前台8项体验修复: 知识库上传自动刷新; 资产PDF归类; Token驾驶舱媒体统计; AI模糊意图澄清选择; ChatPage增加MCP/附件上传/语音; 技能市场安装态修复+清理演示数据; MCP协议应用改名MCP应用; Agent单应用接入(名称/介绍/API Key)

backend/python/app/api/chat.py

@@ -66,6 +66,50 @@ def detect_media_intent(text: str) -> str | None:
         return "audio"
     return None
 
+
+def detect_ambiguous_creative_intent(text: str):
+    """检测「模糊创作意图」：用户使用了创作类动词(制作/创作/做/生成/画/写/设计…)，
+    但未明确产出形式(文案/图片/视频/其他)。此时应与用户确认方向，而不是直接调用大模型
+    （某些模型会对这类开放式创作请求返回 "high risk" 拒答）。
+
+    返回 None 表示无需确认；否则返回 {"options": [...]}。
+    """
+    if not text:
+        return None
+    t = text.strip()
+    tl = t.lower()
+    # 已有明确媒体意图(图像/视频/语音)则无需确认
+    if detect_media_intent(t):
+        return None
+    # 疑问句不触发
+    if tl.endswith(("？", "?")) or any(q in tl for q in ("吗", "呢", "怎么", "如何", "能否", "是否可以", "可以吗", "会不会", "why", "how", "什么", "哪些")):
+        return None
+    # 已明确给出产出形式(文字类/其他类)则无需确认
+    explicit_medium = (
+        "文案", "文章", "诗歌", "诗", "小说", "故事", "代码", "程序", "脚本", "总结", "翻译",
+        "邮件", "报告", "段落", "正文", "文字", "写作", "ppt", "演示", "思维导图", "表格",
+        "图表", "数据",
+    )
+    if any(m in tl for m in explicit_medium):
+        return None
+    # 创作类动词
+    creative_verbs = (
+        "制作", "创作", "做个", "做一个", "搞一个", "产出", "构思", "设想",
+        "打造", "塑造", "生成", "帮我做", "帮我生成", "帮我画", "帮我写", "请制作",
+        "请创作", "请生成", "请画", "请写", "来一个", "来一张", "来一幅", "做一张",
+        "做张", "写一首", "写一段", "写个",
+    )
+    if not any(v in t for v in creative_verbs):
+        return None
+    # 模糊：有创作动词但无明确产出形式 → 提供方向选择
+    options = [
+        {"key": "text", "label": "文案", "desc": "文章 / 故事 / 诗歌等文字内容", "medium": "文案"},
+        {"key": "image", "label": "图片", "desc": "插画 / 海报 / 照片等图像", "medium": "图片"},
+        {"key": "video", "label": "视频", "desc": "短片 / 动画等视频", "medium": "视频"},
+        {"key": "other", "label": "其他", "desc": "语音 / 代码 / 其他形式", "medium": "其他"},
+    ]
+    return {"options": options}
+
 # CST timezone (UTC+8) for consistent time display
 CST_TZ = timezone(timedelta(hours=8))
 
@@ -243,6 +287,8 @@ class SendMessageRequest(BaseModel):
     skill_ids: Optional[list] = None
     agent_ids: Optional[list] = None
     knowledge_base_ids: Optional[list] = None
+    mcp_ids: Optional[list] = None
+    attachments: Optional[list] = None
     use_tools: Optional[bool] = False
 
 
@@ -374,7 +420,36 @@ def _resolve_market_agents(db: Session, agent_ids: list, user_id: str) -> list:
     return results
 
 
-def _parse_skill_content(content: str) -> dict:
+def _resolve_market_mcp(db: Session, mcp_ids: list, user_id: str) -> list:
+    """
+    加载用户选择的 MCP 应用配置（MarketApp.type == 'mcp'）。
+    返回: [{id, name, transport, endpoint, description}]
+    """
+    if not mcp_ids:
+        return []
+
+    mcps = db.query(MarketApp).filter(
+        MarketApp.id.in_(mcp_ids),
+        MarketApp.type == "mcp"
+    ).all()
+
+    results = []
+    for m in mcps:
+        config = {}
+        try:
+            if m.skill_content:
+                config = json.loads(m.skill_content)
+        except Exception:
+            config = {}
+        results.append({
+            "id": m.id,
+            "name": m.name,
+            "transport": m.mcp_transport or config.get("transport", "stdio"),
+            "endpoint": m.agent_endpoint or config.get("endpoint", "") or config.get("url", ""),
+            "description": m.description or config.get("description", ""),
+        })
+
+    return results
     """
     解析SKILL.md格式内容, 提取结构化信息。
     SKILL.md格式:
@@ -651,6 +726,7 @@ def _build_system_prompt(
     agents: list,
     kb_context: str,
     user_content: str,
+    mcp: list = None,
 ) -> tuple:
     """
     动态组装system prompt + function definitions。
@@ -736,6 +812,12 @@ def _build_system_prompt(
     if kb_context:
         system_parts.append(f"参考以下知识库内容回答用户问题:\n{kb_context}")
 
+    # MCP 应用注入
+    if mcp:
+        mcp_descs = ", ".join([f"{s['name']}({s.get('transport', 'stdio')})" for s in mcp])
+        system_parts.append(f"已连接的 MCP 应用(外部工具服务): {mcp_descs}")
+        system_parts.append("当用户请求需要调用外部系统/工具（如查询数据库、调用API、操作第三方服务）时，可参考上述 MCP 应用提供的能力；若平台已接入对应 MCP 工具，优先通过工具调用完成。")
+
     system_prompt = "\n".join(system_parts)
     return system_prompt, function_definitions
 
@@ -882,6 +964,8 @@ def _get_conversation_context(db: Session, conv_id: str) -> dict:
             context["agent_ids"] = first_tool_msg.agent_ids
         if first_tool_msg.knowledge_ids:
             context["knowledge_base_ids"] = first_tool_msg.knowledge_ids
+        if getattr(first_tool_msg, "mcp_ids", None):
+            context["mcp_ids"] = first_tool_msg.mcp_ids
 
     return context
 
@@ -1068,33 +1152,88 @@ async def send_message(
     effective_skill_ids = req.skill_ids or []
     effective_agent_ids = req.agent_ids or []
     effective_kb_ids = req.knowledge_base_ids or []
+    effective_mcp_ids = req.mcp_ids or []
     effective_use_tools = req.use_tools or False
 
-    if not effective_skill_ids and not effective_agent_ids and not effective_kb_ids:
+    if not effective_skill_ids and not effective_agent_ids and not effective_kb_ids and not effective_mcp_ids:
         # 从对话上下文继承
         prev_context = _get_conversation_context(db, conv_id)
         if prev_context:
             effective_skill_ids = prev_context.get("skill_ids", [])
             effective_agent_ids = prev_context.get("agent_ids", [])
             effective_kb_ids = prev_context.get("knowledge_base_ids", [])
-            if effective_skill_ids or effective_agent_ids:
+            effective_mcp_ids = prev_context.get("mcp_ids", [])
+            if effective_skill_ids or effective_agent_ids or effective_mcp_ids:
                 effective_use_tools = True
 
-    # 保存用户消息(含skill/agent/kb选择用于上下文保持)
+    # 附件内容拼接到用户消息中(文本类文件附带内容, 供模型参考)
+    attachment_block = ""
+    if req.attachments:
+        parts = []
+        for att in req.attachments:
+            name = att.get("name", "附件") if isinstance(att, dict) else str(att)
+            ftype = att.get("file_type", "") if isinstance(att, dict) else ""
+            content = att.get("content", "") if isinstance(att, dict) else ""
+            if content:
+                parts.append(f"【附件: {name}】\n{content}")
+            else:
+                parts.append(f"【附件(二进制, 名称: {name}, 类型: {ftype})】")
+        if parts:
+            attachment_block = "\n\n" + "\n\n".join(parts)
+
+    # 保存用户消息(含skill/agent/kb/mcp选择用于上下文保持)
     user_msg = Message(
         id=gen_uuid(),
         conversation_id=conv_id,
         role="user",
-        content=req.content,
+        content=req.content + attachment_block,
         token_used=len(req.content) // 2,
         model_id=req.model_id or conv.model_id,
         skill_ids=req.skill_ids if req.skill_ids else None,
         agent_ids=req.agent_ids if req.agent_ids else None,
         knowledge_ids=req.knowledge_base_ids if req.knowledge_base_ids else None,
+        mcp_ids=req.mcp_ids if req.mcp_ids else None,
+        attachments=req.attachments if req.attachments else None,
         created_at=cst_now(),
     )
     db.add(user_msg)
 
+    # ===== 模糊创作意图拦截 =====
+    # 用户使用创作类动词(制作/创作/做/生成…)但未明确产出形式时，先与用户确认方向，
+    # 避免直接调用大模型被内容安全策略拒答(如返回 "high risk")。
+    clarify = detect_ambiguous_creative_intent(req.content)
+    if clarify:
+        clarify_content = "<<CLARIFY>>" + json.dumps(
+            {"prompt": req.content, "options": clarify["options"]}, ensure_ascii=False
+        ) + "<<CLARIFY>>"
+        ai_msg = Message(
+            id=gen_uuid(),
+            conversation_id=conv_id,
+            role="assistant",
+            content=clarify_content,
+            token_used=0,
+            model_id=req.model_id or conv.model_id,
+            created_at=cst_now(),
+        )
+        db.add(ai_msg)
+        db.commit()
+        db.refresh(user_msg)
+        db.refresh(ai_msg)
+        return {
+            "code": 0,
+            "data": {
+                "user_message": {
+                    "id": user_msg.id, "role": user_msg.role, "content": user_msg.content,
+                    "token_used": user_msg.token_used, "created_at": str(user_msg.created_at),
+                },
+                "assistant_message": {
+                    "id": ai_msg.id, "role": ai_msg.role, "content": clarify_content,
+                    "created_at": str(ai_msg.created_at), "clarify": clarify,
+                },
+            },
+            "message": "请选择创作方向",
+        }
+
     # ===== 核心聊天引擎 =====
     user_id = current_user.id
     model_id = req.model_id or conv.model_id
@@ -1140,6 +1279,7 @@ async def send_message(
                     # Stage A+B: 解析Skills和Agents
                     skills = _resolve_market_skills(db, effective_skill_ids, user_id)
                     agents = _resolve_market_agents(db, effective_agent_ids, user_id)
+                    mcps = _resolve_market_mcp(db, effective_mcp_ids, user_id)
 
                     # Stage B.1: Agent可能自带skill_ids和kb_ids
                     agent_extra_skill_ids = []
@@ -1171,6 +1311,7 @@ async def send_message(
                         agents=agents,
                         kb_context=kb_context,
                         user_content=req.content,
+                        mcp=mcps,
                     )
 
                     # 构建消息列表