From: {entry['source']}\n"
                 f"Content: {entry['content']}\n"
@@ -385,7 +389,7 @@ Return the entities as a JSON array of strings. Only include the most important
             响应文本片段
         """
         # 1. 增强搜索（现在使用优化版本）
-        print("正在处理查询...")
+        print("[generate_response_stream] Processing query...")
         summary, rewritten_query, entities, search_results = self.enhanced_search(query)
         
         # 2. 格式化上下文
@@ -413,7 +417,7 @@ Return the entities as a JSON array of strings. Only include the most important
             if summary:
                 search_info += f"- Summary of history: {summary}\n"
             if rewritten_query != query:
-                search_info += f"- Rewrite query: {rewritten_query}\n"
+                search_info += f"- Rewritten query: {rewritten_query}\n"
             search_info += f"- Key entities: {', '.join(entities) if entities else 'No specific entities extracted'}\n"
             
             if search_results:
@@ -429,7 +433,7 @@ Return the entities as a JSON array of strings. Only include the most important
             # 添加缓存信息（调试用）
             cache_info = self.vectorizer.get_cache_info()
             if cache_info['is_cached']:
-                search_info += f"The vector db has been cached, containing {cache_info['cache_size']} entries\n\n"
+                search_info += f"💡 Vector database cached with {cache_info['cache_size']} entries\n\n"
 
             yield search_info
             
@@ -445,7 +449,7 @@ Return the entities as a JSON array of strings. Only include the most important
             self.conversation_history.append({"role": "assistant", "content": full_response})
             
         except Exception as e:
-            yield f"\n\n错误：生成响应时出现问题 - {str(e)}"
+            yield f"\n\nError: Problem occurred while generating response - {str(e)}"
     
     def generate_response(self, query: str) -> str:
         """
@@ -465,18 +469,18 @@ Return the entities as a JSON array of strings. Only include the most important
     def clear_history(self):
         """清除对话历史"""
         self.conversation_history = []
-        print("对话历史已清除")
+        print("[clear_history] Conversation history cleared")
     
     def clear_vector_cache(self):
         """清除向量数据库缓存"""
         self.vectorizer.clear_cache()
-        print("向量数据库缓存已清除")
+        print("[clear_vector_cache] Vector database cache cleared")
     
     def reload_vector_database(self):
         """重新加载向量数据库"""
-        print("重新加载向量数据库...")
+        print("[reload_vector_database] Reloading vector database...")
         self.vectorizer.load_vector_database(force_reload=True)
-        print("向量数据库重新加载完成")
+        print("[reload_vector_database] Vector database reload completed")
     
     def get_system_status(self) -> Dict:
         """
@@ -514,5 +518,4 @@ Return the entities as a JSON array of strings. Only include the most important
         with open(filepath, 'w', encoding='utf-8') as f:
             json.dump(conversation_data, f, ensure_ascii=False, indent=2)
         
-        print(f"对话已保存到: {filepath}")
-
+        print(f"[save_conversation] Conversation saved to: {filepath}")