添加公共工具：联网搜索（DuckDuckGo）和可视化图表（Mermaid），更新 intent.py 支持 WEB_SEARCH 动作

backend/app/core/intent.py

@@ -29,7 +29,8 @@ class ReasoningAction(Enum):
     DIRECT_RESPONSE = auto()  # 直接回答，不需要额外信息
     RETRIEVE_RAG = auto()     # 需要调用 RAG 检索
     RE_RETRIEVE_RAG = auto()  # 需要重新检索（更多/更好结果）
-    ROUTE_SUBGRAPH = auto()   # 需要路由到子图（contact/dictionary/news_analysis）
+    WEB_SEARCH = auto()       # 需要联网搜索
+    ROUTE_SUBGRAPH = auto()   # 需要路由到子图（contact/dictionary/news_analysis/research）
     CLARIFY = auto()          # 需要澄清用户的问题
     UNKNOWN = auto()          # 未知动作
 
@@ -78,7 +79,8 @@ class ReactIntentReasoner:
         self._subgraph_keywords = {
             "contact": ["通讯录", "联系人", "contact", "email", "邮件", "邮箱"],
             "dictionary": ["词典", "单词", "翻译", "dictionary", "translate", "生词"],
-            "news_analysis": ["资讯", "新闻", "分析", "news", "report", "热点"]
+            "news_analysis": ["资讯", "新闻", "分析", "news", "report", "热点"],
+            "research": ["研究", "深度分析", "报告", "引用", "溯源", "research", "analyze", "report"]
         }
 
     def _get_llm_service(self):
@@ -146,11 +148,12 @@ class ReactIntentReasoner:
 1. DIRECT_RESPONSE - 直接回答（闲聊、打招呼、不需要额外信息）
 2. RETRIEVE_RAG - 需要查询知识库（询问知识、政策、文档等）
 3. RE_RETRIEVE_RAG - 需要重新检索（之前的结果不够，或者用户明确说"再查查"、"更多"）
-4. ROUTE_SUBGRAPH - 需要路由到专门的子图：
+4. WEB_SEARCH - 需要联网搜索（询问最新资讯、热点、实时信息、知识库中没有的内容）
+5. ROUTE_SUBGRAPH - 需要路由到专门的子图：
    - contact: 通讯录、联系人、邮件相关
    - dictionary: 词典、翻译、单词相关
    - news_analysis: 资讯、新闻、热点分析相关
-5. CLARIFY - 需要澄清用户的问题（问题不明确）
+6. CLARIFY - 需要澄清用户的问题（问题不明确）
 
 用户查询: {query}
 当前上下文:
@@ -158,11 +161,12 @@ class ReactIntentReasoner:
 
 请按以下 JSON 格式输出（仅输出 JSON，不要其他内容）:
 {{
-    "action": "DIRECT_RESPONSE|RETRIEVE_RAG|RE_RETRIEVE_RAG|ROUTE_SUBGRAPH|CLARIFY",
+    "action": "DIRECT_RESPONSE|RETRIEVE_RAG|RE_RETRIEVE_RAG|WEB_SEARCH|ROUTE_SUBGRAPH|CLARIFY",
     "confidence": 0.85,
     "reasoning": "简要说明理由",
     "target_subgraph": "contact|dictionary|news_analysis|null (仅当 action=ROUTE_SUBGRAPH 时)",
-    "retrieval_query": "优化后的检索查询 (可选)"
+    "retrieval_query": "优化后的检索查询 (可选)",
+    "search_query": "优化后的搜索查询 (仅当 action=WEB_SEARCH 时)"
 }}
 """
 
@@ -201,6 +205,11 @@ class ReactIntentReasoner:
                 result.retrieval_config.need_re_retrieval = (result.action == ReasoningAction.RE_RETRIEVE_RAG)
                 result.retrieval_config.retrieval_query = data.get("retrieval_query", original_query)
 
+            # 处理联网搜索
+            if result.action == ReasoningAction.WEB_SEARCH:
+                result.metadata["need_web_search"] = True
+                result.metadata["search_query"] = data.get("search_query", original_query)
+
             return result
         except Exception as e:
             print(f"[ReactReasoner] 解析 LLM 响应失败: {e}")
@@ -226,7 +235,22 @@ class ReactIntentReasoner:
                 result.metadata["target_subgraph"] = subgraph_name
                 return result
 
-        # 2. 检查是否需要重新检索
+        # 2. 检查是否需要联网搜索
+        web_search_keywords = ["最新", "今天", "近日", "热点", "新闻", "实时", "搜索", "网上", "互联网", "最新消息", "recent", "latest", "hot", "news", "search", "web"]
+        has_web_search = any(kw in query_lower for kw in web_search_keywords)
+        # 检查是否包含年份（比如2024、2025等），通常需要最新信息
+        import re
+        has_year = bool(re.search(r'202[3-9]|203[0-9]', query))
+        
+        if has_web_search or has_year:
+            result.action = ReasoningAction.WEB_SEARCH
+            result.confidence = 0.85 if has_web_search else 0.7
+            result.reasoning = "需要联网搜索最新信息"
+            result.metadata["need_web_search"] = True
+            result.metadata["search_query"] = query
+            return result
+
+        # 3. 检查是否需要重新检索
         re_retrieve_keywords = ["再", "重新", "更多", "不够", "其他", "没找到", "找不到", "不对", "another", "again", "more"]
         has_re_retrieve = any(kw in query_lower for kw in re_retrieve_keywords)
         has_docs = context.get("retrieved_docs") and len(context["retrieved_docs"]) > 0
@@ -362,6 +386,7 @@ def get_route_by_reasoning(result: ReasoningResult) -> str:
         ReasoningAction.DIRECT_RESPONSE: "direct_response",
         ReasoningAction.RETRIEVE_RAG: "retrieve_rag",
         ReasoningAction.RE_RETRIEVE_RAG: "re_retrieve_rag",
+        ReasoningAction.WEB_SEARCH: "web_search",
         ReasoningAction.CLARIFY: "clarify",
         ReasoningAction.ROUTE_SUBGRAPH: result.metadata.get("target_subgraph", "unknown_subgraph"),
         ReasoningAction.UNKNOWN: "unknown",