fix: 修复前台4项问题(MCP列举/微信技能上限/Word资产栏/技能MCP反馈) + 同步容器live状态

- #549 平台MCP列举: 注入全量已启用MCP清单, AI不再凭空编造
- #550 微信Bot技能自动发现上限 15->300
- #551 知识库Word文档同步资产后Word栏可见 + 双向删除同步(assets/knowledge级联软删)
- #552 对话反馈实际使用的技能/MCP(used_mcp字段), 含历史消息序列化
- 同步容器内其他未提交live改动(im_bot_platform/TokenDashboard/WechatBot/ChatPage)

backend/python/app/api/assets.py

@@ -368,6 +368,19 @@ def delete_asset(
     asset.status = "deleted"
     asset.updated_at = datetime.now()
 
+    # 若资产由知识库文档同步而来，双向删除对应的知识库文档
+    if asset.knowledge_doc_id:
+        try:
+            kb_doc = db.query(KnowledgeDocument).filter(KnowledgeDocument.id == asset.knowledge_doc_id).first()
+            if kb_doc:
+                kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_doc.knowledge_base_id).first()
+                if kb:
+                    kb.document_count = max(0, (kb.document_count or 0) - 1)
+                    kb.updated_at = datetime.now()
+                db.delete(kb_doc)
+        except Exception:
+            pass
+
     LogService.log_operation(
         user_id=current_user.id, username=current_user.username, action="delete",
         target="asset", target_id=asset.id,

backend/python/app/api/chat.py

@@ -4094,7 +4094,28 @@ def _resolve_active_mcp_servers(db: Session, mcp_ids: list) -> list:
 
 
 
-def _discover_installed_skills(db: Session, user_id: str, limit: int = 15) -> list:
+def _list_enabled_mcp_servers(db: Session) -> list:
+    """返回平台所有已启用(is_enabled=True)的 MCP 服务清单，供 AI 准确回答"平台有哪些 MCP"。"""
+    servers = db.query(MCPServer).filter(MCPServer.is_enabled == True).all()
+    out = []
+    for s in servers:
+        try:
+            headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
+        except Exception:
+            headers = {}
+        out.append({
+            "id": s.id,
+            "name": s.name,
+            "description": s.description or "",
+            "transport": s.transport_type or "streamable_http",
+            "endpoint": s.url or "",
+            "url": s.url or "",
+            "headers": headers,
+        })
+    return out
+
+
+def _discover_installed_skills(db: Session, user_id: str, limit: int = 300) -> list:
 
 
 
@@ -5311,6 +5332,7 @@ def _build_system_prompt(
     mcp_functions: list = None,
 
     auto_discovered: bool = False,
+    all_mcp: list = None,
 
 ) -> tuple:
 
@@ -5745,6 +5767,15 @@ def _build_system_prompt(
 
 
 
+    # 平台全量 MCP 清单（Issue：让用户询问"平台有哪些 MCP"时 AI 能准确列举，而非凭空编造）
+    if all_mcp:
+        _mcp_inventory = "\n".join([f"- {m.get('name','')}: {m.get('description','') or '（未填写描述）'}" for m in all_mcp])
+        system_parts.append(
+            f"【平台已安装的 MCP 应用清单（共 {len(all_mcp)} 个，均为平台真实接入并启用）】\n{_mcp_inventory}\n"
+            "当用户询问“平台有哪些 MCP / 接入了哪些 MCP / 有哪些工具服务”时，请严格依据以上清单准确列举名称与功能，"
+            "不要编造或遗漏，也不要把未列出的服务说成已接入。"
+        )
+
     # MCP 应用注入（Issue 3：让用户询问 MCP 能力时，AI 能“看到”该 MCP 的资料与地址）
 
 
@@ -7074,6 +7105,7 @@ def get_messages(
 
 
             "used_skills": msg.used_skills or [],
+            "used_mcp": msg.used_mcp or [],
 
 
 
@@ -8090,6 +8122,7 @@ async def send_message(
                         mcp_functions=mcp_function_definitions,
 
                         auto_discovered=used_auto_skills,
+                        all_mcp=_list_enabled_mcp_servers(db),
 
                     )
 
@@ -8103,6 +8136,10 @@ async def send_message(
 
 
 
+                    # 实际调用追踪（Issue：反馈真实使用的技能/MCP，而非注入的全部）
+                    called_skill_names = []
+                    called_mcp_names = []
+
                     messages = [{"role": "system", "content": system_prompt}]
 
 
@@ -8433,6 +8470,8 @@ async def send_message(
 
                                                 )
 
+                                                called_mcp_names.append(_srv.get("name", ""))
+
                                     else:
 
                                         tool_result = _execute_skill_tool(
@@ -8441,6 +8480,12 @@ async def send_message(
 
                                         )
 
+                                        _sk = next((s for s in skills if s.get("func_name") == func_name), None)
+
+                                        if _sk:
+
+                                            called_skill_names.append(_sk.get("name", ""))
+
 
 
                                     messages.append({
@@ -8671,7 +8716,7 @@ async def send_message(
 
 
 
-    # 更新used_skills
+    # 更新used_skills / used_mcp：优先反馈用户显式指定的；否则反馈本轮实际调用的
 
 
 
@@ -8679,15 +8724,91 @@ async def send_message(
 
 
 
-    if skills:
+    used_mcp = []
 
 
 
-        used_skills = [s["name"] for s in skills]
+    if effective_skill_ids:
 
 
 
-        ai_msg.used_skills = used_skills
+        # 用户显式选择了技能 -> 反馈配对的技能名
+
+
+
+        try:
+
+
+
+            _sel = _resolve_market_skills(db, effective_skill_ids, user_id)
+
+
+
+            used_skills = [s["name"] for s in _sel]
+
+
+
+        except Exception:
+
+
+
+            used_skills = []
+
+
+
+    elif called_skill_names:
+
+
+
+        # 未显式选技能 -> 反馈 AI 本轮实际调用的技能（可能为空）
+
+
+
+        used_skills = list(dict.fromkeys(called_skill_names))
+
+
+
+    if effective_mcp_ids:
+
+
+
+        # 用户显式选择了 MCP -> 反馈配对的 MCP 名
+
+
+
+        try:
+
+
+
+            _sel_mcp = _resolve_active_mcp_servers(db, effective_mcp_ids)
+
+
+
+            used_mcp = [m["name"] for m in _sel_mcp]
+
+
+
+        except Exception:
+
+
+
+            used_mcp = []
+
+
+
+    elif called_mcp_names:
+
+
+
+        used_mcp = list(dict.fromkeys(called_mcp_names))
+
+
+
+    ai_msg.used_skills = used_skills
+
+
+
+    ai_msg.used_mcp = used_mcp
 
 
 
@@ -8980,6 +9101,7 @@ async def send_message(
 
 
                 "used_skills": used_skills,
+                "used_mcp": used_mcp,
 
 
 

backend/python/app/api/im_bot_platform.py

@@ -188,7 +188,7 @@ def _qihua_reply(text: str, user_id: str = None, platform: str = "im") -> str:
     try:
         from app.api.im_llm import _call_real_llm
         db = SessionLocal()
-        return _call_real_llm(db, None, prompt, platform)
+        return _call_real_llm(db, None, prompt, platform, user_id=user_id)
     except Exception as e:
         return f"（AI 回复生成失败：{e}）"
     finally:

backend/python/app/api/knowledge.py

@@ -1149,15 +1149,21 @@ def _split_content_into_chunks(content: str, chunk_size: int = 500) -> list:
 def _sync_asset_for_knowledge_doc(db: Session, user_id: str, doc_id: str, filename: str, file_type: str, file_size: int):
     """将知识库文档同步为资产记录（问题9：资产关联知识库文档用量）"""
     try:
+        # 文件类型归一化为前端子分类标签（docx/doc->word 等），确保出现在「文件 > Word」子栏
+        _ft_label = {
+            "pdf": "pdf", "docx": "word", "doc": "word", "xlsx": "excel", "xls": "excel",
+            "pptx": "ppt", "ppt": "ppt", "csv": "txt", "md": "md", "txt": "txt",
+        }.get((file_type or "").lower(), "other")
         asset = Asset(
             id=gen_uuid(),
             user_id=user_id,
             name=filename,
-            category="doc",
+            category="file",
             description=f"知识库文档 (ID: {doc_id})",
-            file_type=file_type,
+            file_type=_ft_label,
             size=file_size,
             status="active",
+            knowledge_doc_id=doc_id,
             created_at=datetime.now(),
             updated_at=datetime.now(),
         )
@@ -1191,6 +1197,15 @@ def delete_document(
     pass  # total_size removed
     kb.updated_at = datetime.now()
 
+    # 同步删除知识库文档对应的资产记录（双向同步）
+    try:
+        linked_asset = db.query(Asset).filter(Asset.knowledge_doc_id == doc_id).first()
+        if linked_asset:
+            linked_asset.status = "deleted"
+            linked_asset.updated_at = datetime.now()
+    except Exception as _e:
+        logger.warning(f"同步删除关联资产失败: {_e}")
+
     db.delete(doc)
     db.commit()
 

backend/python/app/models.py

@@ -106,6 +106,7 @@ class Message(Base):
     mcp_ids = Column(JSON, default=None, comment="该轮对话选择的MCP应用ID列表")
     attachments = Column(JSON, default=None, comment="该轮对话附带的文件附件列表")
     used_skills = Column(JSON, default=None, comment="该轮对话调用的进化技能名列表")
+    used_mcp = Column(JSON, default=None, comment="该轮对话调用的MCP应用名列表")
     created_at = Column(DateTime, default=datetime.now)
 
 
@@ -747,6 +748,7 @@ class Asset(Base):
     content = Column(Text, default=None)
     novel_meta = Column(JSON, default=None)
     status = Column(String(10), nullable=False, default="active")  # active / deleted
+    knowledge_doc_id = Column(String(36), default=None, comment="关联的知识库文档ID（由知识库同步资产时写入）")
     created_at = Column(DateTime, default=datetime.now)
     updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
 

frontend/src/pages/front/AssetsPage.tsx

@@ -54,6 +54,7 @@ function mapFileTypeToLabel(ft: string): string {
   const mapping: Record<string, string> = {
     pdf: 'pdf', docx: 'word', doc: 'word', xlsx: 'excel', xls: 'excel',
     pptx: 'ppt', ppt: 'ppt', csv: 'txt', md: 'md', txt: 'txt',
+    word: 'word', excel: 'excel',
   };
   return mapping[ft] || 'other';
 }
@@ -399,7 +400,7 @@ const AssetsPage: React.FC = () => {
   const VALID_CATEGORIES = ['file', 'novel', 'image', 'video', 'audio', 'custom', 'other'];
   const normalizeAsset = (item: any): Asset => {
     const rawCat = item.category;
-    const ft = item.fileType || item.file_type || 'other';
+    const ft = mapFileTypeToLabel(item.fileType || item.file_type || 'other');
     let category = rawCat;
     if (!VALID_CATEGORIES.includes(category)) {
       category = (ft && ft !== 'other') ? 'file' : 'other';

frontend/src/pages/front/TokenDashboardPage.tsx

@@ -160,6 +160,37 @@ function getMediaStats(logs: TokenUsageLog[]) {
   return { imageCount, videoCount, imageTokens, videoTokens, models };
 }
 
+// 分页拉取全部 token 日志（后端 /logs 默认只返回 20 条，会导致前端上下翻页无法查看更早记录）。
+// 通过 page_size=100 循环拉取，直到不足一页为止。
+async function fetchAllTokenLogs(): Promise<TokenUsageLog[]> {
+  const all: any[] = [];
+  let page = 1;
+  let hasMore = true;
+  while (hasMore) {
+    const res = await tokenAPI.getLogs({ page_size: 100, page });
+    const rawLogs = (res.data as any)?.items || res.data || [];
+    if (!Array.isArray(rawLogs) || rawLogs.length === 0) {
+      hasMore = false;
+      break;
+    }
+    all.push(
+      ...rawLogs.map((item: any) => ({
+        ...item,
+        promptTokens: item.promptTokens ?? item.prompt_tokens ?? 0,
+        completionTokens: item.completionTokens ?? item.completion_tokens ?? 0,
+        totalTokens: item.totalTokens ?? item.total_tokens ?? 0,
+        modelName: item.modelName ?? item.model_name ?? '',
+        cost: item.cost ?? 0,
+        createdAt: item.createdAt ?? item.created_at ?? item.createdAt,
+        appName: item.appName ?? item.app_name ?? '',
+      }))
+    );
+    if (rawLogs.length < 100) hasMore = false;
+    else page += 1;
+  }
+  return all as TokenUsageLog[];
+}
+
 const TokenDashboardPage: React.FC = () => {
   const navigate = useNavigate();
   const [totalConsumed, setTotalConsumed] = useState(0);
@@ -178,28 +209,18 @@ const TokenDashboardPage: React.FC = () => {
       setLoading(true);
       setError(null);
       try {
-        const [overviewRes, balanceRes, logsRes] = await Promise.all([
+        const [overviewRes, balanceRes, allLogs] = await Promise.all([
           tokenAPI.getOverview(),
           tokenAPI.getBalance(),
-          tokenAPI.getLogs(),
+          fetchAllTokenLogs(),
         ]);
         setTotalConsumed(overviewRes.data?.total_used || overviewRes.data?.total_consumed || overviewRes.data?.totalConsumed || 0);
         setBalance(balanceRes.data?.balance || 0);
         // 从overview响应中获取服务器时间
         const serverTimeStr = overviewRes.data?.server_time || overviewRes.data?.serverTime || overviewRes.data?.current_time || overviewRes.data?.time || '';
         setServerTime(serverTimeStr);
-        // 兼容分页格式和直接数组两种返回格式
+        // 已拉取全部日志（fetchAllTokenLogs 内部循环分页），客户端可自由上下翻页
-        const rawLogs = (logsRes.data as any)?.items || logsRes.data || [];
+        setLogs(allLogs);
-        // 兼容后端返回的 snake_case 字段名
-        setLogs(rawLogs.map((item: any) => ({
-          ...item,
-          promptTokens: item.promptTokens ?? item.prompt_tokens ?? 0,
-          completionTokens: item.completionTokens ?? item.completion_tokens ?? 0,
-          totalTokens: item.totalTokens ?? item.total_tokens ?? 0,
-          modelName: item.modelName ?? item.model_name ?? '',
-          cost: item.cost ?? 0,
-          createdAt: item.createdAt ?? item.created_at ?? item.createdAt,
-        })));
       } catch (err: any) {
         setError(err.message || '数据加载失败');
       } finally {

frontend/src/pages/front/WechatBot.tsx

@@ -93,7 +93,16 @@ const WechatBot: React.FC = () => {
     }
   };
 
-  const formatTime = (t: string) => (t ? new Date(t).toLocaleString('zh-CN') : '-');
+  // 后端按北京时间(Asia/Shanghai)存储连接时间（无时区标记），前端统一按北京时间展示，
+  // 不受浏览器所在时区影响。
+  const formatTime = (t: string) => {
+    if (!t) return '-';
+    const iso = t.includes('T') ? t : t.replace(' ', 'T');
+    const withTz = iso.includes('+') || iso.includes('Z') ? iso : iso + '+08:00';
+    const d = new Date(withTz);
+    if (isNaN(d.getTime())) return t;
+    return d.toLocaleString('zh-CN', { timeZone: 'Asia/Shanghai', hour12: false });
+  };
 
   return (
     <div style={{ minHeight: '100%', background: '#FFFFFF', color: '#1A1F36', padding: '32px' }}>

frontend/src/types/index.ts

@@ -61,6 +61,7 @@ export interface Message {
   tokenUsed: number;
   createdAt: string;
   used_skills?: string[];
+  used_mcp?: string[];
 }