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)
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@@ -368,6 +368,19 @@ def delete_asset(
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asset.status = "deleted"
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asset.updated_at = datetime.now()
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# 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
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if asset.knowledge_doc_id:
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try:
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kb_doc = db.query(KnowledgeDocument).filter(KnowledgeDocument.id == asset.knowledge_doc_id).first()
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if kb_doc:
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kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_doc.knowledge_base_id).first()
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if kb:
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kb.document_count = max(0, (kb.document_count or 0) - 1)
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kb.updated_at = datetime.now()
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db.delete(kb_doc)
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except Exception:
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pass
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LogService.log_operation(
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user_id=current_user.id, username=current_user.username, action="delete",
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target="asset", target_id=asset.id,
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@@ -4094,7 +4094,28 @@ def _resolve_active_mcp_servers(db: Session, mcp_ids: list) -> list:
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def _discover_installed_skills(db: Session, user_id: str, limit: int = 15) -> list:
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def _list_enabled_mcp_servers(db: Session) -> list:
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"""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"。"""
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servers = db.query(MCPServer).filter(MCPServer.is_enabled == True).all()
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out = []
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for s in servers:
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try:
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headers = json.loads(s.headers) if isinstance(s.headers, str) else (s.headers or {})
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except Exception:
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headers = {}
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out.append({
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"id": s.id,
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"name": s.name,
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"description": s.description or "",
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"transport": s.transport_type or "streamable_http",
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"endpoint": s.url or "",
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"url": s.url or "",
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"headers": headers,
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})
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return out
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def _discover_installed_skills(db: Session, user_id: str, limit: int = 300) -> list:
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@@ -5311,6 +5332,7 @@ def _build_system_prompt(
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mcp_functions: list = None,
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auto_discovered: bool = False,
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all_mcp: list = None,
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) -> tuple:
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@@ -5745,6 +5767,15 @@ def _build_system_prompt(
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# 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）
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if all_mcp:
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_mcp_inventory = "\n".join([f"- {m.get('name','')}: {m.get('description','') or '（未填写描述）'}" for m in all_mcp])
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system_parts.append(
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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"
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"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，"
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"不要编造或遗漏，也不要把未列出的服务说成已接入。"
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)
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# 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）
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@@ -7074,6 +7105,7 @@ def get_messages(
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"used_skills": msg.used_skills or [],
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"used_mcp": msg.used_mcp or [],
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@@ -8090,6 +8122,7 @@ async def send_message(
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mcp_functions=mcp_function_definitions,
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auto_discovered=used_auto_skills,
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all_mcp=_list_enabled_mcp_servers(db),
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)
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@@ -8103,6 +8136,10 @@ async def send_message(
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# 实际调用追踪（Issue：反馈真实使用的技能/MCP，而非注入的全部）
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called_skill_names = []
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called_mcp_names = []
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messages = [{"role": "system", "content": system_prompt}]
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@@ -8433,6 +8470,8 @@ async def send_message(
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)
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called_mcp_names.append(_srv.get("name", ""))
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else:
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tool_result = _execute_skill_tool(
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@@ -8441,6 +8480,12 @@ async def send_message(
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)
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_sk = next((s for s in skills if s.get("func_name") == func_name), None)
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if _sk:
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called_skill_names.append(_sk.get("name", ""))
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messages.append({
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@@ -8671,7 +8716,7 @@ async def send_message(
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# 更新used_skills
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# 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
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@@ -8679,11 +8724,83 @@ async def send_message(
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if skills:
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used_mcp = []
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used_skills = [s["name"] for s in skills]
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if effective_skill_ids:
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# 用户显式选择了技能 -> 反馈配对的技能名
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try:
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_sel = _resolve_market_skills(db, effective_skill_ids, user_id)
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used_skills = [s["name"] for s in _sel]
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except Exception:
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used_skills = []
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elif called_skill_names:
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# 未显式选技能 -> 反馈 AI 本轮实际调用的技能（可能为空）
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used_skills = list(dict.fromkeys(called_skill_names))
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if effective_mcp_ids:
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# 用户显式选择了 MCP -> 反馈配对的 MCP 名
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try:
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_sel_mcp = _resolve_active_mcp_servers(db, effective_mcp_ids)
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used_mcp = [m["name"] for m in _sel_mcp]
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except Exception:
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used_mcp = []
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elif called_mcp_names:
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used_mcp = list(dict.fromkeys(called_mcp_names))
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@@ -8691,6 +8808,10 @@ async def send_message(
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ai_msg.used_mcp = used_mcp
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@@ -8980,6 +9101,7 @@ async def send_message(
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"used_skills": used_skills,
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"used_mcp": used_mcp,
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@@ -188,7 +188,7 @@ def _qihua_reply(text: str, user_id: str = None, platform: str = "im") -> str:
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try:
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from app.api.im_llm import _call_real_llm
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db = SessionLocal()
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return _call_real_llm(db, None, prompt, platform)
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return _call_real_llm(db, None, prompt, platform, user_id=user_id)
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except Exception as e:
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return f"（AI 回复生成失败：{e}）"
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finally:
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@@ -1149,15 +1149,21 @@ def _split_content_into_chunks(content: str, chunk_size: int = 500) -> list:
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def _sync_asset_for_knowledge_doc(db: Session, user_id: str, doc_id: str, filename: str, file_type: str, file_size: int):
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"""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）"""
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try:
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# 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
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_ft_label = {
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"pdf": "pdf", "docx": "word", "doc": "word", "xlsx": "excel", "xls": "excel",
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"pptx": "ppt", "ppt": "ppt", "csv": "txt", "md": "md", "txt": "txt",
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}.get((file_type or "").lower(), "other")
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asset = Asset(
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id=gen_uuid(),
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user_id=user_id,
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name=filename,
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category="doc",
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category="file",
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description=f"知识库文档 (ID: {doc_id})",
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file_type=file_type,
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file_type=_ft_label,
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size=file_size,
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status="active",
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knowledge_doc_id=doc_id,
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created_at=datetime.now(),
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updated_at=datetime.now(),
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)
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@@ -1191,6 +1197,15 @@ def delete_document(
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pass # total_size removed
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kb.updated_at = datetime.now()
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# 同步删除知识库文档对应的资产记录（双向同步）
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try:
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linked_asset = db.query(Asset).filter(Asset.knowledge_doc_id == doc_id).first()
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if linked_asset:
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linked_asset.status = "deleted"
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linked_asset.updated_at = datetime.now()
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except Exception as _e:
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logger.warning(f"同步删除关联资产失败: {_e}")
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db.delete(doc)
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db.commit()
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@@ -106,6 +106,7 @@ class Message(Base):
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mcp_ids = Column(JSON, default=None, comment="该轮对话选择的MCP应用ID列表")
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attachments = Column(JSON, default=None, comment="该轮对话附带的文件附件列表")
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used_skills = Column(JSON, default=None, comment="该轮对话调用的进化技能名列表")
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used_mcp = Column(JSON, default=None, comment="该轮对话调用的MCP应用名列表")
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created_at = Column(DateTime, default=datetime.now)
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@@ -747,6 +748,7 @@ class Asset(Base):
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content = Column(Text, default=None)
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novel_meta = Column(JSON, default=None)
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status = Column(String(10), nullable=False, default="active") # active / deleted
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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）")
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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@@ -54,6 +54,7 @@ function mapFileTypeToLabel(ft: string): string {
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const mapping: Record<string, string> = {
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pdf: 'pdf', docx: 'word', doc: 'word', xlsx: 'excel', xls: 'excel',
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pptx: 'ppt', ppt: 'ppt', csv: 'txt', md: 'md', txt: 'txt',
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word: 'word', excel: 'excel',
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};
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return mapping[ft] || 'other';
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}
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@@ -399,7 +400,7 @@ const AssetsPage: React.FC = () => {
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const VALID_CATEGORIES = ['file', 'novel', 'image', 'video', 'audio', 'custom', 'other'];
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const normalizeAsset = (item: any): Asset => {
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const rawCat = item.category;
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const ft = item.fileType || item.file_type || 'other';
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const ft = mapFileTypeToLabel(item.fileType || item.file_type || 'other');
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let category = rawCat;
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if (!VALID_CATEGORIES.includes(category)) {
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category = (ft && ft !== 'other') ? 'file' : 'other';
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@@ -764,6 +764,7 @@ const ChatPage: React.FC = () => {
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tokenUsed: updated.tokenUsed ?? updated.token_used ?? m.tokenUsed,
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createdAt: updated.createdAt || updated.created_at || m.createdAt,
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used_skills: updated.usedSkills ?? updated.used_skills ?? m.used_skills,
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used_mcp: updated.usedMcp ?? updated.used_mcp ?? m.used_mcp,
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} : m));
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stopMediaPolling(msgId);
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} catch (e) {
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@@ -1039,6 +1040,7 @@ const ChatPage: React.FC = () => {
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tokenUsed: data.assistant_message.token_used || 0,
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createdAt: data.assistant_message.created_at || new Date().toISOString(),
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used_skills: data.assistant_message.used_skills || [],
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used_mcp: data.assistant_message.used_mcp || [],
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});
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}
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return [...filtered, ...newMsgs];
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@@ -1311,6 +1313,7 @@ const ChatPage: React.FC = () => {
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messages.map(msg => {
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// Real used_skills from API response
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const usedSkills: string[] = (msg.role === 'assistant' && msg.used_skills) ? msg.used_skills : [];
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const usedMcp: string[] = (msg.role === 'assistant' && msg.used_mcp) ? msg.used_mcp : [];
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// 模糊创作意图确认卡片
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const clarify = msg.role === 'assistant' ? parseClarify(msg.content) : null;
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return (
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@@ -1357,6 +1360,27 @@ const ChatPage: React.FC = () => {
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)}
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</div>
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)}
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{msg.role === 'assistant' && usedMcp.length > 0 && (
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<div className="evo-skill-tags-row">
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{usedMcp.slice(0, 3).map(mcpName => (
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<span
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key={mcpName}
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className="evo-skill-tag evo-mcp-tag"
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title={`使用了 MCP 应用：${mcpName}`}
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>
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<svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round">
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<rect x="3" y="3" width="18" height="18" rx="2" ry="2"/>
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<line x1="9" y1="9" x2="15" y2="15"/>
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<line x1="15" y1="9" x2="9" y2="15"/>
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</svg>
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{mcpName}
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</span>
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))}
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{usedMcp.length > 3 && (
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<span className="evo-skill-tag evo-skill-tag-more">+{usedMcp.length - 3}</span>
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)}
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</div>
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)}
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<div className={`message-meta ${msg.role}`}>
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<span className="message-time">{formatTime(msg.createdAt)}</span>
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<span className="message-tokens">{msg.tokenUsed} tokens</span>
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@@ -160,6 +160,37 @@ function getMediaStats(logs: TokenUsageLog[]) {
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return { imageCount, videoCount, imageTokens, videoTokens, models };
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}
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// 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）。
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// 通过 page_size=100 循环拉取，直到不足一页为止。
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async function fetchAllTokenLogs(): Promise<TokenUsageLog[]> {
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const all: any[] = [];
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let page = 1;
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let hasMore = true;
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while (hasMore) {
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const res = await tokenAPI.getLogs({ page_size: 100, page });
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const rawLogs = (res.data as any)?.items || res.data || [];
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if (!Array.isArray(rawLogs) || rawLogs.length === 0) {
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hasMore = false;
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break;
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}
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all.push(
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...rawLogs.map((item: any) => ({
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...item,
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promptTokens: item.promptTokens ?? item.prompt_tokens ?? 0,
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completionTokens: item.completionTokens ?? item.completion_tokens ?? 0,
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totalTokens: item.totalTokens ?? item.total_tokens ?? 0,
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modelName: item.modelName ?? item.model_name ?? '',
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cost: item.cost ?? 0,
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createdAt: item.createdAt ?? item.created_at ?? item.createdAt,
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appName: item.appName ?? item.app_name ?? '',
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}))
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);
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if (rawLogs.length < 100) hasMore = false;
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else page += 1;
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}
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return all as TokenUsageLog[];
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}
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const TokenDashboardPage: React.FC = () => {
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const navigate = useNavigate();
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const [totalConsumed, setTotalConsumed] = useState(0);
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@@ -178,28 +209,18 @@ const TokenDashboardPage: React.FC = () => {
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setLoading(true);
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setError(null);
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try {
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const [overviewRes, balanceRes, logsRes] = await Promise.all([
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const [overviewRes, balanceRes, allLogs] = await Promise.all([
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tokenAPI.getOverview(),
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tokenAPI.getBalance(),
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tokenAPI.getLogs(),
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fetchAllTokenLogs(),
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]);
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setTotalConsumed(overviewRes.data?.total_used || overviewRes.data?.total_consumed || overviewRes.data?.totalConsumed || 0);
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setBalance(balanceRes.data?.balance || 0);
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||||
// 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
|
||||
const rawLogs = (logsRes.data as any)?.items || logsRes.data || [];
|
||||
// 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,
|
||||
})));
|
||||
// 已拉取全部日志（fetchAllTokenLogs 内部循环分页），客户端可自由上下翻页
|
||||
setLogs(allLogs);
|
||||
} catch (err: any) {
|
||||
setError(err.message || '数据加载失败');
|
||||
} finally {
|
||||
|
||||
@@ -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' }}>
|
||||
|
||||
@@ -61,6 +61,7 @@ export interface Message {
|
||||
tokenUsed: number;
|
||||
createdAt: string;
|
||||
used_skills?: string[];
|
||||
used_mcp?: string[];
|
||||
}
|
||||
|
||||
|
||||
|
||||
Reference in new issue
Block a user